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.

Risk indicators for obstetrical anal sphincter injury in vaginal birth after caesarean section compared to first vaginal delivery

OBJECTIVES

Comparison of the rate of obstetric anal sphincter injury (OASI) between women having their first vaginal birth after caesarean section (CS) and true nulliparous women with a vaginal delivery. Assessment of risk indicators for OASI in women with vaginal birth after one CS (VBAC).

STUDY DESIGN

28 535 women with their first VBAC and a cohort of 275 439 nulliparous women with a vaginal delivery of a liveborn infant in a cephalic position from the Dutch perinatal registry were analyzed. We compared the OASI rate with univariate and multivariate analysis. In women with VBAC possible risk indicators for OASI were assessed using univariate and multivariate logistic regression analysis.

RESULTS

The rate of OASI was 5.2% in women with vaginal birth after CS and 4.0% in women with a first vaginal delivery. The adjusted OR (aOR) for vaginal birth after an elective CS was higher (aOR 1.34, 95% CI 1.23-1.47) compared to vaginal birth after an emergency CS (aOR 1.16, 95% CI 1.08-1.25). In women with vaginal birth after emergency CS, the aOR for the indication non-progressive labor was 1.18 (95% CI 1.08-1.29), whereas CS for suspected fetal distress was not significantly associated with obstetric anal sphincter injury in VBAC. In the 28 535 women with a VBAC, mediolateral episiotomy (MLE), birth weight < 3000 g and maternal age < 25 years were associated with a significantly lower rate of OASI. A gestational age of 42 weeks, birth weight ≥ 3500 g, operative vaginal delivery and duration of the 2nd stage of labour of ≥ 60 min were associated with a significantly higher rate of OASI.

CONCLUSIONS

Women with a VBAC have a higher rate of OASI in comparison with women with a first vaginal delivery, with the exception of women with a vaginal birth after an emergency CS for suspected fetal distress. Factors associated with a significantly lower rate for OASI were MLE, maternal age < 25 and birth weight < 3000 g. A gestational age of 42 weeks, birth weight between 3500 and 4000 g and ≥ 4000 g, operative vaginal delivery and duration of the 2nd stage of delivery longer dan 60 min were associated with a significantly higher rate of OASI.

Interaction between geriatric syndromes in predicting three months mortality risk

OBJECTIVES

Capturing frailty using a quick tool has proven to be challenging. We hypothesise that this is due to the complex interactions between frailty domains. We aimed to identify these interactions and assess whether adding interactions between domains improves mortality predictability.

METHODS

In this retrospective cohort study, we selected all patients aged 70 or older who were admitted to one Dutch hospital between April 2015 and April 2016. Patient characteristics, frailty screening (using VMS (Safety Management System), a screening tool used in Dutch hospital care), length of stay, and mortality within three months were retrospectively collected from electronic medical records. To identify predictive interactions between the frailty domains, we constructed a classification tree with mortality as the outcome using five variables: the four VMS-domains (delirium risk, fall risk, malnutrition, physical impairment) and their sum. To determine if any domain interactions were predictive for three-month mortality, we performed a multivariable logistic regression analysis.

RESULTS

We included 4,478 patients. (median age: 79 years; maximum age: 101 years; 44.8% male) The highest risk for three-month mortality included patients that were physically impaired and malnourished (23% (95%-CI 19.0-27.4%)). Subgroups had comparable three-month mortality risks based on different domains: malnutrition without physical impairment (15.2% (96%-CI 12.4-18.6%)) and physical impairment and delirium risk without malnutrition (16.3% (95%-CI 13.7-19.2%)).

DISCUSSION

We showed that taking interactions between domains into account improves the predictability of three-month mortality risk. Therefore, when screening for frailty, simply adding up domains with a cut-off score results in loss of valuable information.

Identifying prognostic factors for clinical outcomes and costs in four high-volume surgical treatments using routinely collected hospital data

Identifying prognostic factors (PFs) is often costly and labor-intensive. Routinely collected hospital data provide opportunities to identify clinically relevant PFs and construct accurate prognostic models without additional data-collection costs. This multicenter (66 hospitals) study reports on associations various patient-level variables have with outcomes and costs. Outcomes were in-hospital mortality, intensive care unit (ICU) admission, length of stay, 30-day readmission, 30-day reintervention and in-hospital costs. Candidate PFs were age, sex, Elixhauser Comorbidity Score, prior hospitalizations, prior days spent in hospital, and socio-economic status. Included patients dealt with either colorectal carcinoma (CRC, n = 10,254), urinary bladder carcinoma (UBC, n = 17,385), acute percutaneous coronary intervention (aPCI, n = 25,818), or total knee arthroplasty (TKA, n = 39,214). Prior hospitalization significantly increased readmission risk in all treatments (OR between 2.15 and 25.50), whereas prior days spent in hospital decreased this risk (OR between 0.55 and 0.95). In CRC patients, women had lower risk of in-hospital mortality (OR 0.64), ICU admittance (OR 0.68) and 30-day reintervention (OR 0.70). Prior hospitalization was the strongest PF for higher costs across all treatments (31–64% costs increase/hospitalization). Prognostic model performance (c-statistic) ranged 0.67–0.92, with Brier scores below 0.08. R-squared ranged from 0.06–0.19 for LoS and 0.19–0.38 for costs. Identified PFs should be considered as building blocks for treatment-specific prognostic models and information for monitoring patients after surgery. Researchers and clinicians might benefit from gaining a better insight into the drivers behind (costs) prognosis.

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.

Knowledge, health beliefs and attitudes towards dementia and dementia risk reduction among descendants of people with dementia: a qualitative study using focus group discussions

Background

Individuals with a parental family history of dementia have an increased risk of developing dementia because they share their genes as well as their psychosocial behaviour. Due to this increased risk and their experience with dementia, they may be particularly eager to receive information regarding dementia risk reduction (DRR). This study evaluated the knowledge, beliefs and attitudes towards dementia and DRR among descendants of people with dementia.

