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.Stephanie Medlock, Anita C. J. Ravelli, Pieter Tamminga, Ben W. M. Mol, Ameen Abu-Hann

Health Information-Seeking Behavior of Seniors Who Use the Internet:A Survey

Background: The Internet is viewed as an important source for health information and a medium for patient empowerment. However, little is known about how seniors use the Internet in relation to other sources for health information.Objective: The aim was to determine which information resources seniors who use the Internet use and trust for health information, which sources are preferred, and which sources are used by seniors for different information needs.Methods: Questions from published surveys were selected based on their relevance to the study objectives. The Autonomy Preference Index was used to assess information needs and preferences for involvement in health decisions. Invitation to participate in this online survey was sent to the email list of a local senior organization (298 addresses) in the Netherlands.Results: There were 118 respondents with a median age of 72 years (IQR 67-78 years). Health professionals, pharmacists, and the Internet were the most commonly used and trusted sources of health information. Leaflets, television, newspapers, and health magazines were also important sources. Respondents who reported higher use of the Internet also reported higher use of other sources (PConclusions: For these seniors who use the Internet, the Internet was a preferred source of health information. Seniors who report higher use of the Internet also report higher use of other information resources and were also the primary consumers of paper-based resources. Respondents most frequently searched for health information after an appointment rather than to prepare for an appointment. Resources used varied by health topic. Future research should seek to confirm these findings in a general elderly population, investigate how seniors seek and understand information on the Internet, and investigate how to reach seniors who prefer not to use the Internet for health information.</p

Validation of the ADFICE_IT Models for Predicting Falls and Recurrent Falls in Geriatric Outpatients

Objectives: Before being used in clinical practice, a prediction model should be tested in patients whose data were not used in model development. Previously, we developed the ADFICE_IT models for predicting any fall and recurrent falls, referred as Any_fall and Recur_fall. In this study, we externally validated the models and compared their clinical value to a practical screening strategy where patients are screened for falls history alone. Design: Retrospective, combined analysis of 2 prospective cohorts. Setting and Participants: Data were included of 1125 patients (aged ≥65 years) who visited the geriatrics department or the emergency department. Methods: We evaluated the models' discrimination using the C-statistic. Models were updated using logistic regression if calibration intercept or slope values deviated significantly from their ideal values. Decision curve analysis was applied to compare the models’ clinical value (ie, net benefit) against that of falls history for different decision thresholds. Results: During the 1-year follow-up, 428 participants (42.7%) endured 1 or more falls, and 224 participants (23.1%) endured a recurrent fall (≥2 falls). C-statistic values were 0.66 (95% CI 0.63-0.69) and 0.69 (95% CI 0.65-0.72) for the Any_fall and Recur_fall models, respectively. Any_fall overestimated the fall risk and we therefore updated only its intercept whereas Recur_fall showed good calibration and required no update. Compared with falls history, Any_fall and Recur_fall showed greater net benefit for decision thresholds of 35% to 60% and 15% to 45%, respectively.Conclusions and Implications: The models performed similarly in this data set of geriatric outpatients as in the development sample. This suggests that fall-risk assessment tools that were developed in community-dwelling older adults may perform well in geriatric outpatients. We found that in geriatric outpatients the models have greater clinical value across a wide range of decision thresholds compared with screening for falls history alone.</p

Assessing Quality of Care of Elderly Patients Using the ACOVE Quality Indicator Set: A Systematic Review

Background: Care of the elderly is recognized as an increasingly important segment of health care. The Assessing Care Of Vulnerable Elderly (ACOVE) quality indicators (QIs) were developed to assess and improve the care of elderly patients. Objectives: The purpose of this review is to summarize studies that assess the quality of care using QIs from or based on ACOVE, in order to evaluate the state of quality of care for the reported conditions. Methods: We systematically searched MEDLINE, EMBASE and CINAHL for English-language studies indexed by February 2010. Articles were included if they used any ACOVE QIs, or adaptations thereof, for assessing the quality of care. Included studies were analyzed and relevant information was extracted. We summarized the results of these studies, and when possible generated an overall conclusion about the quality of care as measured by ACOVE for each condition, in various settings, and for each QI. Results: Seventeen studies were included with 278 QIs (original, adapted or newly developed). The quality scores showed large variation between and within conditions. Only a few conditions showed a stable pass rate range over multiple studies. Overall, pass rates for dementia (interquartile range (IQR): 11%-35%), depression (IQR: 27%-41%), osteoporosis (IQR: 34%-43%) and osteoarthritis (IQR: 29-41%) were notably low. Medication management and use (range: 81%-90%), hearing loss (77%-79%) and continuity of care (76%-80%) scored higher than other conditions. Out of the 278 QIs, 141 (50%) had mean pass rates below 50% and 121 QIs (44%) had pass rates above 50%. Twenty-three percent of the QIs scored above 75%, and 16% scored below 25%. Conclusions: Quality of care per condition varies markedly across studies. Although there has been much effort in improving the care for elderly patients in the last years, the reported quality of care according to the ACOVE indicators is still relatively lo

