Post-coordination in practice: Evaluating compositional terminological system-based registration of ICU reasons for admission

Background: Re-use of patient data from an electronic patient record heavily relies on structured, coded data. Terminological systems (TSs) are meant to support this. TSs are more commonly implemented nowadays, especially those that support post-coordination. The effect of using a compositional TS on correctness and specificity of captured patient data is unknown. Objective: To evaluate the agreement between free-text reasons for admission to intensive care captured in a patient data management system (PDMS) and reasons for admission that were recorded using a compositional TS embedded in the PDMS. Methods: Observational study comparing pairs of free-text reasons for admission to intensive care with reasons for admission that were recorded using a compositional TS. Both reasons for admission were captured in the PDMS by clinicians during regular care practice. Each pair was judged as exact match, partial match or mismatch by two independent raters. Partial matches were further analyzed to investigate whether free-text or TS-based reasons for admissions included more detail and whether these differences could be explained by the content, the interface of the TS or by user or usability characteristics. Results: Eleven percent of the pairs matched exactly, 79% of the pairs matched partially and 10% of the pairs did not match. Compared to free-text registration TS-based registration resulted in more detail for 21% of the partial matches, in less detail for 43% of the partial matches and in 36% of the partial matches some detail was added while at the same time other detail was lacking. In 65% of the cases in which the TS-based registration lacked some detail, this detail was available in the content of the TS. Physicians who used the TS occasionally had a significantly higher percentage of mismatches. Conclusion: In practice, post-coordination leads to information with different detail but a level of detail comparable to free-text registration of reasons for admission. Details missing in the TS-based reasons for admission were most often available in the TS, indicating that user interaction with the system is more of an impediment than the contents of the TS. (C) 2008 Elsevier Ireland Ltd. All rights reserve

Mortality prediction by SOFA score in ICU-patients after cardiac surgery; Comparison with traditional prognostic-models

Background: There are many prognostic models and scoring systems in use to predict mortality in ICU patients. The only general ICU scoring system developed and validated for patients after cardiac surgery is the APACHE-IV model. This is, however, a labor-intensive scoring system requiring a lot of data and could therefore be prone to error. The SOFA score on the other hand is a simpler system, has been widely used in ICUs and could be a good alternative. The goal of the study was to compare the SOFA score with the APACHE-IV and other ICU prediction models. Methods: We investigated, in a large cohort of cardiac surgery patients admitted to Dutch ICUs, how well the SOFA score from the first 24 h after admission, predict hospital and ICU mortality in comparison with other recalibrated general ICU scoring systems. Measures of discrimination, accuracy, and calibration (area under the receiver operating characteristic curve (AUC), Brier score, R2, and Ĉ-statistic) were calculated using bootstrapping. The cohort consisted of 36,632 Patients from the Dutch National Intensive Care Evaluation (NICE) registry having had a cardiac surgery procedure for which ICU admission was necessary between January 1st, 2006 and June 31st, 2018. Results: Discrimination of the SOFA-, APACHE-IV-, APACHE-II-, SAPS-II-, MPM24-II - models to predict hospital mortality was good with an AUC of respectively: 0.809, 0.851, 0.830, 0.850, 0.801. Discrimination of the SOFA-, APACHE-IV-, APACHE-II-, SAPS-II-, MPM24-II - models to predict ICU mortality was slightly better with AUCs of respectively: 0.809, 0.906, 0.892, 0.919, 0.862. Calibration of the models was generally poor. Conclusion: Although the SOFA score had a good discriminatory power for hospital- and ICU mortality the discriminatory power of the APACHE-IV and SAPS-II was better. The SOFA score should not be preferred as mortality prediction model above traditional prognostic ICU-models

The healthcare costs of intoxicated patients who survive ICU admission are higher than non-intoxicated ICU patients : a retrospective study combining healthcare insurance data and data from a Dutch national quality registry

