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.

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.

Prognostic models of in-hospital mortality of intensive care patients using neural representation of unstructured text: A systematic review and critical appraisal.

OBJECTIVE: To review and critically appraise published and preprint reports of prognostic models of in-hospital mortality of patients in the intensive-care unit (ICU) based on neural representations (embeddings) of clinical notes. METHODS: PubMed and arXiv were searched up to August 1, 2022. At least two reviewers independently selected the studies that developed a prognostic model of in-hospital mortality of intensive-care patients using free-text represented as embeddings and extracted data using the CHARMS checklist. Risk of bias was assessed using PROBAST. Reporting on the model was assessed with the TRIPOD guideline. To assess the machine learning components that were used in the models, we present a new descriptive framework based on different techniques to represent text and provide predictions from text. The study protocol was registered in the PROSPERO database (CRD42022354602). RESULTS: Eighteen studies out of 2,825 were included. All studies used the publicly-available MIMIC dataset. Context-independent word embeddings are widely used. Model discrimination was provided by all studies (AUROC 0.75-0.96), but measures of calibration were scarce. Seven studies used both structural clinical variables and notes. Model discrimination improved when adding clinical notes to variables. None of the models was externally validated and often a simple train/test split was used for internal validation. Our critical appraisal demonstrated a high risk of bias in all studies and concerns regarding their applicability in clinical practice. CONCLUSION: All studies used a neural architecture for prediction and were based on one publicly available dataset. Clinical notes were reported to improve predictive performance when used in addition to only clinical variables. Most studies had methodological, reporting, and applicability issues. We recommend reporting both model discrimination and calibration, using additional data sources, and using more robust evaluation strategies, including prospective and external validation. Finally, sharing data and code is encouraged to improve study reproducibility.

The daily updated Dutch national database on COVID-19 epidemiology, vaccination and sewage surveillance.

The Dutch national open database on COVID-19 has been incrementally expanded since its start on 30 April 2020 and now includes datasets on symptoms, tests performed, individual-level positive cases and deaths, cases and deaths among vulnerable populations, settings of transmission, hospital and ICU admissions, SARS-CoV-2 variants, viral loads in sewage, vaccinations and the effective reproduction number. This data is collected by municipal health services, laboratories, hospitals, sewage treatment plants, vaccination providers and citizens and is cleaned, analysed and published, mostly daily, by the National Institute for Public Health and the Environment (RIVM) in the Netherlands, using automated scripts. Because these datasets cover the key aspects of the pandemic and are available at detailed geographical level, they are essential to gain a thorough understanding of the past and current COVID-19 epidemiology in the Netherlands. Future purposes of these datasets include country-level comparative analysis on the effect of non-pharmaceutical interventions against COVID-19 in different contexts, such as different cultural values or levels of socio-economic disparity, and studies on COVID-19 and weather factors.

Automated identification of patient subgroups: A case-study on mortality of COVID-19 patients admitted to the ICU.

BACKGROUND: - Subgroup discovery (SGD) is the automated splitting of the data into complex subgroups. Various SGD methods have been applied to the medical domain, but none have been extensively evaluated. We assess the numerical and clinical quality of SGD methods. METHOD: - We applied the improved Subgroup Set Discovery (SSD++), Patient Rule Induction Method (PRIM) and APRIORI - Subgroup Discovery (APRIORI-SD) algorithms to obtain patient subgroups on observational data of 14,548 COVID-19 patients admitted to 73 Dutch intensive care units. Hospital mortality was the clinical outcome. Numerical significance of the subgroups was assessed with information-theoretic measures. Clinical significance of the subgroups was assessed by comparing variable importance on population and subgroup levels and by expert evaluation. RESULTS: - The tested algorithms varied widely in the total number of discovered subgroups (5-62), the number of selected variables, and the predictive value of the subgroups. Qualitative assessment showed that the found subgroups make clinical sense. SSD++ found most subgroups (n = 62), which added predictive value and generally showed high potential for clinical use. APRIORI-SD and PRIM found fewer subgroups (n = 5 and 6), which did not add predictive value and were clinically less relevant. CONCLUSION: - Automated SGD methods find clinical subgroups that are relevant when assessed quantitatively (yield added predictive value) and qualitatively (intensivists consider the subgroups significant). Different methods yield different subgroups with varying degrees of predictive performance and clinical quality. External validation is needed to generalize the results to other populations and future research should explore which algorithm performs best in other settings.

