An influence diagram for assessing GVHD prophylaxis after bone marrow transplantation in children

A Graphical Decision-Theoretic Model for Neonatal Jaundice

Background. Neonatal jaundice is treated daily at all hospitals. However, the routine, urgency, and case load of most doctors stop them from carefully analyzing all the factors that they would like to (and should) take into account. This article develops a complex decision support system for neonatal jaundice management. Methods. The problem is represented by means of an influence diagram, including admission and treatment decisions. The corresponding uncertainty model is built with the aid of both historical data and subjective judgments. Parents and doctors were interviewed to elicit a multiattribute utility function. The decision analysis cycle is completed with sensitivity analyses and explanations of the results. Results. The construction and use of this decision support system for jaundice management have induced a profound change in daily medical practice, avoiding aggressive treatments—there have been no exchange transfusions in the past 3 years—and reducing the lengths of stay at the hospital. More information is now taken into account to decide on treatments. Interestingly, after embarking on this modeling effort, physicians came to view jaundice as a much more difficult problem than they had initially thought. Comparisons between real cases and system proposals revealed that treatments by nonexpert doctors tend to be longer than what expert doctors would administer. Conclusion. The system is especially designed to help neonatologists in situations in which their lack of experience may lead to unnecessary treatments. Different points of view from several expert doctors and, more interestingly, from parents are taken into account. This knowledge gives a broader picture of the medical problem— incorporating new action criteria, new agents to intervene, more uncertainty variables—to get an insight into the suitability of each therapeutic decision for each patient situation. The benefits gained and the usefulness perceived by neonatologists are worth the increased and time-consuming effort of developing this complex system. Although specially designed for a specific hospital and for neonatal jaundice management, it can be easily adapted to other hospitals and problems.

Cost-effectiveness of preoperative imaging for appendicitis after indeterminate ultrasonography in the second or third trimester of pregnancy

OBJECTIVE

To assess the cost-effectiveness of diagnostic laparoscopy, computed tomography (CT), and magnetic resonance imaging (MRI) after indeterminate ultrasonography in pregnant women with suspected appendicitis.

METHODS

A decision-analytic model was developed to simulate appendicitis during pregnancy taking into consideration the health outcomes for both the pregnant women and developing fetuses. Strategies included diagnostic laparoscopy, CT, and MRI. Outcomes included positive appendectomy, negative appendectomy, maternal perioperative complications, preterm delivery, fetal loss, childhood cancer, lifetime costs, discounted life expectancy, and incremental cost-effectiveness ratios.

RESULTS

Magnetic resonance imaging is the most cost-effective strategy, costing $6,767 per quality-adjusted life-year gained relative to CT, well below the generally accepted $50,000 per quality-adjusted life-year threshold. In a setting where MRI is unavailable, CT is cost-effective even when considering the increased risk of radiation-associated childhood cancer ($560 per quality-adjusted life-year gained relative to diagnostic laparoscopy). Unless the negative appendectomy rate is less than 1%, imaging of any type is more cost-effective than proceeding directly to diagnostic laparoscopy.

CONCLUSIONS

Depending on imaging costs and resource availability, both CT and MRI are potentially cost-effective. The risk of radiation-associated childhood cancer from CT has little effect on population-level outcomes or cost-effectiveness but is a concern for individual patients. For pregnant women with suspected appendicitis, an extremely high level of clinical diagnostic certainty must be reached before proceeding to operation without preoperative imaging.

Statement of current majority practices in graft-versus-host disease prophylaxis and treatment in children

Great variations exist in the prophylaxis and treatment of GVHD in children undergoing allogeneic stem cell transplantation (SCT). The EBMT Working Party Paediatric Diseases (EBMT-WP PD) and the International BFM Study Group--Subcommittee Bone Marrow Transplantation (IBFM-SG), aimed at evaluating current local standards in the prevention and treatment of GVHD and steps which can be taken to achieve a uniform policy for the individual methods. Several conferences with their members assessed practices which are mainly applied or under investigation in children and identified where additional information is needed. For prevention of GVHD, the majority of the paediatric centres prefer CsA +/- MTX. Addition of folinic acid to MTX was considered for reduction of side-effects. During treatment of acute GVHD most centres administer prednisolone and whole blood level-adjusted CsA as medications of first choice. In cases of poor or no response to this therapy, additional immunosuppressive agents such as ATG, mycophenolate-mofetile and tacrolimus are being increasingly used. The treatment of chronic GVHD usually consists of various combinations of prednisolone and CsA. In severe cases, extracorporeal photopheresis, psoralene-UVA (PUVA) and thalidomide are administered.

Learning temporal probabilistic causal models from longitudinal data

Medical problems often require the analysis and interpretation of large collections of longitudinal data in terms of a structural model of the underlying physiological behavior. A suitable way to deal with this problem is to identify a temporal causal model that may effectively explain the patterns observed in the data. Here we will concentrate on probabilistic models, that provide a convenient framework to represent and manage underspecified information; in particular, we will consider the class of Causal Probabilistic Networks (CPN). We propose a method to perform structural learning of CPNs representing time-series through model selection. Starting from a set of plausible causal structures and a collection of possibly incomplete longitudinal data, we apply a learning algorithm to extract from the data the conditional probabilities describing each model. The models are then ranked according to their performance in reconstructing the original time-series, using several scoring functions, based on one-step ahead predictions. In this paper we describe the proposed methodology through an example taken from the diabetes monitoring domain. The selection process is applied to a set of input-output models that generalize the class of ARX models, where the inputs are the insulin and meal intakes and the outputs are the blood glucose levels. Although the physiological process underlying this particular application is characterized by strong non-linearities and low data reliability, we show that it is possible to obtain meaningful results, in terms of conditional probability learning and model ranking power.

Reusable influence diagrams

Influence Diagrams have been recognized as a suitable formalism for building probabilistic expert systems. Nevertheless, the most part of applications consists in stand-alone systems, concerning a very limited domain. On the other hand, Artificial Intelligence research has outlined Blackboard Architectures as the basis for building expert systems in which several knowledge sources, in general built with different formalisms, cooperate to the solution of a complex task. This paper addresses the use of influence diagrams as knowledge sources of such a system, and particularly faces the problem of reusing the same influence diagram in different inference phases. We will show that, specially in planning tasks, the modularity requirement of keeping the knowledge sources separated, may imply that an influence diagram must call another influence diagram to solve itself and to maintain the coherence of the whole set of decisions underlying the plan. Conditions for the correctness of this concatenation of knowledge sources will be provided, and an example from the medical domain of therapy planning for Acute Myeloid Leukemia will be shown, as an implemented prototype exploiting these ideas.