Comparative performance of intensive care mortality prediction models based on manually curated versus automatically extracted electronic health record data

INTRODUCTION

Benchmarking intensive care units for audit and feedback is frequently based on comparing actual mortality versus predicted mortality. Traditionally, mortality prediction models rely on a limited number of input variables and significant manual data entry and curation. Using automatically extracted electronic health record data may be a promising alternative. However, adequate data on comparative performance between these approaches is currently lacking.

METHODS

The AmsterdamUMCdb intensive care database was used to construct a baseline APACHE IV in-hospital mortality model based on data typically available through manual data curation. Subsequently, new in-hospital mortality models were systematically developed and evaluated. New models differed with respect to the extent of automatic variable extraction, classification method, recalibration usage and the size of collection window.

RESULTS

A total of 13 models were developed based on data from 5,077 admissions divided into a train (80%) and test (20%) cohort. Adding variables or extending collection windows only marginally improved discrimination and calibration. An XGBoost model using only automatically extracted variables, and therefore no acute or chronic diagnoses, was the best performing automated model with an AUC of 0.89 and a Brier score of 0.10.

DISCUSSION

Performance of intensive care mortality prediction models based on manually curated versus automatically extracted electronic health record data is similar. Importantly, our results suggest that variables typically requiring manual curation, such as diagnosis at admission and comorbidities, may not be necessary for accurate mortality prediction. These proof-of-concept results require replication using multi-centre data.

Strain on Scarce Intensive Care Beds Drives Reduced Patient Volumes, Patient Selection, and Worse Outcome: A National Cohort Study

OBJECTIVES

Strain on ICUs during the COVID-19 pandemic required stringent triage at the ICU to distribute resources appropriately. This could have resulted in reduced patient volumes, patient selection, and worse outcome of non-COVID-19 patients, especially during the pandemic peaks when the strain on ICUs was extreme. We analyzed this potential impact on the non-COVID-19 patients.

DESIGN

A national cohort study.

SETTING

Data of 71 Dutch ICUs.

PARTICIPANTS

A total of 120,393 patients in the pandemic non-COVID-19 cohort (from March 1, 2020 to February 28, 2022) and 164,737 patients in the prepandemic cohort (from January 1, 2018 to December 31, 2019).

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Volume, patient characteristics, and mortality were compared between the pandemic non-COVID-19 cohort and the prepandemic cohort, focusing on the pandemic period and its peaks, with attention to strata of specific admission types, diagnoses, and severity. The number of admitted non-COVID-19 patients during the pandemic period and its peaks were, respectively, 26.9% and 34.2% lower compared with the prepandemic cohort. The pandemic non-COVID-19 cohort consisted of fewer medical patients (48.1% vs. 50.7%), fewer patients with comorbidities (36.5% vs. 40.6%), and more patients on mechanical ventilation (45.3% vs. 42.4%) and vasoactive medication (44.7% vs. 38.4%) compared with the prepandemic cohort. Case-mix adjusted mortality during the pandemic period and its peaks was higher compared with the prepandemic period, odds ratios were, respectively, 1.08 (95% CI, 1.05-1.11) and 1.10 (95% CI, 1.07-1.13).

CONCLUSIONS

In non-COVID-19 patients the strain on healthcare has driven lower patient volume, selection of fewer comorbid patients who required more intensive support, and a modest increase in the case-mix adjusted mortality.

Predicting 30-day mortality in intensive care unit patients with ischaemic stroke or intracerebral haemorrhage

BACKGROUND

Stroke patients admitted to an intensive care unit (ICU) follow a particular survival pattern with a high short-term mortality, but if they survive the first 30 days, a relatively favourable subsequent survival is observed.

OBJECTIVES

The development and validation of two prognostic models predicting 30-day mortality for ICU patients with ischaemic stroke and for ICU patients with intracerebral haemorrhage (ICH), analysed separately, based on parameters readily available within 24 h after ICU admission, and with comparison with the existing Acute Physiology and Chronic Health Evaluation IV (APACHE-IV) model.

DESIGN

Observational cohort study.

