Ameen Abu-Hanna

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

IntroductionTo 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).MethodsMedline, 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.ResultsEighteen 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.ConclusionsModels 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 accuracy.

The impact of computerized physician medication order entry in hospitalized patients--a systematic review.

OBJECTIVE To identify all published studies evaluating computerized physician order entry (CPOE) in the inpatient setting and uniformly classify these studies on outcome measure and study design. DATA SOURCES All studies that evaluated the effect of CPOE on outcomes pertaining to the medication process in inpatients were electronically searched in MEDLINE (1966 to August 2006), EMBASE (1980 to August 2006) and the Cochrane library. In addition, the bibliographies of retrieved articles were manually searched. Articles were selected if one of their main objectives was CPOE evaluation in an inpatient setting. REVIEW METHOD Identified titles and abstracts were independently screened by three reviewers to determine eligibility for further review. RESULTS We found 67 articles, which included articles on CPOE evaluation on some outcome at the time of ordering. Most papers evaluated multiple outcome measures. The outcome measures were clustered in the following categories: adherence (n=22); alerts and appropriateness of alerts (n=7); safety (n=21); time (n=7); costs and (organizational) efficiency (n=23); and satisfaction, usage and usability (n=10). Most studies used a before-after design (n=35) followed by observational studies (n=24) and randomized controlled trials (n=8). CONCLUSION The impact of CPOE systems was especially positive in the category adherence to guidelines, but also to some extent in alerts and appropriateness of alerts; costs and organizational efficiency; and satisfaction and usability. Although on average, there seems to be a positive effect of CPOE on safety, studies tended to be non-randomized and were focused on medication error rates, not powered to detect a difference in adverse drug event rates. Some recent studies suggested that errors, adverse drug events (ADEs) and even mortality increased after CPOE implementation. Only in the category time the impact has been shown to be negative, but this only refers to the physicians time, not the net time. Except for safety, on the whole spectrum of outcomes, results of RCT studies were in line with non-RCT study results.

Evaluation of Outpatient Computerized Physician Medication Order Entry Systems: A Systematic Review

This paper provides a systematic literature review of CPOE evaluation studies in the outpatient setting on: safety; cost and efficiency; adherence to guideline; alerts; time; and satisfaction, usage, and usability. Thirty articles with original data (randomized clinical trial, non-randomized clinical trial, or observational study designs) met the inclusion criteria. Only four studies assessed the effect of CPOE on safety. The effect was not significant on the number of adverse drug events. Only one study showed a significant reduction of the number of medication errors. Three studies showed significant reductions in medication costs; five other studies could not support this. Most studies on adherence to guidelines showed a significant positive effect. The relatively small number of evaluation studies published to date do not provide adequate evidence that CPOE systems enhance safety and reduce cost in the outpatient settings. There is however evidence for (a) increasing adherence to guidelines, (b) increasing total prescribing time, and (c) high frequency of ignored alerts.

Geriatric Conditions in Acutely Hospitalized Older Patients: Prevalence and One-Year Survival and Functional Decline

Background To study the prevalence of eighteen geriatric conditions in older patients at admission, their reporting rate in discharge summaries and the impact of these conditions on mortality and functional decline one year after admission. Method A prospective multicenter cohort study conducted between 2006 and 2008 in two tertiary university teaching hospitals and one regional teaching hospital in the Netherlands. Patients of 65 years and older, acutely admitted and hospitalized for at least 48 hours, were invited to participate. Eighteen geriatric conditions were assessed at hospital admission, and outcomes (mortality, functional decline) were assessed one year after admission. Results 639 patients were included, with a mean age of 78 years. IADL impairment (83%), polypharmacy (61%), mobility difficulty (59%), high levels of primary caregiver burden (53%), and malnutrition (52%) were most prevalent. Except for polypharmacy and cognitive impairment, the reporting rate of the geriatric conditions in discharge summaries was less than 50%. One year after admission, 35% had died and 33% suffered from functional decline. A high Charlson comorbidity index score, presence of malnutrition, high fall risk, presence of delirium and premorbid IADL impairment were associated with mortality and overall poor outcome (mortality or functional decline). Obesity lowered the risk for mortality. Conclusion Geriatric conditions were highly prevalent and associated with poor health outcomes after admission. Early recognition of these conditions in acutely hospitalized older patients and improving the handover to the general practitioner could lead to better health outcomes and reduce the burden of hospital admission for older patients.

Prediction of Mortality in Very Premature Infants: A Systematic Review of Prediction Models

Context Being born very preterm is associated with elevated risk for neonatal mortality. The aim of this review is to give an overview of prediction models for mortality in very premature infants, assess their quality, identify important predictor variables, and provide recommendations for development of future models. Methods Studies were included which reported the predictive performance of a model for mortality in a very preterm or very low birth weight population, and classified as development, validation, or impact studies. For each development study, we recorded the population, variables, aim, predictive performance of the model, and the number of times each model had been validated. Reporting quality criteria and minimum methodological criteria were established and assessed for development studies. Results We identified 41 development studies and 18 validation studies. In addition to gestational age and birth weight, eight variables frequently predicted survival: being of average size for gestational age, female gender, non-white ethnicity, absence of serious congenital malformations, use of antenatal steroids, higher 5-minute Apgar score, normal temperature on admission, and better respiratory status. Twelve studies met our methodological criteria, three of which have been externally validated. Low reporting scores were seen in reporting of performance measures, internal and external validation, and handling of missing data. Conclusions Multivariate models can predict mortality better than birth weight or gestational age alone in very preterm infants. There are validated prediction models for classification and case-mix adjustment. Additional research is needed in validation and impact studies of existing models, and in prediction of mortality in the clinically important subgroup of infants where age and weight alone give only an equivocal prognosis.