Dynamic prediction of mortality in COVID-19 patients in the intensive care unit: A retrospective multi-center cohort study.

Background: The COVID-19 pandemic continues to overwhelm intensive care units (ICUs) worldwide, and improved prediction of mortality among COVID-19 patients could assist decision making in the ICU setting. In this work, we report on the development and validation of a dynamic mortality model specifically for critically ill COVID-19 patients and discuss its potential utility in the ICU. Methods: We collected electronic medical record (EMR) data from 3222 ICU admissions with a COVID-19 infection from 25 different ICUs in the Netherlands. We extracted daily observations of each patient and fitted both a linear (logistic regression) and non-linear (random forest) model to predict mortality within 24 h from the moment of prediction. Isotonic regression was used to re-calibrate the predictions of the fitted models. We evaluated the models in a leave-one-ICU-out (LOIO) cross-validation procedure. Results: The logistic regression and random forest model yielded an area under the receiver operating characteristic curve of 0.87 [0.85; 0.88] and 0.86 [0.84; 0.88], respectively. The recalibrated model predictions showed a calibration intercept of -0.04 [-0.12; 0.04] and slope of 0.90 [0.85; 0.95] for logistic regression model and a calibration intercept of -0.19 [-0.27; -0.10] and slope of 0.89 [0.84; 0.94] for the random forest model. Discussion: We presented a model for dynamic mortality prediction, specifically for critically ill COVID-19 patients, which predicts near-term mortality rather than in-ICU mortality. The potential clinical utility of dynamic mortality models such as benchmarking, improving resource allocation and informing family members, as well as the development of models with more causal structure, should be topics for future research.

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.

Linking of global intensive care (LOGIC): An international benchmarking in critical care initiative.

Benchmarking is a common and effective method for measuring and analyzing ICU performance. With the existence of national registries, objective information can now be obtained to allow benchmarking of ICU care within and between countries. The present manuscript briefly describes the current status of benchmarking in healthcare and critical care and presents the LOGIC project, an initiative to promote international benchmarking for intensive care units. Currently 13 registries have joined LOGIC. We showed large differences in the utilization of ICU as well as resources and in outcomes. Despite the need for careful interpretation of differences due to variation in definitions and limited risk adjustment, LOGIC is a growing worldwide initiative that allows access to insightful epidemiologic data from ICUs in multiple databases and registries.

The effect of ICU-tailored drug-drug interaction alerts on medication prescribing and monitoring: protocol for a cluster randomized stepped-wedge trial.

BACKGROUND: Drug-drug interactions (DDIs) can cause patient harm. Between 46 and 90% of patients admitted to the Intensive Care Unit (ICU) are exposed to potential DDIs (pDDIs). This rate is twice as high as patients on general wards. Clinical decision support systems (CDSSs) have shown their potential to prevent pDDIs. However, the literature shows that there is considerable room for improvement of CDSSs, in particular by increasing the clinical relevance of the pDDI alerts they generate and thereby reducing alert fatigue. However, consensus on which pDDIs are clinically relevant in the ICU setting is lacking. The primary aim of this study is to evaluate the effect of alerts based on only clinically relevant interactions for the ICU setting on the prevention of pDDIs among Dutch ICUs. METHODS: To define the clinically relevant pDDIs, we will follow a rigorous two-step Delphi procedure in which a national expert panel will assess which pDDIs are perceived clinically relevant for the Dutch ICU setting. The intervention is the CDSS that generates alerts based on the clinically relevant pDDIs. The intervention will be evaluated in a stepped-wedge trial. A total of 12 Dutch adult ICUs using the same patient data management system, in which the CDSS will operate, were invited to participate in the trial. Of the 12 ICUs, 9 agreed to participate and will be enrolled in the trial. Our primary outcome measure is the incidence of clinically relevant pDDIs per 1000 medication administrations. DISCUSSION: This study will identify pDDIs relevant for the ICU setting. It will also enhance our understanding of the effectiveness of alerts confined to clinically relevant pDDIs. Both of these contributions can facilitate the successful implementation of CDSSs in the ICU and in other domains as well. TRIAL REGISTRATION: Nederlands Trial register Identifier: NL6762 . Registered November 26, 2018.

Trends in practice of blood glucose control in critically ill patients in the Netherlands.