SETTING

All 85 ICUs participating in the Dutch National Intensive Care Evaluation database.

PATIENTS

All adult patients with ischaemic stroke or ICH admitted to these ICUs between 2010 and 2019.

MAIN OUTCOME MEASURES

Models were developed using logistic regressions and compared with the existing APACHE-IV model. Predictive performance was assessed using ROC curves, calibration plots and Brier scores.

RESULTS

We enrolled 14 303 patients with stroke admitted to ICU: 8422 with ischaemic stroke and 5881 with ICH. Thirty-day mortality was 27% in patients with ischaemic stroke and 41% in patients with ICH. Important factors predicting 30-day mortality in both ischaemic stroke and ICH were age, lowest Glasgow Coma Scale (GCS) score in the first 24 h, acute physiological disturbance (measured using the Acute Physiology Score) and the application of mechanical ventilation. Both prognostic models showed high discrimination with an AUC 0.85 [95% confidence interval (CI), 0.84 to 0.87] for patients with ischaemic stroke and 0.85 (0.83 to 0.86) in ICH. Calibration plots and Brier scores indicated an overall good fit and good predictive performance. The APACHE-IV model predicting 30-day mortality showed similar performance with an AUC of 0.86 (95% CI, 0.85 to 0.87) in ischaemic stroke and 0.87 (0.86 to 0.89) in ICH.

CONCLUSION

We developed and validated two prognostic models for patients with ischaemic stroke and ICH separately with a high discrimination and good calibration to predict 30-day mortality within 24 h after ICU admission.

TRIAL REGISTRATION

Trial registration: Dutch Trial Registry ( https://www.trialregister.nl/ ); identifier: NTR7438.

Quality improvement of Dutch ICUs from 2009 to 2021: A registry based observational study

PURPOSE

To investigate the development in quality of ICU care over time using the Dutch National Intensive Care Evaluation (NICE) registry.

MATERIALS AND METHODS

We included data from all ICU admissions in the Netherlands from those ICUs that submitted complete data between 2009 and 2021 to the NICE registry. We determined median and interquartile range for eight quality indicators. To evaluate changes over time on the indicators, we performed multilevel regression analyses, once without and once with the COVID-19 years 2020 and 2021 included. Additionally we explored between-ICU heterogeneity by calculating intraclass correlation coefficients (ICC).

RESULTS

705,822 ICU admissions from 55 (65%) ICUs were included in the analyses. ICU length of stay (LOS), duration of mechanical ventilation (MV), readmissions, in-hospital mortality, hypoglycemia, and pressure ulcers decreased significantly between 2009 and 2019 (OR <1). After including the COVID-19 pandemic years, the significant change in MV duration, ICU LOS, and pressure ulcers disappeared. We found an ICC ≤0.07 on the quality indicators for all years, except for pressure ulcers with an ICC of 0.27 for 2009 to 2021.

CONCLUSIONS

Quality of Dutch ICU care based on seven indicators significantly improved from 2009 to 2019 and between-ICU heterogeneity is medium to small, except for pressure ulcers. The COVID-19 pandemic disturbed the trend in quality improvement, but unaltered the between-ICU heterogeneity.

Characteristics and outcome of COVID-19 patients admitted to the ICU: a nationwide cohort study on the comparison between the consecutive stages of the COVID-19 pandemic in the Netherlands, an update

BACKGROUND

Previously, we reported a decreased mortality rate among patients with COVID-19 who were admitted at the ICU during the final upsurge of the second wave (February-June 2021) in the Netherlands. We examined whether this decrease persisted during the third wave and the phases with decreasing incidence of COVID-19 thereafter and brought up to date the information on patient characteristics.

METHODS

Data from the National Intensive Care Evaluation (NICE)-registry of all COVID-19 patients admitted to an ICU in the Netherlands were used. Patient characteristics and rates of in-hospital mortality (the primary outcome) during the consecutive periods after the first wave (periods 2-9, May 25, 2020-January 31, 2023) were compared with those during the first wave (period 1, February-May 24, 2020).

