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).

Agency
National Institute of Health (NIH)
Institute
National Institute of Nursing Research (NINR)
Type
Research Project (R01)
Project #
5R01NR013912-02
Application #
8554375
Study Section
Biomedical Computing and Health Informatics Study Section (BCHI)
Program Officer
Huss, Karen
Project Start
2012-09-27
Project End
2015-06-30
Budget Start
2013-07-01
Budget End
2014-06-30
Support Year
2
Fiscal Year
2013
Total Cost
$387,665
Indirect Cost
$75,365
Name
University of Pittsburgh
Department
Other Health Professions
Type
Schools of Nursing
DUNS #
004514360
City
Pittsburgh
State
PA
Country
United States
Zip Code
15213
Morris, Alan H (2018) Human Cognitive Limitations. Broad, Consistent, Clinical Application of Physiological Principles Will Require Decision Support. Ann Am Thorac Soc 15:S53-S56
Pinsky, Michael R (2018) Cardiopulmonary Interactions: Physiologic Basis and Clinical Applications. Ann Am Thorac Soc 15:S45-S48
Hravnak, Marilyn; Pellathy, Tiffany; Chen, Lujie et al. (2018) A call to alarms: Current state and future directions in the battle against alarm fatigue. J Electrocardiol 51:S44-S48
Yoon, Joo H; Pinsky, Michael R (2018) Predicting adverse hemodynamic events in critically ill patients. Curr Opin Crit Care 24:196-203
Mahmood, Syed S; Pinsky, Michael R (2018) Heart-lung interactions during mechanical ventilation: the basics. Ann Transl Med 6:349
Bose, Eliezer L; Clermont, Gilles; Chen, Lujie et al. (2018) Cardiorespiratory instability in monitored step-down unit patients: using cluster analysis to identify patterns of change. J Clin Monit Comput 32:117-126
Chen, Lujie; Ogundele, Olufunmilayo; Clermont, Gilles et al. (2017) Dynamic and Personalized Risk Forecast in Step-Down Units. Implications for Monitoring Paradigms. Ann Am Thorac Soc 14:384-391
Bose, Eliezer; Hravnak, Marilyn; Sereika, Susan M (2017) Vector Autoregressive Models and Granger Causality in Time Series Analysis in Nursing Research: Dynamic Changes Among Vital Signs Prior to Cardiorespiratory Instability Events as an Example. Nurs Res 66:12-19
Guillame-Bert, Mathieu; Dubrawski, Artur; Wang, Donghan et al. (2017) Learning temporal rules to forecast instability in continuously monitored patients. J Am Med Inform Assoc 24:47-53
Hravnak, Marilyn; Chen, Lujie; Dubrawski, Artur et al. (2016) Real alerts and artifact classification in archived multi-signal vital sign monitoring data: implications for mining big data. J Clin Monit Comput 30:875-888

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