BACKGROUND: The aim of this study was to describe the healthcare costs of intoxicated ICU patients in the year before and the year after ICU admission, and to compare their healthcare costs with non-intoxicated ICU patients and a population based control group. METHODS: We conducted a retrospective cohort study, combining a national health insurance claims database and a national quality registry database for ICUs. Claims data in the timeframe 2012 until 2014 were combined with the clinical data of patients who had been admitted to an ICU during 2013. Three study populations were compared and matched according to socioeconomic status, type of admission, age and gender: an "ICU population", an "intoxication population" and a "control population" (who had never been on the ICU). RESULTS: 2591 individual "intoxicated ICU patients" were compared to 2577 general "ICU patients" and 2591 patients from the "control population". The median and interquartile ranges (IQR) healthcare costs per day alive for the "intoxicated ICU patients" were higher during the year before ICU admission (€20.3 (IQR €3.6-€76.4)) and the year after ICU admission (€23.9 (IQR €5.1-€82.4)) compared to the ICU population (€6.1 (IQR €0.9-€29.3) and €13.6 (IQR €3.3-€54.9) respectively) and a general control population (€1.1 (IQR €0.3-€4.6) and €1.1 (IQR €0.4-€4.9) respectively). The healthcare associated costs in intoxicated ICU patients were correlated with the number of chronic conditions present prior ICU admission (p < 0.0001). CONCLUSIONS: Intoxicated patients admitted to the ICU had in the year before and after ICU admission much higher median healthcare costs per day alive compared to other ICU patients and a general population control group. Healthcare costs are greatly influenced by the number of psychiatric and other chronic conditions of these intoxicated patients

The healthcare costs of intoxicated patients who survive ICU admission are higher than non-intoxicated ICU patients: A retrospective study combining healthcare insurance data and data from a Dutch national quality registry

Background: The aim of this study was to describe the healthcare costs of intoxicated ICU patients in the year before and the year after ICU admission, and to compare their healthcare costs with non-intoxicated ICU patients and a population based control group. Methods: We conducted a retrospective cohort study, combining a national health insurance claims database and a national quality registry database for ICUs. Claims data in the timeframe 2012 until 2014 were combined with the clinical data of patients who had been admitted to an ICU during 2013. Three study populations were compared and matched according to socioeconomic status, type of admission, age and gender: an "ICU population", an "intoxication population" and a "control population" (who had never been on the ICU). Results: 2591 individual "intoxicated ICU patients" were compared to 2577 general "ICU patients" and 2591 patients from the "control population". The median and interquartile ranges (IQR) healthcare costs per day alive for the "intoxicated ICU patients" were higher during the year before ICU admission (€20.3 (IQR €3.6-€76.4)) and the year after ICU admission (€23.9 (IQR €5.1-€82.4)) compared to the ICU population (€6.1 (IQR €0.9-€29.3) and €13.6 (IQR €3.3-€54.9) respectively) and a general control population (€1.1 (IQR €0.3-€4.6) and €1.1 (IQR €0.4-€4.9) respectively). The healthcare associated costs in intoxicated ICU patients were correlated with the number of chronic conditions present prior ICU admission (p < 0.0001). Conclusions: Intoxicated patients admitted to the ICU had in the year before and after ICU admission much higher median healthcare costs per day alive compared to other ICU patients and a general population control group. Healthcare costs are greatly influenced by the number of psychiatric and other chronic conditions of these intoxicated patients

Using SNOMED CT to identify a Crossmap between two Classification Systems: A Comparison with an Expert-Based and a Data-Driven Strategy

A crossmap between successive versions of classification systems is necessary to maintain the continuity of health care documentation. A reference terminology can serve as an intermediary to support this task. Within this study we evaluated the use of SNOMED CT to create a crossmap between two versions of an intensive care classification system. Firstly, the SNOMED CT crossmap was compared with an expert-based and a data-driven crossmap. Next, the influence of these crossmap strategies on the health care outcome was evaluated. For 50% of the analyzed cases, the three mapping strategies resulted in the same crossmaps. In other cases, there was an overlap between the SNOMED CT crossmaps and the crossmaps provided by one of the two other strategies. Differences in the crossmap results had however no significant influence on the health care outcomes. SNOMED CT can be used as an intermediary to solve the problem of crossmapping between versions of classification system

Determinants of mortality after hospital discharge in ICU patients: literature review and Dutch cohort study

First, to conduct a literature review on the long-term mortality of ICU patients and its determinants. Second, to assess the influence of the found determinants at 3, 6, and 12 months mortality after hospital discharge in the Dutch ICU population. Combination of a literature review to evaluate determinants of long-term mortality and a Dutch cohort study in which the found determinants are applied. PubMed and EMBASE were searched on English written articles published between 1966 and 2011. The cohort study was conducted in ICU patients from 81 Dutch mixed ICUs. A total of 24 articles with a main focus on describing or predicting the case-mix adjusted long-term mortality of the general ICU population were identified. The cohort study consisted of 48,107 ICU patients who were discharged alive from the hospital between January 1, 2007, and October 1, 2010. None. The included articles are summarized on patient and study characteristics, methods, results, and determinants used for case-mix adjustment. Additionally, the quality of the included articles was assessed using a checklist for studies on long-term survival. The median mortality rate of the general ICU population 1 year after ICU admission was 24% (range 16% to 44%). The determinants used for case-mix adjustment differed widely between the studies. In the cohort study, we found that age, reason for ICU admission, and comorbidities were associated with all long-term mortality endpoints. However, the magnitude and direction of the influence by these determinants differed for the different endpoints (i.e., 3, 6, and 12 mo after hospital discharge). The long-term mortality found in the included articles was difficult to compare due to low quality, variation in case-mix, study design, and differences in case-mix adjustment. The most commonly used determinants in the literature were comparable to the most important determinants found in the Dutch cohort stud