RESULTS

After adjustment for patient characteristics and ICU occupancy rate, the mortality risk during the initial upsurge of the third wave (period 6, October 5, 2021-January, 31, 2022) was similar to that of the first wave (ORadj = 1.01, 95%-CI [0.88-1.16]). The mortality rates thereafter decreased again (e.g., period 9, October 5, 2022-January, 31, 2023: ORadj = 0.52, 95%-CI [0.41-0.66]). Among the SARS-CoV-2 positive patients, there was a huge drop in the proportion of patients with COVID-19 as main reason for ICU admission: from 88.2% during the initial upsurge of the third wave to 51.7%, 37.3%, and 41.9% for the periods thereafter. Restricting the analysis to these patients did not modify the results on mortality.

CONCLUSIONS

The results show variation in mortality rates among critically ill COVID-19 patients across the calendar time periods that is not explained by differences in case-mix and ICU occupancy rates or by varying proportions of patients with COVID-19 as main reason for ICU admission. The consistent increase in mortality during the initial, rising phase of each separate wave might be caused by the increased virulence of the contemporary virus strain and lacking immunity to the new strain, besides unmeasured patient-, treatment- and healthcare system characteristics.

Development and evaluation of regression tree models for predicting in-hospital mortality of a national registry of COVID-19 patients over six pandemic surges

BACKGROUND

Objective prognostic information is essential for good clinical decision making. In case of unknown diseases, scarcity of evidence and limited tacit knowledge prevent obtaining this information. Prediction models can be useful, but need to be not only evaluated on how well they predict, but also how stable these models are under fast changing circumstances with respect to development of the disease and the corresponding clinical response. This study aims to provide interpretable and actionable insights, particularly for clinicians. We developed and evaluated two regression tree predictive models for in-hospital mortality of COVID-19 patient at admission and 24 hours (24 h) after admission, using a national registry. We performed a retrospective analysis of observational routinely collected data.

METHODS

Two regression tree models were developed for admission and 24 h after admission. The complexity of the trees was managed via cross validation to prevent overfitting. The predictive ability of the model was assessed via bootstrapping using the Area under the Receiver-Operating-Characteristic curve, Brier score and calibration curves. The tree models were assessed on the stability of their probabilities and predictive ability, on the selected variables, and compared to a full-fledged logistic regression model that uses variable selection and variable transformations using splines. Participants included COVID-19 patients from all ICUs participating in the Dutch National Intensive Care Evaluation (NICE) registry, who were admitted at the ICU between February 27, 2020, and November 23, 2021. From the NICE registry, we included concerned demographic data, minimum and maximum values of physiological data in the first 24 h of ICU admission and diagnoses (reason for admission as well as comorbidities) for model development. The main outcome measure was in-hospital mortality. We additionally analysed the Length-of-Stay (LoS) per patient subgroup per survival status.

RESULTS

A total of 13,369 confirmed COVID-19 patients from 70 ICUs were included (with mortality rate of 28%). The optimism-corrected AUROC of the admission tree (with seven paths) was 0.72 (95% CI: 0.71-0.74) and of the 24 h tree (with 11 paths) was 0.74 (0.74-0.77). Both regression trees yielded good calibration and variable selection for both trees was stable. Patient subgroups comprising the tree paths had comparable survival probabilities as the full-fledged logistic regression model, survival probabilities were stable over six COVID-19 surges, and subgroups were shown to have added predictive value over the individual patient variables.

CONCLUSIONS

We developed and evaluated regression trees, which operate at par with a carefully crafted logistic regression model. The trees consist of homogenous subgroups of patients that are described by simple interpretable constraints on patient characteristics thereby facilitating shared decision-making.

Can we reliably automate clinical prognostic modelling? A retrospective cohort study for ICU triage prediction of in-hospital mortality of COVID-19 patients in the Netherlands

BACKGROUND

Building Machine Learning (ML) models in healthcare may suffer from time-consuming and potentially biased pre-selection of predictors by hand that can result in limited or trivial selection of suitable models. We aimed to assess the predictive performance of automating the process of building ML models (AutoML) in-hospital mortality prediction modelling of triage COVID-19 patients at ICU admission versus expert-based predictor pre-selection followed by logistic regression.

