Performance of prognostic models in critically ill cancer patients – a review

INTRODUCTION: Prognostic models, such as the Acute Physiology and Chronic Health Evaluation (APACHE) II or III, the Simplified Acute Physiology Score (SAPS) II, and the Mortality Probability Models (MPM) II were developed to quantify the severity of illness and the likelihood of hospital survival for a general intensive care unit (ICU) population. Little is known about the performance of these models in specific populations, such as patients with cancer. Recently, specific prognostic models have been developed to predict mortality for cancer patients who are admitted to the ICU. The present analysis reviews the performance of general prognostic models and specific models for cancer patients to predict in-hospital mortality after ICU admission. METHODS: Studies were identified by searching the Medline databases from 1994 to 2004. We included studies evaluating the performance of mortality prediction models in critically ill cancer patients. RESULTS: Ten studies were identified that evaluated prognostic models in cancer patients. Discrimination between survivors and non-survivors was fair to good, but calibration was insufficient in most studies. General prognostic models uniformly underestimate the likelihood of hospital mortality in oncological patients. Two versions of a specific oncological scoring systems (Intensive Care Mortality Model (ICMM)) were evaluated in five studies and showed better discrimination and calibration than the general prognostic models. CONCLUSION: General prognostic models generally underestimate the risk of mortality in critically ill cancer patients. Both general prognostic models and specific oncology models may reliably identify subgroups of patients with a very high risk of mortality

Facilitating pre-operative assessment guidelines representation using SNOMED CT

Objective: To investigate whether SNOMED CT covers the terms used in pre-operative assessment guidelines, and if necessary, how the measured content coverage can be improved. Pre-operative assessment guidelines were retrieved from the websites of (inter)national anesthesiarelated societies. The recommendations in the guidelines were rewritten to ‘‘IF condition THEN action” statements to facilitate data extraction. Terms were extracted from the IF–THEN statements and mapped to SNOMED CT. Content coverage was measured by using three scores: no match, partial match and complete match. Non-covered concepts were evaluated against the SNOMED CT editorial documentation. Results: From 6 guidelines, 133 terms were extracted, of which 71% (n = 94) completely matched with SNOMED CT concepts. Disregarding the vague concepts in the included guidelines SNOMED CT’s content coverage was 89%. Of the 39 non-completely covered concepts, 69% violated at least one of SNOMED CT’s editorial principles or rules. These concepts were categorized based on four categories: non-reproducibility, classification-derived phrases, numeric ranges, and procedures categorized by complexity. Conclusion: Guidelines include vague terms that cannot be well supported by terminological systems thereby hampering guideline-based decision support systems. This vagueness reduces the content coverage of SNOMED CT in representing concepts used in the pre-operative assessment guidelines. Formalization of the guidelines using SNOMED CT is feasible but to optimize this, first the vagueness of some guideline concepts should be resolved and a few currently missing but relevant concepts should be added to SNOMED CT

The Use of SNOMED CT for Representing Concepts Used in Preoperative Guidelines

The use of guidelines to improve quality of care depends on presenting them in a standard machine-interpretable form and using common terms in guidelines as well as in patient records. In this study, the use of SNOMED CT for representing concepts used in preoperative assessment guidelines was evaluated. Terms used in six of these guidelines were mapped to this terminology. Mappings were presented based on three scores: no match, partial match, and complete match. As eleven of the terms were repeatedly used in different guidelines, we analyzed the results based on “token” and “type” coverage. Of 133 extracted terms from guidelines, 107 terms should be covered by SNOMED CT of which 87% was completely represented by this terminology. Our study showed that SNOMED CT content should be extended before preoperative assessment guidelines can be completely automated

Tight glycemic control and computerized decision-support systems: a systematic review

