Patients with cancer on the ICU: the times they are changing

A recent paper by Taccone and coworkers showed that 15% of patients from 198 European intensive care units (ICUs) had a malignancy, mostly solid tumors but also hematological malignancies. Over the past years, the prognosis of cancer patients has improved significantly, even when ICU admission is necessary. Refusal of ICU admission should not be based on a diagnosis of cancer as the underlying condition. In contrast, these decisions should be based on the availability of treatment options, and on patients' own preferences

Evaluation of SOFA-based models for predicting mortality in the ICU: A systematic review

Introduction To systematically review studies evaluating the performance of Sequential Organ Failure Assessment ( SOFA)based models for predicting mortality in patients in the intensive care unit (ICU). Methods Medline, EMBASE and other databases were searched for English-language articles with the major objective of evaluating the prognostic performance of SOFA-based models in predicting mortality in surgical and/or medical ICU admissions. The quality of each study was assessed based on a quality framework for prognostic models. Results Eighteen articles met all inclusion criteria. The studies differed widely in the SOFA derivatives used and in their methods of evaluation. Ten studies reported about developing a probabilistic prognostic model, only five of which used an independent validation data set. The other studies used the SOFA-based score directly to discriminate between survivors and non-survivors without fitting a probabilistic model. In five of the six studies, admission-based models ( Acute Physiology and Chronic Health Evaluation (APACHE) II/III) were reported to have a slightly better discrimination ability than SOFA-based models at admission ( the receiver operating characteristic curve (AUC) of SOFA-based models ranged between 0.61 and 0.88), and in one study a SOFA model had higher AUC than the Simplified Acute Physiology Score (SAPS) II model. Four of these studies used the Hosmer-Lemeshow tests for calibration, none of which reported a lack of fit for the SOFA models. Models based on sequential SOFA scores were described in 11 studies including maximum SOFA scores and maximum sum of individual components of the SOFA score ( AUC range: 0.69 to 0.92) and delta SOFA ( AUC range: 0.51 to 0.83). Studies comparing SOFA with other organ failure scores did not consistently show superiority of one scoring system to another. Four studies combined SOFA-based derivatives with admission severity of illness scores, and they all reported on improved predictions for the combination. Quality of studies ranged from 11.5 to 19.5 points on a 20-point scale. Conclusions Models based on SOFA scores at admission had only slightly worse performance than APACHE II/III and were competitive with SAPS II models in predicting mortality in patients in the general medical and/or surgical ICU. Models with sequential SOFA scores seem to have a comparable performance with other organ failure scores. The combination of sequential SOFA derivatives with APACHE II/III and SAPS II models clearly improved prognostic performance of either model alone. Due to the heterogeneity of the studies, it is impossible to draw general conclusions on the optimal mathematical model and optimal derivatives of SOFA scores. Future studies should use a standard evaluation methodology with a standard set of outcome measures covering discrimination, calibration and accurac

Selective decontamination of the digestive tract reduces mortality in critically ill patients

Several emotional responses may be invoked in critical care physicians when confronted with selective decontamination of the digestive tract (SDD). Although recent meta-analyses have shown that the use of SDD reduces the occurrence of ventilator-associated pneumonia and improves ICU survival, the effectiveness of SDD has remained controversial. We recently concluded a large randomized, controlled trial on the use of SDD that showed improved survival of ICU patients treated with SDD. A second concern regarding use of SDD has been the fear for the emergence of antimicrobial resistance. Interestingly, a recently published study did not confirm this fear, and our recently finished study even demonstrated a decline in colonization with P. aeruginosa and enterobacteriaceae that were resistant against tobramycin, ceftazidime, imipenem and ciprofloxacin. The hopes are that this study will at long last end the debate about the efficacy and safety of SDD in critically ill patients

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

Factors that predict outcome of intensive care treatment in very elderly patients: a review

INTRODUCTION: Advanced age is thought to be associated with increased mortality in critically ill patients. This report reviews available data on factors that determine outcome, on the value of prognostic models, and on preferences regarding life-sustaining treatments in (very) elderly intensive care unit (ICU) patients. METHODS: We searched the Medline database (January 1966 to January 2005) for English language articles. Selected articles were cross-checked for other relevant publications. RESULTS: Mortality rates are higher in elderly ICU patients than in younger patients. However, it is not age per se but associated factors, such as severity of illness and premorbid functional status, that appear to be responsible for the poorer prognosis. Patients' preferences regarding life-sustaining treatments are importantly influenced by the likelihood of a beneficial outcome. Commonly used prognostic models have not been calibrated for use in the very elderly. Furthermore, they do not address long-term survival and functional outcome. CONCLUSION: We advocate the development of new prognostic models, validated in elderly ICU patients, that predict not only survival but also functional and cognitive status after discharge. Such a model may support informed decision making with respect to patients' preferences

Identification of high-risk subgroups in very elderly intensive care unit patients

INTRODUCTION: Current prognostic models for intensive care unit (ICU) patients have not been specifically developed or validated in the very elderly. The aim of this study was to develop a prognostic model for ICU patients 80 years old or older to predict in-hospital mortality by means of data obtained within 24 hours after ICU admission. Aside from having good overall performance, the model was designed to reliably and specifically identify subgroups at very high risk of dying. METHODS: A total of 6,867 consecutive patients 80 years old or older from 21 Dutch ICUs were studied. Data necessary to calculate the Glasgow Coma Scale, Acute Physiology and Chronic Health Evaluation II, Simplified Acute Physiology Score II (SAPS II), Mortality Probability Models II scores, and ICU and hospital survival were recorded. Data were randomly divided into a developmental (n = 4,587) and a validation (n = 2,289) set. By means of recursive partitioning analysis, a classification tree predicting in-hospital mortality was developed. This model was compared with the original SAPS II model and with the SAPS II model after recalibration for very elderly ICU patients in the Netherlands. RESULTS: Overall performance measured by the area under the receiver operating characteristic curve and by the Brier score was similar for the classification tree, the original SAPS II model, and the recalibrated SAPS II model. The tree identified most patients with very high risk of mortality (9.2% of patients versus 8.9% for the original SAPS II and 5.9% for the recalibrated SAPS II had a risk of more than 80%). With a cut-point at a risk of 80%, the positive predictive values were 0.88 for the tree, 0.83 for the original SAPS II, and 0.87 for the recalibrated SAPS II. CONCLUSION: Prognostic models with good overall performance may also reliably identify subgroups of very elderly ICU patients who have a very high risk of dying before hospital discharge. The classification tree has the advantage of identifying the separate factors contributing to bad outcome and of using few variables. Up to 9.5% of patients were found to have a risk to die of more than 85

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

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

Risk factors and outcomes after unplanned extubations on the ICU: a case-control study

Diabetes mellitus: pathophysiological changes and therap