METHODS

We conducted an observational study of all COVID-19 patients admitted to Dutch ICUs between February and July 2020. We included 2,690 COVID-19 patients from 70 ICUs participating in the Dutch National Intensive Care Evaluation (NICE) registry. The main outcome measure was in-hospital mortality. We asessed model performance (at admission and after 24h, respectively) of AutoML compared to the more traditional approach of predictor pre-selection and logistic regression.

FINDINGS

Predictive performance of the autoML models with variables available at admission shows fair discrimination (average AUROC = 0·75-0·76 (sdev = 0·03), PPV = 0·70-0·76 (sdev = 0·1) at cut-off = 0·3 (the observed mortality rate), and good calibration. This performance is on par with a logistic regression model with selection of patient variables by three experts (average AUROC = 0·78 (sdev = 0·03) and PPV = 0·79 (sdev = 0·2)). Extending the models with variables that are available at 24h after admission resulted in models with higher predictive performance (average AUROC = 0·77-0·79 (sdev = 0·03) and PPV = 0·79-0·80 (sdev = 0·10-0·17)).

CONCLUSIONS

AutoML delivers prediction models with fair discriminatory performance, and good calibration and accuracy, which is as good as regression models with expert-based predictor pre-selection. In the context of the restricted availability of data in an ICU quality registry, extending the models with variables that are available at 24h after admission showed small (but significantly) performance increase.

Comparison of outcome and characteristics between 6343 COVID-19 patients and 2256 other community-acquired viral pneumonia patients admitted to Dutch ICUs

Purpose

Describe the differences in characteristics and outcomes between COVID-19 and other viral pneumonia patients admitted to Dutch ICUs.

Materials and methods

Data from the National-Intensive-Care-Evaluation-registry of COVID-19 patients admitted between February 15th and January 1th 2021 and other viral pneumonia patients admitted between January 1st 2017 and January 1st 2020 were used. Patients' characteristics, the unadjusted, and adjusted in-hospital mortality were compared.

Results

6343 COVID-19 and 2256 other viral pneumonia patients from 79 ICUs were included. The COVID-19 patients included more male (71.3 vs 49.8%), had a higher Body-Mass-Index (28.1 vs 25.5), less comorbidities (42.2 vs 72.7%), and a prolonged hospital length of stay (19 vs 9 days). The COVID-19 patients had a significantly higher crude in-hospital mortality rate (Odds ratio (OR) = 1.80), after adjustment for patient characteristics and ICU occupancy rate the OR was respectively 3.62 and 3.58.

Conclusion

Higher mortality among COVID-19 patients could not be explained by patient characteristics and higher ICU occupancy rates, indicating that COVID-19 is more severe compared to other viral pneumonia. Our findings confirm earlier warnings of a high need of ICU capacity and high mortality rates among relatively healthy COVID-19 patients as this may lead to a higher mental workload for the staff.

The impact of COVID-19 on nursing workload and planning of nursing staff on the Intensive Care: A prospective descriptive multicenter study

INTRODUCTION

The impact of the care for COVID-19 patients on nursing workload and planning nursing staff on the Intensive Care Unit has been huge. Nurses were confronted with a high workload and an increase in the number of patients per nurse they had to take care of.

OBJECTIVE

The primary aim of this study is to describe differences in the planning of nursing staff on the Intensive Care in the COVID period versus a recent non-COVID period. The secondary aim was to describe differences in nursing workload in COVID-19 patients, pneumonia patients and other patients on the Intensive Care. We finally wanted to assess the cause of possible differences in Nursing Activities Scores between the different groups.

METHODS

We analyzed data on nursing staff and nursing workload as measured by the Nursing Activities Score of 3,994 patients and 36,827 different shifts in 6 different hospitals in the Netherlands. We compared data from the COVID-19 period, March 1st 2020 till July 1st 2020, with data in a non-COVID period, March 1st 2019 till July 1st 2019. We analyzed the Nursing Activities Score per patient, the number of patients per nurse and the Nursing Activities Score per nurse in the different cohorts and time periods. Differences were tested by a Chi-square, non-parametric Wilcoxon or Student's t-test dependent on the distribution of the data.

