Patients on step-down units (SDU) undergo continuous noninvasive vital sign (VS) monitoring to facilitate nurse detection of cardiorespiratory instability (CRI), yet our data show nurses do not quickly detect CRI onset nor seek help early 80% of the time even when individual VS monitors are alarming. We recently demonstrated that by using a complexity modeling-based algorithm we could improve detection of CRI. This proposal seeks to further apply complexity modeling-based algorithms to predict CRI prior to overt instability with sufficient lead-time and accuracy to support a nursing decision for preemptive therapy. Clinical decisional support systems (CDSS) continuously process complex data from disparate sources and apply predictive algorithms to alert clinicians early to impending events. One data-driven CDSS approach uses an artificial intelligence type called """"""""machine learning"""""""" to evaluate moving-time series data and learn data patterns leading to an event. However, applying CDSS to predict CRI events has been limited by lack of suitably detailed datasets for learning support. We recently demonstrated that artificial neural network (ANN) machine learning of static VS data detected CRI up to 9.5 min before any continuously monitored VS alarmed. By applying machine learning to evaluate high-frequency VS data over longer moving time blocks and including demographic and clinical data a more sensitive and specific CRI prediction with longer lead-times will emerge. We propose to: 1) assemble a complex multidimensional dataset from our existing data sources to serve as the learning platform, 2) develop and validate machine-learned models for dynamic CRI prediction in SDU patients, and test which models are most effective relative to predictive ability, model parsimony and lead-time to event, and 3) use these findings to develop a prototype CRI prediction CDSS tool for nurses. Our demonstrated ability to assemble large high-frequency datasets with defined CRI events from which machine learning occurs makes our team (nursing, medicine, mathematics, computational biology, engineering, statistics) uniquely qualified to conduct this work. Study findings can foster a shift in CRI care from a reactive to preemptive nursing approach. Developing a sensitive, specific, parsimonious and clinically practical means to predict patient instability has important implications for reducing preventable morbidity and mortality, improving patient safety, nursing care (monitoring frequency, case load and mixture, staff allocation) and care delivery systems (triage, bed allocation, prevention of adverse events).

Public Health Relevance

Data from the proposed study will provide clinical decision support to permit nurses to predict cardiorespiratory instability prior to its overt expression in continuously but noninvasively monitored step-down unit (SDU) patients, and predict those patients who are likely to become unstable in the future. Such information will importantly permit nurses to shift instability care from a reactive to a preemptive approach, and impact patient safety and preventable mortality in SDU patients. Developing a sensitive, specific and parsimonious means to predict which patients may become unstable also has important implications for improving nursing surveillance and care (frequency of monitoring, case load mixture, workload, staff allocation) and care delivery systems (patient triage to monitored or non-monitored units, higher vs. lower cost bed allocation, adverse events prevention).

National Institute of Health (NIH)
National Institute of Nursing Research (NINR)
Research Project (R01)
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Biomedical Computing and Health Informatics Study Section (BCHI)
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Huss, Karen
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University of Pittsburgh
Other Health Professions
Schools of Nursing
United States
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