Objective: To identify and summarize characteristics of computerized decision-support systems (CDSS) for tight glycemic control (TGC) and to review their effects on the quality of the TGC process in critically ill patients. Methods: We searched Medline (1950-2008) and included studies on critically ill adult patients that reported original data from a clinical trial or observational study with a main objective of evaluating a given TGC protocol with a CDSS. Results: Seventeen articles met the inclusion criteria. Eleven out of seventeen studies evaluated the effect of a new TGC protocol that was introduced simultaneously with a CDSS implementation. Most of the reported CDSSs were stand-alone, were not integrated in any other clinical information systems and used the "passive'' mode requiring the clinician to ask for advice. Different implementation sites, target users, and time of advice were used, depending on local circumstances. All controlled studies reported on at least one quality indicator of the blood glucose regulatory process that was improved by introducing the CDSS. Nine out of ten controlled studies either did not report on the number of hypoglycemia events (one study), or reported on no change (six studies) or even a reduction in this number (two studies). Conclusions: While most studies evaluating the effect of CDSS on the quality of the TGC process found improvement when evaluated on the basis of the quality indicators used, it is impossible to define the exact success factors, because of simultaneous implementation of the CDSS with a new or modified TGC protocol and the hybrid solutions used to integrate the CDSS into the clinical workflo

Analyzing Differences in Operational Disease Definitions Using Ontological Modeling

In medicine, there are many diseases which cannot be precisely characterized but are considered as natural kinds. In the communication between health care professionals, this is generally not problematic. In biomedical research, however, crisp definitions are required to unambiguously distinguish patients with and without the disease. In practice, this results in different operational definitions being in use for a single disease. This paper presents an approach to compare different operational definitions of a single disease using ontological modeling. The approach is illustrated with a case-study in the area of severe sepsis

Forty years of SNOMED: a literature review

BACKGROUND: Over a period of 40 years, SNOMED has developed from a pathology-specific nomenclature (SNOP) into a logic-based health care terminology. In spite of its long existence and continuous evolvement, it is yet unknown to what extent SNOMED is used in clinical practice, and what benefits were achieved. The aim of this paper is to investigate this by providing an overview of published studies in which a version of SNOMED was studied or applied. METHODS: This paper analyzes the use of SNOMED over time, as reflected in scientific publications, by means of Medline literature search in PubMed. The search included papers from 1966 until June 2006. For each selected paper the following characteristics were classified: version, medical domain, coding moment (during or after the care process), usage, and type of evaluation (e.g., does SNOMED work, does SNOMED help). RESULTS: 250 papers were included in this research. The number of annual publications has increased, as has the number of domains in which SNOMED is being used. Theoretical studies mainly concern comparison of SNOMED to other terminological systems and SNOMED as an illustration of a terminological systems' theory. Few studies are available on the usage of SNOMED in clinical practice, largely involving coding information and retrieval/aggregation based on SNOMED codes. CONCLUSION: The clinical application of SNOMED is broadening beyond pathology. The majority of studies concern proving the value of SNOMED in theory. Fewer studies are available on the usage of SNOMED in clinical practice. Literature gives no indication of the use of SNOMED for direct care purposes such as decision suppor

Перспективи використання теорії катастроф у дослідженні економічних криз

OBJECTIVE: To assess in-hospital and long-term mortality of Dutch ICU patients admitted with an acute intoxication. DESIGN: Cohort of ICU admissions from a national ICU registry linked to records from an insurance claims database. SETTING: Eighty-one ICUs (85% of all Dutch ICUs). PATIENTS: Seven thousand three hundred thirty-one admissions between January 1, 2008, and October 1, 2011. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Kaplan-Meier curves were used to compare the unadjusted mortality of the total intoxicated population and for specific intoxication subgroups based on the Acute Physiology and Chronic Health Evaluation IV reasons for admission: 1) alcohol(s), 2) analgesics, 3) antidepressants, 4) street drugs, 5) sedatives, 6) poisoning (carbon monoxide, arsenic, or cyanide), 7) other toxins, and 8) combinations. The case-mix adjusted mortality was assessed by the odds ratio adjusted for age, gender, severity of illness, intubation status, recurrent intoxication, and several comorbidities. The ICU mortality was 1.2%, and the in-hospital mortality was 2.1%. The mortality 1, 3, 6, 12, and 24 months after ICU admission was 2.8%, 4.1%, 5.2%, 6.5%, and 9.3%, respectively. Street drugs had the highest mortality 2 years after ICU admission (12.3%); a combination of different intoxications had the lowest (6.3%). The adjusted observed mortality showed that intoxications with street drugs and "other toxins" have a significant higher mortality 1 month after ICU admission (odds ratio adj = 1.63 and odds ratioadj= 1.73, respectively). Intoxications with alcohol or antidepressants have a significant lower mortality 1 month after ICU admission (odds ratioadj = 0.50 and odds ratioadj = 0.46, respectively). These differences were not found in the adjusted mortality 3 months upward of ICU admission. CONCLUSIONS: Overall, the mortality 2 years after ICU admission is relatively low compared with other ICU admissions. The first 3 months after ICU admission there is a difference in mortality between the subgroups, not thereafter. Still, the difference between the in-hospital mortality and the mortality after 2 years is substantial