RESULTS

Our results showed both a significant higher number of patients per nurse (1.1 versus 1.0, p<0.001) and a significant higher Nursing Activities Score per Intensive Care nurse (76.5 versus 50.0, p<0.001) in the COVID-19 period compared to the non-COVID period. The Nursing Activities Score was significantly higher in COVID-19 patients compared to both the pneumonia patients (55.2 versus 50.0, p<0.001) and the non-COVID patients (55.2 versus 42.6, p<0.001), mainly due to more intense hygienic procedures, mobilization and positioning, support and care for relatives and respiratory care.

CONCLUSION

With this study we showed the impact of COVID-19 patients on the planning of nursing care on the Intensive Care. The COVID-19 patients caused a high nursing workload, both in number of patients per nurse and in Nursing Activities Score per nurse.

The objective nursing workload and perceived nursing workload in Intensive Care Units: Analysis of association

BACKGROUND

A range of classification systems are in use for the measurement of nursing workload in Intensive Care Units. However, it is unknown to what extent the measured (objective) nursing workload, usually in terms of the amount of nursing activities, is related to the workload actually experienced (perceived) by nurses.

OBJECTIVES

The aim of this study was to assess the association between the objective nursing workload and the perceived nursing workload and to identify other factors associated with the perceived nursing workload.

METHODS

We measured the objective nursing workload with the Nursing Activities Score and the perceived nursing workload with the NASA-Task Load Index during 228 shifts in eight different Intensive Care Units. We used linear mixed-effect regression models to analyze the association between the objective and perceived nursing workload. Furthermore, we investigated the association of patient characteristics (severity of illness, comorbidities, age, body mass index, and planned or unplanned admission), education level of the nurse, and contextual factors (numbers of patients per nurse, the type of shift (day, evening, night) and day of admission or discharge) with perceived nursing workload. We adjusted for confounders.

RESULTS

We did not find a significant association between the observed workload per nurse and perceived nursing workload (p=0.06). The APACHE-IV Acute Physiology Score of a patient was significantly associated with the perceived nursing workload, also after adjustment for confounders (p=0.02). None of the other patient characteristics was significantly associated with perceived nursing workload. Being a certified nurse or a student nurse was the only nursing or contextual factor significantly associated with the perceived nursing workload, also after adjustment for confounders (p=0.03).

CONCLUSION

Workload is perceived differently by nurses compared to the objectively measured workload by the Nursing Activities Score. Both the severity of illness of the patient and being a student nurse are factors that increase the perceived nursing workload. To keep the workload of nurses in balance, planning nursing capacity should be based on the Nursing Activities Score, on the severity of patient illness and the graduation level of the nurse.

Nurse Operation Workload (NOW), a new nursing workload model for intensive care units based on time measurements: An observational study

BACKGROUND

Several instruments have been developed to measure nursing workload. The commonly used Nursing Activities Score (NAS) and Therapeutic Intervention Scoring System (TISS) are applied to all types of ICU patients. Former research showed that NAS explained 59 to 81% of actual nursing time, whereas the Therapeutic Intervention Scoring System (TISS) described only 43% of the actual nursing time. In both models the development was not based on time measurements.

OBJECTIVES

The aim of this study was to develop a time-based model which can assess patient related nursing workload more accurately and to evaluate whether patient characteristics influence nursing time and therefore should be included in the model.

DESIGN

Observational study design.

SETTING

All 82 Dutch ICUs participate in the National Intensive Care Evaluation (NICE) quality registry. Fifteen of these ICUs are participating in the newly implemented voluntary nursing capacity module. Seven of these ICUs voluntarily participated in this study.

PARTICIPANTS

The patient(s) that were under the responsibility of a chosen nurse were followed by the observer during the entire shift.