Method

Using a semi-structured topic guide, three focus group discussions were conducted consisting of 12 female (80%) and 3 male (20%) descendants of people with dementia with a mean (± SD) age of 48.8 (± 12) years. Focus group discussions were audio recorded and transcribed. Each transcript was analysed thoroughly, and where appropriate, a code was generated and assigned by two researchers independently. Then, similar codes were grouped together and categorized into themes.

Results

The items in the topic guide could only be addressed after participants had been given the opportunity to share their experiences of having a parent with dementia. Participants were unaware or uncertain about the possibility of reducing the risk of developing dementia and therefore hesitant to assess their dementia risk without treatment options in sight. Moreover, participants indicated that their general practitioner only gave some information on heritability, not on DRR. Although participants identified a large number of modifiable risk factors as a group during the group discussions, they were eager to receive more information on dementia and DRR. In the end, participants adopted a more positive attitude towards a DRR programme and provided suggestions for the development of future DRR programmes.

Conclusions

Although the research aim was to evaluate the knowledge, beliefs and attitudes towards dementia and DRR, sharing experiences of having a parent with dementia seemed a prerequisite for considering participants’ own risk of developing dementia and participating in a DRR programme. Knowledge of dementia and DRR was limited. Due to unawareness of the possibility of reducing dementia risk, participants were hesitant about assessing their dementia risk. Group discussions positively changed the perception of dementia risk assessment and participants’ willingness to participate in a DRR programme.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-021-11415-2.

Explaining heterogeneity of individual treatment causal effects by subgroup discovery: An observational case study in antibiotics treatment of acute rhino-sinusitis

OBJECTIVES

Individuals may respond differently to the same treatment, and there is a need to understand such heterogeneity of causal individual treatment effects. We propose and evaluate a modelling approach to better understand this heterogeneity from observational studies by identifying patient subgroups with a markedly deviating response to treatment. We illustrate this approach in a primary care case-study of antibiotic (AB) prescription on recovery from acute rhino-sinusitis (ARS).

METHODS

Our approach consists of four stages and is applied to a large dataset in primary care dataset of 24,392 patients suspected of suffering from ARS. We first identify pre-treatment variables that either confound the relationship between treatment and outcome or are risk factors of the outcome. Second, based on the pre-treatment variables we create Synthetic Random Forest (SRF) models to compute the potential outcomes and subsequently the causal individual treatment effect (ITE) estimates. Third, we perform subgroup discovery using the ITE estimates as outcomes to identify positive and negative responders. Fourth, we evaluate the predictive performance of the identified subgroups for predicting the outcome in two ways: the likelihood ratio test, and whether the subgroups are selected via the Akaike Information Criterion (AIC) using backward stepwise variable selection. We validate the whole modelling strategy by means of 10-fold-cross-validation.

RESULTS

Based on 20 pre-treatment variables, four subgroups (three for positive responders and one for negative responders) were identified. The log likelihood ratio tests showed that the subgroups were significant. Variable selection using the AIC kept two of the four subgroups, one for positive responders and one for negative responders. As for the validation of the whole modelling strategy, all reported measures (the number of pre-treatment variables associated with the outcome, number of subgroups, number of subgroups surviving variable selection and coverage) showed little variation.

CONCLUSIONS

With the proposed approach, we identified subgroups of positive and negative responders to treatment that markedly deviate from the mean response. The subgroups showed additive predictive value of the outcome. The modelling approach strategy was shown to be robust on this dataset. Our approach was thus able to discover understandable subgroups from observational data that have predictive value and which may be considered by the clinical users to get insight into who responds positively or negatively to a proposed treatment.

Risk factors for the recurrence of obstetrical anal sphincter injury and the role of a mediolateral episiotomy: an analysis of a national registry

OBJECTIVE

The assessment of risk factors, including mediolateral episiotomy (MLE), for the recurrence of obstetric anal sphincter injury (rOASI).

DESIGN

Population-based cohort study.

SETTING

Data from the nationwide database of the Dutch Perinatal Registry (Perined).

POPULATION

A cohort of 391 026 women at term, of whom 9943 had an OASI in their first delivery and had a second vaginal delivery of a liveborn infant in cephalic position.

METHODS

Possible risk factors were tested for statistical significance using univariate and multivariate logistic regression analysis.

MAIN OUTCOME MEASURES

Rate of rOASI.

RESULTS

The rate of rOASI was 5.8%. Multivariate analysis identified a birthweight of ≥4000 g (adjusted OR, aOR, 2.1, 95% CI 1.6-2.6) and a duration of second stage of ≥30 minutes (aOR 1.8, 95% CI 1.4-2.3) as statistically significant risk factors for rOASI. Mediolateral episiotomy was associated with a statistically significant lower rate of rOASI in spontaneous vaginal delivery (SVD) (aOR 0.4, 95% CI 0.3-0.5) and in operative vaginal delivery (OVD) (aOR 0.2, 95% CI 0.1-0.5).

CONCLUSIONS

Women with a history of OASI have a higher rate of OASI in their next delivery. Duration of the second stage of ≥30 minutes and a birthweight of ≥4000 g are significantly associated with an increased rate of rOASI. Mediolateral episiotomy is associated with a significantly lower rate of rOASI in both SVD and OVD.

TWEETABLE ABSTRACT

Mediolateral episiotomy is associated with a significant lower recurrence rate of OASI in women with an OASI in their first delivery.