BACKGROUND: Publication of the Normoglycemia in Intensive Care Evaluation and Survival Using Glucose Algorithm Regulation (NICE-SUGAR) trial in 2009 and several observational studies caused a change in the recommendations for blood glucose control in intensive care patients. We evaluated local trends in blood glucose control in intensive care units in the Netherlands before and after the publication of the NICE-SUGAR trial and the revised Surviving Sepsis Campaign (SSC) guidelines in 2012. METHODS: Survey focusing on the timing of changes in thresholds in local guidelines for blood glucose control and interrupted time-series analysis of patients admitted to seven intensive care units in the Netherlands from September 2008 through July 2014. Statistical process control was used to visualise and analyse trends in metrics for blood glucose control in association with the moment changes became effective. RESULTS: Overall, the mean blood glucose level increased and the median percentage of blood glucose levels within the normoglycaemic range and in the hypoglycaemic range decreased, while the relative proportion of hyperglycaemic measurements increased. Changes in metrics were notable after publication of the NICE-SUGAR trial and the SSC guidelines but more frequent after changes in local guidelines; some changes seemed to appear independent of changes in local guidelines. CONCLUSION: Local guidelines for blood glucose practice have changed in intensive care units in the Netherlands since the publication of the NICE-SUGAR trial and the revised SSC guidelines. Trends in the metrics for blood glucose control suggest new, higher target ranges for blood glucose control.

Recommendations for intensive care follow-up clinics; report from a survey and conference of Dutch intensive cares.

BACKGROUND: With the increasing awareness of postintensive care syndrome and the unbridled development of post-ICU clinics in the Netherlands, guidelines for ICU after care are needed. The purpose of this study was to develop recommendations for the set-up of post-ICU clinics. METHODS: Recommendations regarding the design of post-ICU clinics were formulated based on a survey among Dutch ICUs and the available literature. Subsequently, in a round table conference stakeholders discussed and voted on a final approval of the recommendations. RESULTS: The response rate of our survey was 70% (57 of 82), 40% of the respondents provided ICU after care. Twenty-one people from 16 ICUs participated in the round table conference. Only two studies are available with information on organization and effectiveness of post-ICU clinics. It is recommended to invite patients who are mechanically ventilated for more than 2 days at a post-ICU clinic between 6 and 12 weeks after hospital discharge and screen for physical, psychological and cognitive impairments by using validated electronic patient-reported questionnaires. The set-up of a national registry for benchmarking and research purposes is suggested. CONCLUSION: This study recommends how to organize post-ICU clinics based on literature and expert opinion. The implementation of the recommendations will facilitate the set-up of post-ICU clinics, research on effectiveness of post-ICU clinics and benchmarking of quality of ICU care.

Positive end-expiratory pressure following coronary artery bypass grafting.

BACKGROUND: Cardiac surgery-related pulmonary complications include alterations in lung mechanics and anomalies in gas exchange. Higher levels of positive end-expiratory pressure (PEEP) have been suggested to benefit cardiac surgical patients. We compared respiratory compliance, arterial oxygenation and time till tracheal extubation in 2 cohorts of patients weaned from mechanical ventilation with different levels of PEEP after elective and uncomplicated coronary artery bypass grafting (CABG). We hypothesized that higher PEEP levels improve pulmonary compliance and gas exchange in the first hours of weaning from mechanical ventilation, but not to shorten time till tracheal extubation. METHODS: Secondary retrospective analysis of 2 randomized controlled trials: in the first trial patients were weaned with PEEP levels of 10 cmH2O for the first 4 hours followed by PEEP levels of 5 cmH2O until tracheal extubation (high PEEP, HP); and the second trial patients were weaned with PEEP levels of 5 cmH2O during the entire weaning phase (low PEEP, LP). The primary endpoint was pulmonary compliance. Secondary endpoints included arterial oxygenation, duration of mechanical ventilation and postoperative pulmonary complications. RESULTS: The analysis included 121 patients; 60 HP patients and 61 LP patients. Baseline characteristics were similar. Compared to LP patients, HP patients had a better pulmonary compliance, 47.2±14.1 versus 42.7±10.2 ml/cmH2O (P<0.05), and higher levels of PaO2, 18.5±6.6 (138.75±49.5) versus 16.7±5.4 (125.25±40.5) kPa (mmHg) (P<0.05). Patients in the HP group were less frequent in need of supplementary oxygen after ICU discharge. These differences remained present during the entire weaning phase, even after reduction of PEEP. However, HP patients had a longer time till tracheal extubation, 16.9±6.1 versus 10.5±5.0 hours (P<0.001). HP patients had longer durations of postoperative infusion of propofol, 4.9 (2.6-7.4) versus 3.5 (1.8-5.8) hours (P<0.05). There were no differences in use of inotropes. Cumulative fluid balances were slightly higher in HP patients. CONCLUSION: Use of higher PEEP levels after elective uncomplicated CABG improves pulmonary compliance and oxygenation but seems to be associated with a delay in tracheal extubation.