METHODS

Time spent per nursing activity per patient was measured in different shifts in seven Dutch ICUs. Nursing activities were measured using an in-house developed web application. Three different models of varying complexity (1. nursing activities only; 2. nursing activities and case-mix correction; 3. complex model with case-mix correction per nursing activity) were developed to explain the total amount of nursing time per patient. The performance of the three models was assessed in 1000 bootstrap samples using the squared Pearson correlation coefficient (R²), Root Mean Squared Prediction Error (RMSPE), Mean Absolute Prediction Error (MAPE), and prediction bias.

RESULTS

In total 287 unique patients have been observed in 371 shifts. Model one's Pearson's R was 0.89 (95%CI 0.86-0.92), model two with case-mix correction 0.90 (95%CI 0.88-0.93), and the third complex model 0.64 (95%CI 0.56-0.72) compared with the actual patient related nursing workload.

CONCLUSION

The newly developed Nurse Operation Workload (NOW) model outperforms existing models in measuring nursing workload, while it includes a lower number of activities and therewith lowers the registration burden. Case-mix correction does not further improve the performance of this model. The patient related nursing workload measured by the NOW gives insight in the actual nursing time needed by patients and can therefore be used to evaluate the average workload per patient per nurse.

The performance of acute versus antecedent patient characteristics for 1-year mortality prediction during intensive care unit admission: a national cohort study

Background

Multiple factors contribute to mortality after ICU, but it is unclear how the predictive value of these factors changes during ICU admission. We aimed to compare the changing performance over time of the acute illness component, antecedent patient characteristics, and ICU length of stay (LOS) in predicting 1-year mortality.

Methods

In this retrospective observational cohort study, the discriminative value of four generalized mixed-effects models was compared for 1-year and hospital mortality. Among patients with increasing ICU LOS, the models included (a) acute illness factors and antecedent patient characteristics combined, (b) acute component only, (c) antecedent patient characteristics only, and (d) ICU LOS. For each analysis, discrimination was measured by area under the receiver operating characteristics curve (AUC), calculated using the bootstrap method. Statistical significance between the models was assessed using the DeLong method (p value < 0.05).

Results

In 400,248 ICU patients observed, hospital mortality was 11.8% and 1-year mortality 21.8%. At ICU admission, the combined model predicted 1-year mortality with an AUC of 0.84 (95% CI 0.84–0.84). When analyzed separately, the acute component progressively lost predictive power. From an ICU admission of at least 3 days, antecedent characteristics significantly exceeded the predictive value of the acute component for 1-year mortality, AUC 0.68 (95% CI 0.68–0.69) versus 0.67 (95% CI 0.67–0.68) (p value < 0.001). For hospital mortality, antecedent characteristics outperformed the acute component from a LOS of at least 7 days, comprising 7.8% of patients and accounting for 52.4% of all bed days. ICU LOS predicted 1-year mortality with an AUC of 0.52 (95% CI 0.51–0.53) and hospital mortality with an AUC of 0.54 (95% CI 0.53–0.55) for patients with a LOS of at least 7 days.

Conclusions

Comparing the predictive value of factors influencing 1-year mortality for patients with increasing ICU LOS, antecedent patient characteristics are more predictive than the acute component for patients with an ICU LOS of at least 3 days. For hospital mortality, antecedent patient characteristics outperform the acute component for patients with an ICU LOS of at least 7 days. After the first week of ICU admission, LOS itself is not predictive of hospital nor 1-year mortality.

Cumulative Prognostic Score Predicting Mortality in Patients Older Than 80 Years Admitted to the ICU

OBJECTIVES

To develop a scoring system model that predicts mortality within 30 days of admission of patients older than 80 years admitted to intensive care units (ICUs).

DESIGN

Prospective cohort study.

SETTING

A total of 306 ICUs from 24 European countries.

PARTICIPANTS

Older adults admitted to European ICUs (N = 3730; median age = 84 years [interquartile range = 81‐87 y]; 51.8% male).

MEASUREMENTS

Overall, 24 variables available during ICU admission were included as potential predictive variables. Multivariable logistic regression was used to identify independent predictors of 30‐day mortality. Model sensitivity, specificity, and accuracy were evaluated with receiver operating characteristic curves.

