Combining data and meta-analysis to build Bayesian networks for clinical decision support.

Complex clinical decisions require the decision maker to evaluate multiple factors that may interact with each other. Many clinical studies, however, report 'univariate' relations between a single factor and outcome. Such univariate statistics are often insufficient to provide useful support for complex clinical decisions even when they are pooled using meta-analysis. More useful decision support could be provided by evidence-based models that take the interaction between factors into account. In this paper, we propose a method of integrating the univariate results of a meta-analysis with a clinical dataset and expert knowledge to construct multivariate Bayesian network (BN) models. The technique reduces the size of the dataset needed to learn the parameters of a model of a given complexity. Supplementing the data with the meta-analysis results avoids the need to either simplify the model - ignoring some complexities of the problem - or to gather more data. The method is illustrated by a clinical case study into the prediction of the viability of severely injured lower extremities. The case study illustrates the advantages of integrating combined evidence into BN development: the BN developed using our method outperformed four different data-driven structure learning methods, and a well-known scoring model (MESS) in this domain.

Predicting Length of Stay in Hospital Using Electronic Records Available at the Time of Admission.

Predicting a patient's hospital length of stay (LoS) can help manage staffing. In this paper, we explore LoS prediction for a large group of patients admitted non-electively. We use information available at admission, including demographics, acute and long-term diagnoses and physiological tests results. Data were extracted from the electronic health records (EHR), so that the LoS prediction would not require additional data entry. Although the data can be accessed, the system does not present a unified view of the data for one patient: to resolve this we designed a process of cleaning and combining data for each patient. The data was used to fit semi-parametric, parametric and competing outcomes survival models. All models performed similarly, with concordance of approximately 0.7. Calibration results showed underestimation of predicted discharges for patients with high discharge probabilities and overestimation of predicted discharges for those with low discharge probabilities. The main challenges in operationalizing LoS predictions are delays in entering admissions data into EHR and absent data about non-medical factors determining discharges.

Long-term, patient-centered outcomes of lower-extremity vascular trauma.

OBJECTIVE: To describe the long-term outcomes of military lower-extremity vascular injuries, and the decision making of surgeons treating these injuries. BACKGROUND: Lower-extremity vascular trauma is an important cause of preventable death and severe disability, and decisions on amputation or limb salvage can be difficult. Additionally, the complexity of the condition is not amenable to controlled study, and there is limited data to guide clinical decision making and establish sensible treatment expectations during rehabilitation. METHODS: A cohort study of 554 US service members who sustained lower-extremity vascular injury in Iraq or Afghanistan (March 2003 to February 2012) was performed using the military's trauma registry, its electronic health record, patient interviews, and quality-of-life surveys. Long-term surgical and functional outcomes, and the timing and rationale of surgical decisions, were analyzed. RESULTS: Of 579 injured extremities, 49 (8.5%) underwent primary amputation and 530 (91.5%) an initial attempt at salvage. Ninety extremities underwent secondary amputation, occurring in the early (n = 60; <30 days) or late (n = 30; >30 days) phases after injury. For salvage attempts, freedom from amputation 10 years after injury was 82.7% (79.1%-85.7%). Long-term physical and mental health outcomes were similar between service members who underwent reconstruction and those who underwent amputation. CONCLUSION: This military experience provides data that will inform an array of military and civilian providers who care for patients with severe lower-extremity injury. While the majority salvage attempts endure, success is hindered by ischemia and necrosis during the acute stage and pain, dysfunction and infection in the later phases of recovery. LEVEL OF EVIDENCE: Therapeutic/prognostic, level III.