External Validation of the Dutch SOURCE Survival Prediction Model in Belgian Metastatic Oesophageal and Gastric Cancer Patients

The SOURCE prediction model predicts individualised survival conditional on various treatments for patients with metastatic oesophageal or gastric cancer. The aim of this study was to validate SOURCE in an external cohort from the Belgian Cancer Registry. Data of Belgian patients diagnosed with metastatic disease between 2004 and 2014 were extracted (n = 4097). Model calibration and discrimination (c-indices) were determined. A total of 2514 patients with oesophageal cancer and 1583 patients with gastric cancer with a median survival of 7.7 and 5.4 months, respectively, were included. The oesophageal cancer model showed poor calibration (intercept: 0.30, slope: 0.42) with an absolute mean prediction error of 14.6%. The mean difference between predicted and observed survival was −2.6%. The concordance index (c-index) of the oesophageal model was 0.64. The gastric cancer model showed good calibration (intercept: 0.02, slope: 0.91) with an absolute mean prediction error of 2.5%. The mean difference between predicted and observed survival was 2.0%. The c-index of the gastric cancer model was 0.66. The SOURCE gastric cancer model was well calibrated and had a similar performance in the Belgian cohort compared with the Dutch internal validation. However, the oesophageal cancer model had not. Our findings underscore the importance of evaluating the performance of prediction models in other populations.

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.

The risk of intrapartum/neonatal mortality and morbidity following birth at 37 weeks of gestation: a nationwide cohort study

Objective

To assess intrapartum/neonatal mortality and morbidity risk in infants born at 37 weeks of gestation compared with infants born at 39–41 weeks of gestation.

Design

Nationwide cohort study.

Setting

The Netherlands.

Population

A total of 755 198 women delivering at term of a singleton without congenital malformations during 2010–14.

Methods

We used data from the national perinatal registry (PERINED). Analysis was performed with logistic regression and stratification for the way labour started and type of care.

Main outcome measures

Intrapartum or neonatal mortality up to 28 days and adverse neonatal outcome (neonatal mortality, 5‐minute Apgar <7, and/or neonatal intensive care unit admission).

Results

At 37 weeks of gestation intrapartum/neonatal mortality was 1.10‰ compared with 0.59‰ at 39–41 weeks (P < 0.0001). Adjusted odds ratio (aOR) for 37 weeks compared with 39–41 weeks was 1.84 (95% CI) 1.39–2.44). Adverse neonatal outcome at 37 weeks was 21.4‰ compared with 12.04‰ at 39–41 weeks (P < 0.0001) with an aOR 1.63 (95% CI 1.53–1.74). Spontaneous start of labour at 37 weeks of gestation was significantly associated with increased intrapartum/neonatal mortality with an aOR of 2.20 (95% CI 1.56–3.10), in both primary (midwifery‐led) care and specialist care. Neither induction of labour nor planned caesarean section showed increased intrapartum/neonatal mortality risk.

Conclusions

Birth at 37 weeks of gestation is independently associated with a higher frequency of clinically relevant adverse perinatal outcomes than birth at 39–41 weeks. In particular, spontaneous start of labour at 37 weeks of gestation doubles the risk for intrapartum/neonatal mortality. Extra fetal monitoring is warranted.

Tweetable abstract

Birth at 37 weeks of gestation gives markedly higher intrapartum/neonatal mortality risk than at 39–41 weeks, especially with spontaneous start of labour.

Tweetable abstract

Birth at 37 weeks of gestation gives markedly higher intrapartum/neonatal mortality risk than at 39–41 weeks, especially with spontaneous start of labour.

Prediction models for patients with esophageal or gastric cancer: A systematic review and meta-analysis

Background

Clinical prediction models are increasingly used to predict outcomes such as survival in cancer patients. The aim of this study was threefold. First, to perform a systematic review to identify available clinical prediction models for patients with esophageal and/or gastric cancer. Second, to evaluate sources of bias in the included studies. Third, to investigate the predictive performance of the prediction models using meta-analysis.

Methods

MEDLINE, EMBASE, PsycINFO, CINAHL, and The Cochrane Library were searched for publications from the year 2000 onwards. Studies describing models predicting survival, adverse events and/or health-related quality of life (HRQoL) for esophageal or gastric cancer patients were included. Potential sources of bias were assessed and a meta-analysis, pooled per prediction model, was performed on the discriminative abilities (c-indices).

Results

A total of 61 studies were included (45 development and 16 validation studies), describing 47 prediction models. Most models predicted survival after a curative resection. Nearly 75% of the studies exhibited bias in at least 3 areas and model calibration was rarely reported. The meta-analysis showed that the averaged c-index of the models is fair (0.75) and ranges from 0.65 to 0.85.

Conclusion

Most available prediction models only focus on survival after a curative resection, which is only relevant to a limited patient population. Few models predicted adverse events after resection, and none focused on patient’s HRQoL, despite its relevance. Generally, the quality of reporting is poor and external model validation is limited. We conclude that there is a need for prediction models that better meet patients’ information needs, and provide information on both the benefits and harms of the various treatment options in terms of survival, adverse events and HRQoL.

Analysis and Design of an Ontology for Intensive Care Diagnoses

Information about the patient‘s health status and about medical problems in general, play an important role in stratifying a patient population for quality assurance of intensive care. A terminological system which supports both the description of health problems for daily care practice and the aggregation of diagnostic information for evaluative research, is desirable for description of the patient population. This study describes the engineering of an ontology that facilitates a terminological system for intensive care diagnoses. We analyzed the criteria for such an ontology and evaluated existing terminological systems according to these criteria. The analysis shows that none of the existing terminological systems completely satisfies all our criteria. We describe choices regarding design, content and representation of a new ontology on which an adequate terminological system is based. The proposed ontology is characterized by the explicit and formal representation of the domain model, the metaspecification of its concepts, the vocabulary to define concepts and the nomenclature to support the composition of new concepts.

Characterizing Decision Support Telemedicine Systems

Objectives: Decision Support Telemedicine Systems (DSTS) are at the intersection of two disciplines: telemedicine and clinical decision support systems (CDSS). The objective of this paper is to provide a set of characterizing properties for DSTSs. This characterizing property set (CPS) can be used for typing, classifying and clustering DSTSs.