RESULTS

The 30‐day‐mortality was 1562 (41.9%). In multivariable analysis, these variables were selected as independent predictors of mortality: age, sex, ICU admission diagnosis, Clinical Frailty Scale, Sequential Organ Failure Score, invasive mechanical ventilation, and renal replacement therapy. The discrimination, accuracy, and calibration of the model were good: the area under the curve for a score of 10 or higher was .80, and the Brier score was .18. At a cut point of 10 or higher (75% of all patients), the model predicts 30‐day mortality in 91.1% of all patients who die.

CONCLUSION

A predictive model of cumulative events predicts 30‐day mortality in patients older than 80 years admitted to ICUs. Future studies should include other potential predictor variables including functional status, presence of advance care plans, and assessment of each patient's decision‐making capacity.

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.

[Potential lowering of sepsis-related mortality via screening and implementation of guidelines]

The incidence of sepsis continues to increase. However, over the past decade marked reductions in sepsis-related in-hospital mortality have been reported. Large variations in the presentation and severity of illness may be encountered in ICU patients with severe sepsis, which might preclude the success of screening and guideline programmes. However, the authors of this article were able to prove that a national programme involving screening and a package of interventions did lower relative in-hospital mortality by 16.7% over 3.5 years in 52 participating hospitals in the Netherlands. In-hospital mortality did not change in 30 non-participating hospitals. Therefore, the authors recommend implementing updated guidelines, sepsis quality indicators and programmes with a package of interventions to further reduce sepsis mortality. Furthermore, additional research on long term consequences in sepsis survivors is warranted.

The use of linked registries to assess long-term mortality of ICU patients

Clinical registries are frequently used to monitor and analyze the quality of health care by assessing the in-hospital mortality. However, long-term mortality is often ignored as it is rarely recorded in such clinical registries. In this study linkage of a clinical registry and administrative database is used to assess the longterm mortality of a large ICU sample. Information about long-term mortality may be used to inform patients about their prognosis, to get insight in factors that influence long-term mortality, and to adjust admission policy to the ICU. This study showed that the observed mortality in the total ICU population at 3, 6, and 12 months after ICU admission was 20.3%, 22.9%, and 26.6% respectively. Medical and urgent surgery patients showed a higher long-term mortality risk and planned surgery patients showed a lower long-term mortality risk compared to the other ICU patients. In this study we have focused on the general ICU population, though linkage of clinical and administrative databases can also be used to perform analyses in specific diagnostic ICU populations or for non-ICU patients. In this study 71.4% of the clinical records could be linked with the administrative database. Future studies should focus on improving linkage of different registries.

Hospital mortality is associated with ICU admission time

Introduction

Previous studies have shown that patients admitted to the intensive care unit (ICU) after “office hours” are more likely to die. However these results have been challenged by numerous other studies. We therefore analysed this possible relationship between ICU admission time and in-hospital mortality in The Netherlands.

Methods

This article relates time of ICU admission to hospital mortality for all patients who were included in the Dutch national ICU registry (National Intensive Care Evaluation, NICE) from 2002 to 2008. We defined office hours as 08:00–22:00 hours during weekdays and 09:00–18:00 hours during weekend days. The weekend was defined as from Saturday 00:00 hours until Sunday 24:00 hours. We corrected hospital mortality for illness severity at admission using Acute Physiology and Chronic Health Evaluation II (APACHE II) score, reason for admission, admission type, age and gender.

Results

A total of 149,894 patients were included in this analysis. The relative risk (RR) for mortality outside office hours was 1.059 (1.031–1.088). Mortality varied with time but was consistently higher than expected during “off hours” and lower during office hours. There was no significant difference in mortality between different weekdays of Monday to Thursday, but mortality increased slightly on Friday (RR 1.046; 1.001–1.092). During the weekend the RR was 1.103 (1.071–1.136) in comparison with the rest of the week.