The GUIDES checklist: Development of a tool to improve the successful use of guideline-based computerised clinical decision support

Background: Computerised decision support (CDS) based on trustworthy clinical guidelines is a key component of a learning healthcare system. Research shows that the effectiveness of CDS is mixed. Multifaceted context, system, recommendation and implementation factors may potentially affect the success of CDS interventions. This paper describes the development of a checklist that is intended to support professionals to implement CDS successfully. Methods: We developed the checklist through an iterative process that involved a systematic review of evidence and frameworks, a synthesis of the success factors identified in the review, feedback from an international expert panel that evaluated the checklist in relation to a list of desirable framework attributes, consultations with patients and healthcare consumers and pilot testing of the checklist. Results: We screened 5347 papers and selected 71 papers with relevant information on success factors for guideline-based CDS. From the selected papers, we developed a 16-factor checklist that is divided in four domains, i.e. the CDS context, content, system and implementation domains. The panel of experts evaluated the checklist positively as an instrument that could support people implementing guideline-based CDS across a wide range of settings globally. Patients and healthcare consumers identified guideline-based CDS as an important quality improvement intervention and perceived the GUIDES checklist as a suitable and useful strategy. Conclusions: The GUIDES checklist can support professionals in considering the factors that affect the success of CDS interventions. It may facilitate a deeper and more accurate understanding of the factors shaping CDS effectiveness. Relying on a structured approach may prevent that important factors are missed

Health Behaviour Theory in Health Informatics: Support for Positive Change

The rising use of the Internet and information technology has made computerized interventions an attractive channel for providing advice and support for behaviour change. Health behaviour and behaviour change theories are a family of theories which aim to explain the mechanisms by which human behaviours change and use that knowledge to promote change. Among the best-known of these theories are the Social Learning and Social Cognitive theories, the Health Belief Model, the Theory of Reasoned Action and its successors the Theory of Planned Behaviour and the Reasoned Action Approach, and the Transtheoretical model. We discuss three examples of how behaviour change theories have been applied in computer-based interventions: a system to aid users to quit smoking, a decision aid for choice of breast cancer therapy, and an internet-based exercise program for reducing cardiovascular risk. We also discuss misapplication of theory, and reflect on how these theories can best be used. Behaviour change theory can be applied in health informatics interventions in several ways; for example, to select participants for a particular intervention, to shape the content of the intervention to effectively influence behaviour, or to tailor content to individual needs. Application of these theories to provide personalized advice ('decision support') is a young but promising area of research, and could inform other decision support interventions, including those that provide support for clinicians

Implementation of an Open-Source Electronic Health Record for Decision-Support Education in Medical Informatics

Access to electronic medical record systems is limited in many medical informatics education programs. The objective of this study was to inventory open-source patient record systems with decision support capabilities, implement a system for educational use, and test the effect of the system on students' learning. We sought systems that were under active development, with source code available, having an SQL-queryable database, and having decision support capabilities. We identified 20 candidate electronic health record systems, of which 6 mentioned decision support capabilities in their documentation. Of these, the OpenMRS system appeared to meet all of the requirements for use in our course; however, decision support capabilities needed to be added by use of a custom module implementing Arden2Bytecode, an Arden Syntax interpreter. Students who used this system showed an improvement in their knowledge of decision support systems and their capabilities. We conclude that there are a number of promising open-source electronic patient record systems currently under active development, but decision support capabilities are still immature. We anticipate further developments in this area in the coming years