A systematic review on quality indicators for tight glycaemic control in critically ill patients: need for an unambiguous indicator reference subset

Introduction The objectives of this study were to systematically identify and summarize quality indicators of tight glycaemic control in critically ill patients, and to inspect the applicability of their definitions. Methods We searched in MEDLINE (R) for all studies evaluating a tight glycaemic control protocol and/or quality of glucose control that reported original data from a clinical trial or observational study on critically ill adult patients. Results Forty-nine studies met the inclusion criteria; 30 different indicators were extracted and categorized into four nonorthogonal categories: blood glucose zones (for example, 'hypoglycaemia'); blood glucose levels (for example, 'mean blood glucose level'); time intervals (for example, 'time to occurrence of an event'); and protocol characteristics (for example, 'blood glucose sampling frequency'). Hypoglycaemia-related indicators were used in 43 out of 49 studies, acting as a proxy for safety, but they employed many different definitions. Blood glucose level summaries were used in 41 out of 49 studies, reported as means and/or medians during the study period or at a certain time point (for example, the morning blood glucose level or blood glucose level upon starting insulin therapy). Time spent in the predefined blood glucose level range, time needed to reach the defined blood glucose level target, hyperglycaemia-related indicators and protocol-related indicators were other frequently used indicators. Most indicators differ in their definitions even when they are meant to measure the same underlying concept. More importantly, many definitions are not precise, prohibiting their applicability and hence the reproducibility and comparability of research results. Conclusions An unambiguous indicator reference subset is necessary. The result of this systematic review can be used as a starting point from which to develop a standard list of well defined indicators that are associated with clinical outcomes or that concur with clinicians' subjective views on the quality of the regulatory proces

Training in data definitions improves quality of intensive care data

BACKGROUND: Our aim was to assess the contribution of training in data definitions and data extraction guidelines to improving quality of data for use in intensive care scoring systems such as the Acute Physiology and Chronic Health Evaluation (APACHE) II and Simplified Acute Physiology Score (SAPS) II in the Dutch National Intensive Care Evaluation (NICE) registry. METHODS: Before and after attending a central training programme, a training group of 31 intensive care physicians from Dutch hospitals who were newly participating in the NICE registry extracted data from three sample patient records. The 5-hour training programme provided participants with guidelines for data extraction and strict data definitions. A control group of 10 intensive care physicians, who were trained according the to train-the-trainer principle at least 6 months before the study, extracted the data twice, without specific training in between. RESULTS: In the training group the mean percentage of accurate data increased significantly after training for all NICE variables (+7%, 95% confidence interval 5%–10%), for APACHE II variables (+6%, 95% confidence interval 4%–9%) and for SAPS II variables (+4%, 95% confidence interval 1%–6%). The percentage data error due to nonadherence to data definitions decreased by 3.5% after training. Deviations from 'gold standard' SAPS II scores and predicted mortalities decreased significantly after training. Data accuracy in the control group did not change between the two data extractions and was equal to post-training data accuracy in the training group. CONCLUSION: Training in data definitions and data extraction guidelines is an effective way to improve quality of intensive care scoring data