Methods: We performed a systematic keyword-based literature search to identify candidate-characterizing properties. We selected a subset of candidates and refined them by assessing their potential in order to obtain the CPS.

Results: The CPS consists of 14 properties, which can be used for the uniform description and typing of applications of DSTSs. The properties are grouped in three categories that we refer to as the problem dimension, process dimension, and system dimension. We provide CPS instantiations for three prototypical applications.

Conclusions: The CPS includes important properties for typing DSTSs, focusing on aspects of communication for the telemedicine part and on aspects of decisionmaking for the CDSS part. The CPS provides users with tools for uniformly describing DSTSs.

Understanding Terminological Systems I: Terminology and Typology

Terminological systems are an important research issue within the field of medical informatics. For precise understanding of existing terminological systems a referential framework is needed that provides a uniform terminology and typology of terminological systems themselves. In this article a uniform terminology is described by putting relevant fundamental notions and definitions used by standard organizations such as CEN and ISO into perspective, and interrelating them to arrive at a useful typology of terminological systems. This typology is illustrated by applying it to five well-known existing terminological systems.

Understanding Terminological Systems II: Experience with Conceptual and Formal Representation of Structure

This article describes the application of two popular conceptual and formal representation formalisms, as part of a framework for understanding terminological systems. A precise understanding of the structure of a terminological system is essential to assess existing terminological systems, to recognize patterns in various systems and to build new terminological systems. Our experience with the application of this framework to five well-known terminological systems is described.

A Framework for Characterizing Terminological Systems

Objectives: The notion of a terminological system (TS) is complex due to the broad range of systems, applications, and clinical domains. A uniform approach to describe the characteristics of TSs is lacking. This impedes furthering understanding, applicability, mutual comparison and development of TSs. For these reasons we propose a terminological systems characterization framework.

Methods: Relevant issues pertaining to TSs and terminology servers have been extracted from literature describing requirements and functionality of TSs. From these issues, features have been distilled and further refined. A categorization has been developed to provide a convenient arrangement of these features.

Results: The framework distinguishes between application-dependent and application-independent features of TSs. Definitions are provided for measures of content coverage, which was identified as the only application-dependent feature. Application-independent features are categorized along two axes: their respective type of TS and the particular element within that system, i.e. the formalism, the content, or the functionality. For each feature we provide an explicit question, the answer to which yields a feature value. The framework has been applied to SNOMED CT and the CLUE browser.

Conclusions: We present and apply a framework to support a feature-based characterization of terminological systems. Standardized methods for content coverage studies reduce the effort of assessing the applicability of a TS for a specific clinical setting. A two-axial categorization provides a convenient arrangement of the large number of application-independent features. Application of the framework increases comparability of terminological systems. This framework may also help TS developers determine how their system can be improved.

Requirements Analysis of Information Services for Patients on a General Practitioner's Website

Objective : To elicit and analyze information needs of patients and primary care physicians (GPs) regarding the information services (static and functional) that a GP's practice website should provide.

Methods : To find candidate information services, we conducted a literature search and examined primary care physicians' websites, especially Dutch websites. Semi-structured depth interviews with the stakeholders, Dutch patients and GPs, were done to arrive at a final checklist. We then conducted a survey to elicit the level of importance associated with each service on the checklist. The data underwent statistical analysis and relevant requirements were formulated. The requirements were then validated by interviews. General website quality and usability aspects were elicited from the literature.

Results : The research resulted in a checklist of 38 selected information services including their priority ratings for patients and GPs; a discrepancy list between GP and patient priorities; and a requirements document containing information services (14 static and 6 functional), and general quality and usability aspects (8 and 5).

Conclusion : The following items occurred in the top 10 of both user groups: general practice information, information of local public health institutions, self-help information, repeat prescription, links to health websites. At the bottom on both priority lists were: links to journals, tests and forums. Dutch GPs are much more selective in terms of which information services to provide on-line. Discrepancy between the two groups concerns on-line services that seem to require a change to the GP's workflow, or those services that are not recognized for reimbursing the GP. Although the Dutch patients' requirements seem to generalize to other patients, the conflict list might depend on the primary care system.

Mobile health in adults with congenital heart disease: current use and future needs

Objective

Many adults with congenital heart disease (CHD) are affected lifelong by cardiac events, particularly arrhythmias and heart failure. Despite the care provided, the cardiac event rate remains high. Mobile health (mHealth) brings opportunities to enhance daily monitoring and hence timely response in an attempt to improve outcome. However, it is not known if adults with CHD are currently using mHealth and what type of mHealth they may need in the near future.

Methods

Consecutive adult patients with CHD who visited the outpatient clinic at the Academic Medical Center in Amsterdam were asked to fill out questionnaires. Exclusion criteria for this study were mental impairment or inability to read and write Dutch.

Results

All 118 patients participated (median age 40 (range 18–78) years, 40 % male, 49 % symptomatic) and 92 % owned a smartphone. Whereas only a small minority (14 %) of patients used mHealth, the large majority (75 %) were willing to start. Most patients wanted to use mHealth in order to receive more information on physical health, and advice on progression of symptoms or signs of deterioration. Analyses on age, gender and complexity of defect showed significantly less current smartphone usage at older age, but no difference in interest or preferences in type of mHealth application for the near future.

Conclusion

The relatively young adult CHD population only rarely uses mHealth, but the majority are motivated to start using mHealth. New mHealth initiatives are required in these patients with a chronic condition who need lifelong surveillance in order to reveal if a reduction in morbidity and mortality and improvement in quality of life can be achieved.