A systematic review on prognostic indicators of acute on chronic liver failure and their predictive value for mortality.

An early and proper diagnosis of acute on chronic liver failure (ACLF), together with the identification of indicators associated with disease severity is critical for outcome prediction and therapy.

Glucose variability measures and their effect on mortality: a systematic review

ObjectiveTo systematically review the medical literature on the association between glucose variability measures and mortality in critically ill patients.MethodsStudies assessing the association between a measure of glucose variability and mortality that reported original data from a clinical trial or observational study on critically ill adult patients were searched in Ovid MEDLINE® and Ovid EMBASE®. Data on patient populations, study designs, glucose regulations, statistical approaches, outcome measures, and glucose variability indicators (their definition and applicability) were extracted.ResultTwelve studies met the inclusion criteria; 13 different indicators were used to measure glucose variability. Standard deviation and the presence of both hypo- and hyperglycemia were the most common indicators. All studies reported a statistically significant association between mortality and at least one glucose variability indicator. In four studies both blood glucose levels and severity of illness were considered as confounders, but only one of them checked model assumptions to assert inference validity.ConclusionsGlucose variability has been quantified in many different ways, and in each study at least one of them appeared to be associated with mortality. Because of methodological limitations and the possibility of reporting bias, it is still unsettled whether and in which quantification this association is independent of other confounders. Future research will benefit from using an indicator reference subset for glucose variability, metrics that are linked more directly to negative physiological effects, more methodological rigor, and/or better reporting.

A comparison of RIFLE with and without urine output criteria for acute kidney injury in critically ill patients.

IntroductionThe Risk, Injury, Failure, Loss, and End-Stage Renal Disease (RIFLE) is a consensus-based classification system for diagnosing acute kidney insufficiency (AKI), based on serum creatinine (SCr) and urine output criteria (RIFLESCr+UO). The urine output criteria, however, are frequently discarded and many studies in the literature applied only the SCr criteria (RIFLESCr). We diagnosed AKI using both RIFLE methods and compared the effects on time to AKI diagnosis, AKI incidence and AKI severity.MethodsThis was a prospective observational cohort study during four months in adult critically ill patients admitted to the ICU for at least 48 hours. During the first week patients were scored daily for AKI according to RIFLESCr+UO and RIFLESCr. We assessed urine output hourly and fluid balance daily. The baseline SCr was estimated if a recent pre-ICU admission SCr was unknown. Based on the two RIFLE methods for each patient we determined time to AKI diagnosis (AKI-0) and maximum RIFLE grade.ResultsWe studied 260 patients. A pre-ICU admission SCr was available in 101 (39%) patients. The two RIFLE methods resulted in statistically significantly different outcomes for incidence of AKI, diagnosis of AKI for individual patients, distribution of AKI-0 and distribution of the maximum RIFLE grade. Discarding the RIFLE urine criteria for AKI diagnosis significantly underestimated the presence and grade of AKI on admission and during the first ICU week (P < 0,001) and significantly delayed the diagnosis of AKI (P < 0.001). Based on RIFLESCr 45 patients had no AKI on admission but subsequently developed AKI. In 24 of these patients (53%) AKI would have been diagnosed at least one day earlier if the RIFLE urine criteria had been applied. Mortality rate in the AKI population was 38% based on RIFLESCr and 24% based on RIFLESCr+UO (P = 0.02).ConclusionsThe use of RIFLE without the urine criteria significantly underscores the incidence and grade of AKI, significantly delays the diagnosis of AKI and is associated with higher mortality.

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

Please cite this paper as: Ravelli A, Jager K, de Groot M, Erwich J, Rijninks‐van Driel G, Tromp M, Eskes M, Abu‐Hanna A, Mol B. Travel time from home to hospital and adverse perinatal outcomes in women at term in the Netherlands. BJOG 2011;118:457–465.

Prognostic methods in medicine

Prognosis--the prediction of the course and outcome of disease processes--plays an important role in patient management tasks like diagnosis and treatment planning. As a result, prognostic models form an integral part of a number of systems supporting these tasks. Furthermore, prognostic models constitute instruments to evaluate the quality of health care and the consequences of health care policies by comparing predictions according to care norms with actual results. Approaches to developing prognostic models vary from using traditional probabilistic techniques, originating from the field of statistics, to more qualitative and model-based techniques, originating from the field of artificial intelligence (AI). In this paper, various approaches to constructing prognostic models, with emphasis on methods from the field of AI, are described and compared.