Five years of therapeutic drug monitoring in the intensive care did not change vancomycin prescription behaviour: perceived needs for decision support.

BACKGROUND: This study aims to assess clinicians' behaviour in prescribing vancomycin in the Intensive Care Unit (ICU) and their adherence to local guidelines for therapeutic drug monitoring (TDM). METHODS: In this observational cohort study we included all consecutive patients admitted to a 28-bed multidisciplinary mixed adult ICU of a large university hospital in Amsterdam between January 2002 and September 2007 who were prescribed vancomycin for ≥ 3 days. We measured guideline adherence by checking for each given advice the corresponding action and monitored adherence over time using Statistical Process Control. RESULTS: In 475 patients prescribed vancomycin, 1336 serum concentrations were measured, of which 598 in time and 738 with a median delay of 31 hours. Dose or dose frequency adjustments were often not done (54% in advice 2 [half dose frequency] and 86% in advice 4 [increase dose with 50%]) or not done concordantly (32% in advice 2 [half dose frequency] and 60% in advice 7 [half dose frequency if trough serum concentration]). Although adherence was stable over time, the average level was low (58.7%). CONCLUSION: Five years of TDM did not achieve the desired prescription behaviour in the ICU and clinicians feel there is a need for computerized decision support. Local projects should measure adherence and implement appropriate solutions.

Computed radiography versus mobile direct radiography for bedside chest radiographs: impact of dose on image quality and reader agreement.

AIM: To asses the image quality and potential for dose reduction of mobile direct detector (DR) chest radiography as compared with computed radiography (CR) for intensive care unit (ICU) chest radiographs (CXR). METHODS AND MATERIALS: Three groups of age-, weight- and disease-matched ICU patients (n=114 patients; 50 CXR per acquisition technique) underwent clinically indicated bedside CXR obtained with either CR (single read-out powder plates) or mobile DR (GOS-TFT detectors) at identical or 50% reduced dose (DR(50%)). Delineation of anatomic structures and devices used for patient monitoring, overall image quality and disease were scored by four readers. In 12 patients pairs of follow-up CR and DR images were available, and in 15 patients pairs of CR and DR(50%) images were available. In these pairs the overall image quality was also compared side-by-side. RESULTS: Delineation of anatomy in the mediastinum was scored better with DR or DR(50%) than with CR. Devices used for patient monitoring were seen best with DR, with DR(50%) being superior to CR. In the side-by-side comparison, the overall image quality of DR and DR(50%) was rated better than CR in 96% (46/48) and 87% (52/60), respectively. Inter-observer agreement for the assessment of pathology was fair for CR and DR(50%) (κ = 0.33 and κ = 0.39, respectively) and moderate for DR (κ = 0.48). CONCLUSION: Mobile DR units offer better image quality than CR for bedside chest radiography and allow for 50% dose reduction. Inter-observer agreement increases with image quality and is superior with DR, while DR(50%) and CR are comparable.

Early predictability of the need for tracheotomy after admission to ICU: an observational study.

BACKGROUND: The goal of this study was to explore the ability of professional judgment to predict the need for tracheotomy early among intensive care unit (ICU) patients. METHODS: Prospective study using daily questionnaires among ICU physicians in a mixed medical-surgical ICU. The prediction of tracheotomy was by a visual analogue scale (VAS, from 1 to 10, with 1 representing 'absolutely no need for tracheotomy' and 10 representing 'pertinent need for tracheotomy') during ICU stay until tracheal extubation or tracheotomy. For the purpose of this study, a VAS score ≥ 8 was considered a positive prediction for tracheotomy. RESULTS: A total of 476 questionnaires were retrieved for 75 patients (6.4 ± 5.2 questionnaires per patient), of which 11 patients finally proceeded with a tracheostomy. At first assessment (mean of 2.4 ± 0.8 days after ICU admittance), ICU physicians predicted the need for tracheotomy 3.0 (2.0-6.0) higher VAS points for patients who were finally tracheotomized (P<0.01). Patients with a positive prediction had a 5.4 (1.2-24.1) higher chance of receiving tracheotomy (P=0.03). Considering the median VAS score over a maximum of 10 days before tracheotomy, ICU physicians scored tracheotomized patients significantly higher from day 8 onwards. When comparing ICU physicians, fellows and residents separately, only staff physicians scored a significant difference in the VAS score (P<0.05). CONCLUSION: ICU physicians are able to differentiate between patients in need for tracheotomy from those who do not, within 2 days from admittance. The closer the time to the actual intervention, the better the physicians are able to predict this decision.