Point and trend accuracy of a continuous intravenous microdialysis-based glucose-monitoring device in critically ill patients: a prospective study

Background

Microdialysis is a well-established technology that can be used for continuous blood glucose monitoring. We determined point and trend accuracy, and reliability of a microdialysis-based continuous blood glucose-monitoring device (EIRUS^®) in critically ill patients.

Methods

Prospective study involving patients with an expected intensive care unit stay of ≥48 h. Every 15 min, device readings were compared with blood glucose values measured in arterial blood during blocks of 8 h per day for a maximum of 3 days. The Clarke error grid, Bland–Altman plot, mean absolute relative difference and glucose prediction error analysis were used to express point accuracy and the rate error grid to express trend accuracy. Reliability testing included aspects of the device and the external sensor, and the special central venous catheter (CVC) with a semipermeable membrane for use with this device.

Results

We collected 594 paired values in 12 patients (65 [26–80; 8–97] (median [IQR; total range]) paired values per patient). Point accuracy: 93.6 % of paired values were in zone A of the Clarke error grid, 6.4 % were in zone B; bias was 4.1 mg/dL with an upper limit of agreement of 28.6 mg/dL and a lower level of agreement of −20.5 mg/dL in the Bland–Altman analysis; 93.6 % of the values ≥75 mg/dL were within 20 % of the reference values in the glucose prediction error analysis; the mean absolute relative difference was 7.5 %. Trend accuracy: 96.4 % of the paired values were in zone A, and 3.3 and 0.3 % were in zone B and zone C of the rate error grid. Reliability: out of 16 sensors, 4 had to be replaced prematurely; out of 12 CVCs, two malfunctioned (one after unintentional flushing by unsupervised nurses of the ports connected to the internal microdialysis chamber, causing rupture of the semipermeable membrane; one for an unknown reason). Device start-up time was 58 [56–67] min; availability of real-time data was 100 % of the connection time.

Conclusions

In this study in critically ill patients who had no hypoglycemic episodes and a limited number of hyperglycemic excursions, point accuracy of the device was moderate to good. Trend accuracy was very good. The device had no downtimes, but 4 out of 16 external sensors and 2 out of 12 CVCs had practical problems.

Electronic supplementary material

The online version of this article (doi:10.1186/s13613-016-0171-3) contains supplementary material, which is available to authorized users.

Assessment of the quality of fall detection and management in primary care in the Netherlands based on the ACOVE quality indicators

We determined adherence to nine fall-related ACOVE quality indicators to investigate the quality of management of falls in the elderly population by general practitioners in the Netherlands. Our findings demonstrate overall low adherence to these indicators, possibly indicating insufficiency in the quality of fall management. Most indicators showed a positive association between increased risk for functional decline and adherence, four of which with statistical significance.

INTRODUCTION

This study aims to investigate the quality of detection and management of falls in the elderly population by general practitioners in the Netherlands, using the Assessing Care of Vulnerable Elders (ACOVE) quality indicators.

METHODS

Community-dwelling persons aged 70 years or above, registered in participating general practices, were asked to fill in a questionnaire designed to determine general practitioner (GP) adherence to fall-related indicators. We used logistic regression to estimate the association between increased risk for functional decline-quantified by the Identification of Seniors At Risk for Primary Care score-and adherence. We then cross-validated the self-reported falls with medical records.

RESULTS

Of the 950 elders responding to our questionnaire, only 10.6 % reported that their GP proactively asked them about falls. Of the 160 patients who reported two or more falls, or one fall for which they visited the GP, only 23.1 % had fall documentation in their records. Adherence ranged between 13.6 and 48.6 %. There was a significant positive association between the ISAR-PC scores and adherence in four QIs. Documentation of falls was highest (36.7 %) in patients whom the GP had proactively asked about falls.

CONCLUSION

Based on patient self-reports, adherence to the ACOVE fall-related indicators was poor, suggesting that the quality of evaluation and management of falls in community-dwelling older persons in the Netherlands is poor. The documentation of falls and fall-related risk factors was also poor. However, for most QIs, adherence to them increased with the increase in the risk of functional decline.

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.

Factors that drive insulin-dosing decisions of diabetes care providers: a vignette-based study in the Netherlands

AIM

To test how certain patient factors would influence the decision of Dutch care providers regarding insulin dose adjustments. We hypothesize that some of these decisions would diverge from recent evidence and consensus statements.

METHODS

We developed narrative vignettes describing clinical scenarios of patients receiving basal insulin therapy. For each vignette, the respondents were asked to indicate whether they would advise a change in insulin dose. A total of 520 paper questionnaires were distributed among physicians and nurses in primary and secondary care in the Netherlands. Multivariate linear and logistic regression analyses were performed to identify factors associated with dosing decisions.

RESULTS

A total of 190 (37%) questionnaires were returned. In cases of a severe rather than mild hypoglycaemic event, care providers were nearly five times more likely to decrease the dose (odds ratio 4.77, 95% CI 1.65-13.75). Care providers were six times more likely to increase the dose when the patient's current dose was low (30 units) rather than high (90 units) (odds ratio 6.38, 95% CI 3.04-13.37). The plasma glucose concentration during a hypoglycaemic event and a known history of cardiovascular disease did not influence the care providers' dosing decisions.

CONCLUSION

Evidence regarding the optimum insulin titration is not always translated into clinical practice. When formulating guidelines, misconceptions should be identified and addressed.

Transient versus persistent acute kidney injury and the diagnostic performance of fractional excretion of urea in critically ill patients

AIMS

To evaluate the performance of fractional excretion of urea (FeU) for differentiating transient (T) from persistent (P) acute kidney injury (AKI) and to assess performance of FeU in predicting AKI in patients admitted to the ICU.

METHODS

We performed secondary analysis of a multicenter prospective observational cohort study on the predictive performance of biological markers for AKI in critically ill patients. AKI was diagnosed according to RIFLE staging.

