Improvements in data quality for decision support in intensive care

Nowadays, there is a plethora of technology in hospitals and, in particular, in intensive care units. The clinical data produced everyday can be integrated in a decision support system in real-time to improve quality of care of the critically ill patients. However, there are many sensitive aspects that must be taken into account, mainly the data quality and the integration of heterogeneous data sources. This paper presents INTCare, an Intelligent Decision Support System for Intensive Care in real-time and addresses the previous aspects, in particular, the development of an Electronic Nursing Record and the improvements in the quality of monitored data.Fundação para a Ciência e a Tecnologia (FCT

A Comparison of Administrative and Physiologic Predictive Models in Determining Risk Adjusted Mortality Rates in Critically Ill Patients

Hospitals are increasingly compared based on clinical outcomes adjusted for severity of illness. Multiple methods exist to adjust for differences between patients. The challenge for consumers of this information, both the public and healthcare providers, is interpreting differences in risk adjustment models particularly when models differ in their use of administrative and physiologic data. We set to examine how administrative and physiologic models compare to each when applied to critically ill patients.We prospectively abstracted variables for a physiologic and administrative model of mortality from two intensive care units in the United States. Predicted mortality was compared through the Pearsons Product coefficient and Bland-Altman analysis. A subgroup of patients admitted directly from the emergency department was analyzed to remove potential confounding changes in condition prior to ICU admission.We included 556 patients from two academic medical centers in this analysis. The administrative model and physiologic models predicted mortalities for the combined cohort were 15.3% (95% CI 13.7%, 16.8%) and 24.6% (95% CI 22.7%, 26.5%) (t-test p-value<0.001). The r(2) for these models was 0.297. The Bland-Atlman plot suggests that at low predicted mortality there was good agreement; however, as mortality increased the models diverged. Similar results were found when analyzing a subgroup of patients admitted directly from the emergency department. When comparing the two hospitals, there was a statistical difference when using the administrative model but not the physiologic model. Unexplained mortality, defined as those patients who died who had a predicted mortality less than 10%, was a rare event by either model.In conclusion, while it has been shown that administrative models provide estimates of mortality that are similar to physiologic models in non-critically ill patients with pneumonia, our results suggest this finding can not be applied globally to patients admitted to intensive care units. As patients and providers increasingly use publicly reported information in making health care decisions and referrals, it is critical that the provided information be understood. Our results suggest that severity of illness may influence the mortality index in administrative models. We suggest that when interpreting "report cards" or metrics, health care providers determine how the risk adjustment was made and compares to other risk adjustment models

Towards the prevention of acute lung injury: a population based cohort study protocol

<p>Abstract</p> <p>Background</p> <p>Acute lung injury (ALI) is an example of a critical care syndrome with limited treatment options once the condition is fully established. Despite improved understanding of pathophysiology of ALI, the clinical impact has been limited to improvements in supportive treatment. On the other hand, little has been done on the prevention of ALI. Olmsted County, MN, geographically isolated from other urban areas offers the opportunity to study clinical pathogenesis of ALI in a search for potential prevention targets.</p> <p>Methods/Design</p> <p>In this population-based observational cohort study, the investigators identify patients at high risk of ALI using the prediction model applied within the first six hours of hospital admission. Using a validated system-wide electronic surveillance, Olmsted County patients at risk are followed until ALI, death or hospital discharge. Detailed in-hospital (second hit) exposures and meaningful short and long term outcomes (quality-adjusted survival) are compared between ALI cases and high risk controls matched by age, gender and probability of developing ALI. Time sensitive biospecimens are collected for collaborative research studies. Nested case control comparison of 500 patients who developed ALI with 500 matched controls will provide an adequate power to determine significant differences in common hospital exposures and outcomes between the two groups.</p> <p>Discussion</p> <p>This population-based observational cohort study will identify patients at high risk early in the course of disease, the burden of ALI in the community, and the potential targets for future prevention trials.</p

Precision Newborn Screening for Lysosomal Disorders

Purpose: The implementation of newborn screening for lysosomal disorders has uncovered overall poor specificity, psychosocial harm experienced by caregivers, and costly follow-up testing of false-positive cases. We report an informatics solution proven to minimize these issues. Methods: The Kentucky Department for Public Health outsourced testing for mucopolysaccharidosis type I (MPS I) and Pompe disease, conditions recently added to the recommended uniform screening panel, plus Krabbe disease, which was added by legislative mandate. A total of 55,161 specimens were collected from infants born over 1 year starting from February 2016. Testing by tandem mass spectrometry was integrated with multivariate pattern recognition software (Collaborative Laboratory Integrated Reports), which is freely available to newborn screening programs for selection of cases for which a biochemical second-tier test is needed. Results: Of five presumptive positive cases, one was affected with infantile Krabbe disease, two with Pompe disease, and one with MPS I. The remaining case was a heterozygote for the latter condition. The false-positive rate was 0.0018% and the positive predictive value was 80%. Conclusion: Postanalytical interpretive tools can drastically reduce false-positive outcomes, with preliminary evidence of no greater risk of false-negative events, still to be verified by long-term surveillance