Conclusions

Hospital mortality in The Netherlands appears to be increased outside office hours and during the weekends, even when corrected for illness severity at admission. However, incomplete adjustment for certain confounders might still play an important role. Further research is needed to fully explain this difference.

Electronic supplementary material

The online version of this article (doi:10.1007/s00134-010-1918-1) contains supplementary material, which is available to authorized users.

The role of social and health statistics in measuring harm from alcohol

Since excess use of alcohol contributes to so many varieties of health and social harms, in most countries, there are many potential sources of data indicative of alcohol-related harms. In few instances, compilation and interpretation of these data are straightforward, but, mostly, they are open to various sources of measurement error, which need to be taken into account if they are to be applied for research purposes. Police and health statistics are the major source of such information, but the underlying systems are not usually set up with the purpose of monitoring alcohol-related events. In both of these domains, types of events can be identified, which are wholly attributable to excess alcohol use, i.e. drunk-driving, alcoholic liver cirrhosis. Specific alcohol-related events are particularly prone to variations in, respectively, police enforcement practices, medical diagnostic fashion and sensitivity to prejudices about alcohol-related problems. A case will be made in this paper for the use of multiple surrogate measures of alcohol-related harm drawn from several sources in order to measure and track local, regional and national trends. For health statistics on mortality and morbidity, the aetiologic fraction (AF) method will be recommended for such monitoring purposes. It will also be recommended that these data be categorised by the degree to which cases are attributable to alcohol and also by whether the underlying hazardous drinking pattern is a brief drinking bout or a sustained pattern of heavy intake over a number of years. Nighttime occurrences of road crashes, public violence from both police and emergency room attendance data will also be recommended. It will be argued that routine recording of alcohol relatedness of events is usually unreliable, and the above surrogate measures are preferable. Recommendations will also be made for utilising national surveys of drinking behaviour to improve the calculation of alcohol-related morbidity and mortality, as well as refine estimates of per capita alcohol consumption, another major 'surrogate' measure of alcohol-related harm. The arguments will be illustrated with reference to Australia's National Alcohol Indicators Project and related research projects.

Traumatic brain injury: influence of blood alcohol level on post-acute cognitive function

Alcohol intoxication frequently contributes to the occurrence of traumatic brain injury. Few studies, however, have examined whether acute pre-injury alcohol intoxication or premorbid history of alcohol abuse exacerbate cognitive impairments that commonly result from traumatic brain injury. This study examined the influence of blood alcohol level at time of hospital admission on cognitive functioning during the post-acute stage of recovery from traumatic brain injury. After controlling for pre-injury history of alcohol abuse, hospital admission blood alcohol level was predictive of poorer delayed verbal memory, greater decrement in verbal memory over time, and poorer visuospatial functioning. Moreover, there were non-significant trends for higher blood alcohol levels to predict poorer performance on measures of immediate verbal memory and perseveration.

Neuropsychological improvement following endarterectomy as a function of outcome measure and reconstructed vessel

30 patient receiving right or left carotid reconstruction and 15 medically matched controls were compared pre- and post-surgically on measures of motor speed, sustained vigilance, verbal memory and verbal and nonverbal intellectual function. The group receiving right sided vessel reconstruction demonstrated the largest post-operative improvement in intellectual function in any of the groups. The findings suggest that increased blood perfusion following right sided endarterectomy facilitates the right hemisphere's exclusive control of bilateral attention/arousal responses. In addition, findings suggest that detection of post-endarterectomy improvement may be dependent on the specific task dimension sampled, e.g., speed vs. cognitive ability and verbal-graphic vs. nonverbal symbol manipulation.

Failure of single-dose lecithin to alter aspects of central cholinergic activity in Alzheimer's disease

The effect of a single dose (15 g/70 kg) of lecithin (95% phosphatidylcholine) on several measures of central cholinergic activity (memory, cortisol, prolactin, pulse, blood pressure) was assessed in individuals with Alzheimer's disease. In contrast to the reported effects of physostigmine, a cholinesterase inhibitor, lecithin had no effect on these parameters, despite significant increases in plasma and erythrocyte choline.