RESULTS

Of 150 patients, 51 and 41 patients were classified as having T-AKI and P-AKI, respectively. The diagnostic performance for FeU to discriminate T-AKI from P-AKI on the day of AKI was poor (AUC-ROC = 0.61; 95% CI: 0.49-0.73). The diagnostic performance of FeU to predict AKI 1 and 2 days prior to AKI was poor as well (AUC-ROC = 0.61; 95% CI: 0.47-0.74, and 0.58; 95% CI: 0.43-0.73, respectively).

CONCLUSIONS

FeU does not seem to be helpful in differentiating T- from P-AKI in critically ill patients and it is a poor predictor of AKI.

Recurrence risk of preterm birth in subsequent twin pregnancy after preterm singleton delivery

OBJECTIVE

To determine the risk of preterm birth in a subsequent twin pregnancy after previous singleton preterm birth.

DESIGN

Cohort study.

SETTING

Nationwide study in the Netherlands.

POPULATION

In all, 4071 nulliparous women who had a singleton delivery followed by a subsequent twin delivery between the years 1999 and 2007 were studied.

METHODS

Outcome of subsequent twin pregnancy of women with a history of preterm singleton delivery was compared with pregnancy outcome of women with a history of term singleton delivery. First deliveries were subdivided into iatrogenic and spontaneous preterm deliveries. Furthermore analyses were performed by subgroups for gestational age at the time of singleton delivery.

MAIN OUTCOME MEASURE

Spontaneous preterm birth (<37 weeks of gestation) in subsequent twin pregnancy.

RESULTS

In the index singleton pregnancy, preterm birth occurred in 232 (5.7%) of 4071 women. The risk of subsequent twin preterm birth was significantly higher after previous singleton preterm delivery (56.9 versus 20.9%; odds ratio 5.0; 95% CI 3.8-6.6). Risk of subsequent twin preterm birth was dependent on the severity of previous singleton preterm birth and was highest after preceding spontaneous instead of iatrogenic singleton preterm delivery.

CONCLUSION

Preterm birth of a singleton gestation is associated with an increased risk of spontaneous preterm birth in a subsequent twin pregnancy.

Adoption and implementation of the original strict glycemic control guideline is feasible and safe in adult critically ill patients

BACKGROUND

Three trials of tight glucose control (TGC) found clinical benefit of normalization of blood glucose levels in the intensive care unit (ICU). Implementation of TGC was imperfect in subsequent trials, since attained blood glucose levels (BGLs) never reached the targets as in the original trials of TGC. We investigated whether implementation of the TGC guideline as used in the original trials of TGC is feasible and safe.

METHODS

In this study 3 ICUs adopted and implemented the TGC guideline as used in the original trials of TGC using a multifaceted practice change strategy; 3 ICUs that did not change their blood glucose control guideline served as controls. TGC was practiced by physicians and nurses during the first 12-month (period-2), thereafter exclusively by nurses (period-3). Blood glucose metrics 12-month before (period-1) and 24-month after implementation of the guideline were compared.

RESULTS

The analysis included 1321 in period-1, 1169 and 1006 patients in period-2, and -3, respectively, in the intervention ICUs, and 3110 patients in the control ICUs. After implementation of the new TGC guideline, patients in intervention ICUs had lower median BGLs (105 [IQR: 85-130] mg/dL vs. 119 [99-150] mg/dL in period-1, P<0.001; and vs. 113 [95-141] mg/dL in control ICUs, P<0.001). The incidence of severe hypoglycemia initially increased, but again decreased when exclusively nurses practiced TGC, and was not associated with increased mortality or morbidity.

CONCLUSIONS

Implementation of the original TGC guideline is feasible and safe. Our study suggests a learning effect over time.

Statistical process control for monitoring standardized mortality ratios of a classification tree model

OBJECTIVES

The ratio of observed to expected mortality (standardized mortality ratio, SMR), is a key indicator of quality of care. We use PreControl Charts to investigate SMR behavior over time of an existing tree-model for predicting mortality in intensive care units (ICUs) and its implications for hospital ranking. We compare the results to those of a logistic regression model.

METHODS

We calculated SMRs of 30 equally-sized consecutive subsets from a total of 12,143 ICU patients aged 80 years or older and plotted them on a PreControl Chart. We calculated individual hospital SMRs in 2009, with and without repeated recalibration of the models on earlier data.

RESULTS

The overall SMR of the tree-model was stable over time, in contrast to logistic regression. Both models were stable after repeated recalibration. The overall SMR of the tree on the whole validation set was statistically significantly different (SMR 1.00 ± 0.012 vs. 0.94 ± 0.01) and worse in performance than the logistic regression model (AUC 0.76 ± 0.005 vs. 0.79 ± 0.004; Brier score 0.17 ± 0.012 vs. 0.16 ± 0.010). The individual SMRs' range in 2009 was 0.53-1.31 for the tree and 0.64-1.27 for logistic regression. The proportion of individual hospitals with SMR >1, hinting at poor quality of care, reduced from 38% to 29% after recalibration for the tree, and increased from 15% to 35% for logistic regression.

CONCLUSIONS

Although the tree-model has seemingly a longer shelf life than the logistic regression model, its SMR may be less useful for quality of care assessment as it insufficiently responds to changes in the population over time.

Systematic review: acute liver failure - one disease, more than 40 definitions

BACKGROUND

Acute liver failure (ALF) is a clinical syndrome with very high mortality estimates ranging between 60% and 80%.

AIM

To investigate the explicitness and extent of variability in the used ALF definitions in the ALF prognostic literature.

METHODS

All studies that pertain to the prognosis of patients with ALF were electronically searched in MEDLINE (1950-2012) and EMBASE (1950-2012). Identified titles and abstracts were independently screened by three reviewers to determine eligibility for additional review. We included English articles that reported original data from clinical trials or observational studies on ALF patients.