The impact of different prognostic models and their customization on institutional comparison of intensive care units

OBJECTIVES: To evaluate the influence of choice of a prognostic model and the effect of customization of these models on league tables (i.e., rank-order listing) in which intensive care units (ICUs) are ranked by standardized mortality ratios using Acute Physiology and Chronic Health Evaluation (APACHE) II, Simplified Acute Physiology Score (SAPS) II, and Mortality Probability Model II (MPM24II). DESIGN: Retrospective analysis of prospectively collected data on ICU admissions. SETTING: Forty Dutch ICUs. PATIENTS: A data set from a national registry of 86,427 patients from January 2002 to October 2006. INTERVENTIONS: The league tables associated with the different models were compared to evaluate their agreement. Bootstrapping was used to quantify the uncertainty in the ranks for ICUs. First, for each ICU the median rank and its 95% confidence interval were identified for each model. Then, for a given pair of models, for each ICU the median difference in rank and its associated 95% confidence interval were computed. A difference in rank for an ICU for a given pair of models was considered relevant if it was statistically significant and if one of the models would categorize this ICU as a performance outlier (excellent performer or very poor performer) while the other did not. MEASUREMENTS AND MAIN RESULTS: For 20 ICUs, there was a significant difference in rank (2-19 positions) between one or more pairs of models. Three ICUs were rated as performance outliers by one of the models, while the other excluded this possibility with 95% certainty. Furthermore, for ten ICUs, one or more pairs of models classified these ICUs as performance outliers while the other model did not do so with certainty. Regarding the agreement between the original models and their customized versions, in all cases the median change in rank was three positions or less and the models fully agreed with respect to which ICUs should be classified as performance outliers. CONCLUSIONS: Institutional comparison based on case-mix adjusted league tables is sensitive to the choice of prognostic model but not to customization of these models. League tables should always display the uncertainty associated with institutional rank

Lessons learnt during the implementation of a web-based triage tool for Dutch intensive care follow-up clinics

OBJECTIVES: Screening for symptoms of postintensive care syndrome is based on a long list of questionnaires, filled out by the intensive care unit (ICU) survivor and manually reviewed by the health professional. This is an inefficient and time-consuming process. The aim of this study was to evaluate the feasibility of a web-based triage tool and to compare the outcomes from web-based questionnaires to those from paper-based questionnaires

Reported burden on informal caregivers of ICU survivors: a literature review

Critical illness and the problems faced after ICU discharge do not only affect the patient, it also negatively impacts patients' informal caregivers. There is no review which summarizes all types of burden reported in informal caregivers of ICU survivors. It is important that the burdens these informal caregivers suffer are systematically assessed so the caregivers can receive the professional care they need. We aimed to provide a complete overview of the types of burdens reported in informal caregivers of adult ICU survivors, to make recommendations on which burdens should be assessed in this population, and which tools should be used to assess them. We performed a systematic search in PubMed and CINAHL from database inception until June 2014. All articles reporting on burdens in informal caregivers of adult ICU survivors were included. Two independent reviewers used a standardized form to extract characteristics of informal caregivers, types of burdens and instruments used to assess these burdens. The quality of the included studies was assessed using the Newcastle-Ottawa and the PEDro scales. The search yielded 2704 articles, of which we included 28 in our review. The most commonly reported outcomes were psychosocial burden. Six months after ICU discharge, the prevalence of anxiety was between 15% and 24%, depression between 4.7% and 36.4% and post-traumatic stress disorder (PTSD) between 35% and 57.1%. Loss of employment, financial burden, lifestyle interference and low health-related quality of life (HRQoL) were also frequently reported. The most commonly used tools were the Hospital Anxiety and Depression Scale (HADS), Centre for Epidemiological Studies-Depression questionnaire, and Impact of Event Scale (IES). The quality of observational studies was low and of randomized studies moderate to fair. Informal caregivers of ICU survivors suffer a substantial variety of burdens. Although the quality of the included studies was poor, there is evidence that burden in the psychosocial field is most prevalent. We suggest screening informal caregivers of ICU survivors for anxiety, depression, PTSD, and HRQoL using respectively the HADS, IES and Short Form 36 and recommend a follow-up period of at least 6 month