Tracheostomy: current practice on timing, correction of coagulation disorders and peri-operative management - a postal survey in the Netherlands.

BACKGROUND: Several factors may delay tracheostomy. As many critically ill patients either suffer from coagulation abnormalities or are being treated with anticoagulants, fear of bleeding complications during the procedure may also delay tracheostomy. It is unknown whether such (usually mild) coagulation abnormalities are corrected first and to what extent. The purpose of this study was to ascertain current practice of tracheostomy in the Netherlands with regard to timing, pre-operative correction of coagulation disorders and peri-/intra-operative measures. METHODS: In October 2005, a questionnaire was sent to the medical directors of all non-pediatric ICUs with >/=5 beds suitable for mechanical ventilation in the Netherlands. RESULTS: A response was obtained from 44 (64%) out of 69 ICUs included in the survey. Seventy-five percent of patients receive tracheostomy within 2 days after the decision to proceed with a tracheostomy. Reasons indicated as frequent causes for delay were most often logistical factors. A heterogeneous attitude exists regarding values of coagulation parameters acceptable to perform tracheostomy. Fifty percent of the respondents have no guideline on correction of coagulation disorders or anticoagulant therapy before tracheostomy. Antimicrobial prophylaxis is almost never administered before tracheostomy. Forty-eight percent mentioned always using endoscopic guidance and 66% of ICUs only perform chest radiography on indication. CONCLUSIONS: There is a high variation in peri- and intra-operative practice of tracheostomy in the Netherlands. Especially on the subject of coagulation and tracheostomy there are different opinions and protocols are often lacking.

Effects of prolonged intensive care unit stay on quality of life and long-term survival after transthoracic esophageal resection.

OBJECTIVE: There are few prospective data on the effects of prolonged intensive care unit stay on the quality of life and long-term survival of a homogeneous patient population. Therefore, the aims of this prospective study were a) to describe the quality of life in patients after having a transthoracic esophageal resection; and b) to analyze the influences of a prolonged intensive care unit stay on quality of life and survival in patients after esophageal cancer resection who survived to hospital discharge. DESIGN: Prospective study. SETTING: Medical center. PATIENTS: The study population consisted of 109 patients undergoing a transthoracic resection for adenocarcinoma of the middistal esophagus or gastric cardia between April 1994 and February 2000. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: A comparison was made between patients staying or=6 days in the intensive care unit and also or=14 days. Quality of life was assessed in all patients by mailed self-report questionnaires at baseline (preoperatively), at 5 wks, and at 3, 6, 9, 12, 18, 24, 30, and 36 months after surgery. Daily physical, emotional, and social functioning was assessed with the generic Medical Outcome Studies Short Form-20. Disease-specific quality of life was measured by an adapted Rotterdam Symptom Check List. Quality of life data were gathered between July 1994 and March 2003. Five of the 109 patients died in the hospital and were excluded from the analysis. All five of them were in the intensive care unit >or=6 days. Of the remaining 104 patients, 92 provided baseline scores. The data of the 92 patients were used for the quality of life analyses. For the clinicopathologic and survival analysis, the data of 104 hospital survivors were used. Patients spent a median of 5.5 days (range 0-71) in the intensive care unit. The Medical Outcome Studies Short Form-20 and the Rotterdam Symptom Check List measurements showed no clear differences in long-term quality of life between patients after a short vs. a prolonged postoperative intensive care unit period. The median overall survival in all patients was 2.0 yrs (range 0.1-8.0). Median overall survival in patients staying in the intensive care unit or=6 days (p = .9, log-rank test). Median overall survival in patients staying in the intensive care unit or=14 days (p = .74, log-rank test). CONCLUSIONS: For patients who survived to hospital discharge after transthoracic esophagectomy, there was no difference in long-term quality of life or survival between those submitted to the intensive care unit for a short period vs. a long period.