RESULTS

A total of 103 studies were included. Of these studies 87 used 41 different ALF definitions and the remaining 16 studies did not report any explicit ALF definition. Four components underlying ALF definitions accounted for the differences: presence and/or grading of hepatic encephalopathy (HE); the interval between onset of disease and occurrence of HE; presence of coagulopathy and pre-existing liver disease.

CONCLUSIONS

The diversity in acute liver failure definitions hinders comparability and quantitative analysis among studies. There is room for improvement in the reporting of acute liver failure definitions in prognostic studies. The result of this review may be useful as a starting point to create a uniform acute liver failure definition.

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.

Trends in preterm birth: singleton and multiple pregnancies in the Netherlands, 2000-2007

OBJECTIVE

Several studies have reported increasing trends in preterm birth in developed countries, mainly attributable to an increase in medically indicated preterm births. Our aim was to describe trends in preterm birth among singleton and multiple pregnancies in the Netherlands.

DESIGN

Prospective cohort study.

SETTING

Nationwide study.

POPULATION

We studied 1,451,246 pregnant women from 2000 to 2007.

METHODS

We assessed trends in preterm birth. We subdivided preterm birth into spontaneous preterm birth after premature prelabour rupture of membranes (pPROM), medically indicated preterm birth and spontaneous preterm birth without pPROM. We performed analyses separately for singletons and multiples.

MAIN OUTCOME MEASURES

The primary outcome was preterm birth, defined as birth before 37 weeks of gestation, with very preterm birth (<32 weeks of gestation) being a secondary outcome.

RESULTS

The risk of preterm birth was 7.7% and the risk of very preterm birth was 1.3%. In singleton pregnancies, the preterm birth risk decreased significantly from 6.4% to 6.0% (P < 0.0001), mainly as a result of the decrease in spontaneous preterm birth without pPROM (3.6-3.1%, P < 0.0001). In multiple pregnancies, the preterm birth risk increased significantly (47.3-47.7%, P = 0.047), mainly as a result of medically indicated preterm birth, which increased from 15.0% to 17.9% (P < 0.0001).

CONCLUSION

In the Netherlands, the preterm birth risk in singleton pregnancies decreased significantly over the years. The trend of increasing preterm birth risk reported in other countries was only observed in (medically indicated) preterm birth in multiple pregnancies.

Travel time from home to hospital and adverse perinatal outcomes in women at term in the Netherlands

OBJECTIVE

To study the effect of travel time, at the start or during labour, from home to hospital on mortality and adverse outcomes in pregnant women at term in primary and secondary care.

DESIGN

Population-based cohort study from 2000 up to and including 2006.

SETTING

The Netherlands Perinatal Registry.

POPULATION

A total of 751,926 singleton term hospital births.

METHODS

We assessed the impact of travel time by car, calculated from the postal code of the woman's residence to the 99 maternity units, on neonatal outcome. Logistic regression modelling with adjustments for gestational age, maternal age, parity, ethnicity, socio-economic status, urbanisation, tertiary care centres and volume of the hospital was used.

MAIN OUTCOME MEASURES

Mortality (intrapartum, and early and late neonatal mortality) and adverse neonatal outcomes (mortality, Apgar <4 and/or admission to a neonatal intensive care unit).

RESULTS

The mortality was 1.5 per 1000 births, and adverse outcomes occurred in 6.0 per 1000 births. There was a positive relationship between longer travel time (≥20 minutes) and total mortality (OR 1.17, 95% CI 1.002-1.36), neonatal mortality within 24 hours (OR 1.51, 95% CI 1.13-2.02) and with adverse outcomes (OR 1.27, 95% CI 1.17-1.38). In addition to travel time, both delivery at 37 weeks of gestation (OR 2.23, 95% CI 1.81-2.73) or 41 weeks of gestation (OR 1.52, 95% CI 1.29-1.80) increased the risk of mortality.

CONCLUSIONS

A travel time from home to hospital of 20 minutes or more by car is associated with an increased risk of mortality and adverse outcomes in women at term in the Netherlands. These findings should be considered in plans for the centralisation of obstetric care.

A subgroup discovery approach for scrutinizing blood glucose management guidelines by the identification of hyperglycemia determinants in ICU patients

OBJECTIVE

Despite the wide use of blood glucose management guidelines in intensive care (IC), hyperglycemia is still common. The aim of this study was the discovery of possible hyperglycemia determinants by applying the Patient Rule Induction Method (PRIM) to routinely collected data within the first 24 hours of admission, and to relate them to the literature.

METHODS

PRIM was applied in two set-ups to data of 2001 IC patients including 50,021 records of blood glucose levels and other variables. The independent predictors of blood glucose measurements were variables whose value is known before the time of the corresponding measurement. Subgroups were validated using a random split design, and time-sensitivity of performance was analyzed.

RESULTS

PRIM was able to identify relatively large subgroups having markedly high mean glucose values. PRIM also discovered possible determinants of which less is known about their relationship to hyperglycemia. Some possible determinants reported in the literature were not found by PRIM.

CONCLUSIONS

We demonstrated for the first time the utility of using subgroup discovery to uncover possible determinants for non-responsiveness to treatment. This implies the possible use of this technology to scrutinize the effects of various guidelines in clinical medicine on patient outcomes without requiring the development of a global predictive model. We hypothesize that by focusing on the identified subgroups, clinical guidelines may be improved. Further research is required to test this hypothesis.

Requirements analysis of information services for patients on a general practitioner's website--patient and general practitioner's perspectives

OBJECTIVE

To elicit and analyze information needs of patients and primary care physicians (GPs) regarding the information services (static and functional) that a GP's practice website should provide.

METHODS

To find candidate information services, we conducted a literature search and examined primary care physicians' websites, especially Dutch websites. Semi-structured depth interviews with the stakeholders, Dutch patients and GPs, were done to arrive at a final checklist. We then conducted a survey to elicit the level of importance associated with each service on the checklist. The data underwent statistical analysis and relevant requirements were formulated. The requirements were then validated by interviews. General website quality and usability aspects were elicited from the literature.

RESULTS

The research resulted in a checklist of 38 selected information services including their priority ratings for patients and GPs; a discrepancy list between GP and patient priorities; and a requirements document containing information services (14 static and 6 functional), and general quality and usability aspects (8 and 5).

CONCLUSION

The following items occurred in the top 10 of both user groups: general practice information, information of local public health institutions, self-help information, repeat prescription, links to health web sites. At the bottom on both priority lists were: links to journals, tests and forums. Dutch GPs are much more selective in terms of which information services to provide on-line. Discrepancy between the two groups concerns on-line services that seem to require a change to the GP's workflow, or those services that are not recognized for reimbursing the GP. Although the Dutch patients' requirements seem to generalize to other patients, the conflict list might depend on the primary care system.

Decision Support Telemedicine Systems: A Conceptual Model and Reusable Templates

Decision support telemedicine systems (DSTSs) are systems combining elements from telemedicine and clinical decision support systems. Although emerging more, these types of systems have not been given much attention in the literature. Our objective is to define the term DSTS, to propose a general DSTS model, and to propose model-based templates to aid DSTS development for three medical tasks. The definition, general model and model-based templates are based on a systematic literature search. To build the model, we use Unified Modeling Language class-models. The models were supplemented by class-attributes stemming from a recently suggested set of DSTS characterizing properties. We tested the applicability of the templates to new DSTSs found in a separate limited literature search. We provide a definition of DSTS, propose a conceptual model for understanding DSTSs, and synthesize a set of reusable templates, and examples for using them. The templates are shown to be relevant and are likely useful for modeling new systems. Our definition combines and harmonizes the various existing definitions. The conceptual model and the reusable modeling templates are demonstrated to be useful in understanding and modeling DSTSs during the early stages of their development.

Short-term and long-term mortality in very elderly patients admitted to an intensive care unit

OBJECTIVE

To report short-term and long-term mortality of very elderly ICU patients and to determine independent risk factors for short-term and long-term mortality

DESIGN AND SETTING

Retrospective cohort study in the medical/surgical ICU of a tertiary university teaching hospital.

PATIENTS

578 consecutive ICU patients aged 80 years or older.

RESULTS

Demographic, physiological, and laboratory values derived from the first 24h after ICU admission. ICU mortality of unplanned surgical (34.0%) and medical patients (37.7%) was higher than that of planned surgical patients (10.6%), as was post-ICU hospital mortality (26.5% and 29.7% vs. 4.4%). Mortality 12 months after hospital discharge, including ICU and hospital mortality, was 62.1% in unplanned surgical and 69.2% in medical patients vs. 21.6% in planned patients. Only median survival of planned surgical patients did not differ from survival in the age- and gender-matched general population. Independent risk factors for ICU mortality were lower Glasgow Coma Scale score, higher SAPS II score, the lowest urine output over 8 h, abnormal body temperature, low plasma bicarbonate levels, and higher oxygen fraction of inspired air. High urea concentrations and admission type were risk factors for hospital mortality, and high creatinine concentration was an independent risk factor for 12-month mortality.

CONCLUSION

Mortality in very elderly patients after unplanned surgical or medical ICU admission is higher than after planned admission. The most important factors independently associated with ICU mortality were related to the severity of illness at admission. Long-term mortality was associated with renal function.

A framework for characterizing terminological systems

OBJECTIVES

The notion of a terminological system (TS) is complex due to the broad range of systems, applications, and clinical domains. A uniform approach to describe the characteristics of TSs is lacking. This impedes furthering understanding, applicability, mutual comparison and development of TSs. For these reasons we propose a terminological systems characterization framework.

METHODS

Relevant issues pertaining to TSs and terminology servers have been extracted from literature describing requirements and functionality of TSs. From these issues, features have been distilled and further refined. A categorization has been developed to provide a convenient arrangement of these features.

RESULTS

The framework distinguishes between application-dependent and application-independent features of TSs. Definitions are provided for measures of content coverage, which was identified as the only application-dependent feature. Application-independent features are categorized along two axes: their respective type of TS and the particular element within that system, i.e. the formalism, the content, or the functionality. For each feature we provide an explicit question, the answer to which yields a feature value. The framework has been applied to SNOMED CT and the CLUE browser.

CONCLUSIONS

We present and apply a framework to support a feature-based characterization of terminological systems. Standardized methods for content coverage studies reduce the effort of assessing the applicability of a TS for a specific clinical setting. A two-axial categorization provides a convenient arrangement of the large number of application-independent features. Application of the framework increases comparability of terminological systems. This framework may also help TS developers determine how their system can be improved.

Characterizing Decision Support Telemedicine Systems

OBJECTIVES

Decision Support Telemedicine Systems (DSTS) are at the intersection of two disciplines: tele-medicine and clinical decision support systems (CDSS). The objective of this paper is to provide a set of characterizing properties for DSTSs. This characterizing property set (CPS) can be used for typing, classifying and clustering DSTSs.

METHODS

We performed a systematic keyword-based literature search to identify candidate-characterizing properties. We selected a subset of candidates and refined them by assessing their potential in order to obtain the CPS.

RESULTS

The CPS consists of 14 properties, which can be used for the uniform description and typing of applications of DSTSs. The properties are grouped in three categories that we refer to as the problem dimension, process dimension, and system dimension. We provide CPS instantiations for three prototypical applications.

CONCLUSIONS

The CPS includes important properties for typing DSTSs, focusing on aspects of communication for the telemedicine part and on aspects of decisionmaking for the CDSS part. The CPS provides users with tools for uniformly describing DSTSs.