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Keim-Malpass J, Moorman LP, Moorman JR, Hamil S, Yousevfand G, Monfredi OJ, Ratcliffe SJ, Krahn KN, Jones MK, Clark MT, Bourque JM. Prospective validation of clinical deterioration predictive models prior to intensive care unit transfer among patients admitted to acute care cardiology wards. Physiol Meas 2024; 45:065004. [PMID: 38772399 DOI: 10.1088/1361-6579/ad4e90] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Accepted: 05/21/2024] [Indexed: 05/23/2024]
Abstract
Objective. Very few predictive models have been externally validated in a prospective cohort following the implementation of an artificial intelligence analytic system. This type of real-world validation is critically important due to the risk of data drift, or changes in data definitions or clinical practices over time, that could impact model performance in contemporaneous real-world cohorts. In this work, we report the model performance of a predictive analytics tool developed before COVID-19 and demonstrate model performance during the COVID-19 pandemic.Approach. The analytic system (CoMETⓇ, Nihon Kohden Digital Health Solutions LLC, Irvine, CA) was implemented in a randomized controlled trial that enrolled 10 422 patient visits in a 1:1 display-on display-off design. The CoMET scores were calculated for all patients but only displayed in the display-on arm. Only the control/display-off group is reported here because the scores could not alter care patterns.Main results.Of the 5184 visits in the display-off arm, 311 experienced clinical deterioration and care escalation, resulting in transfer to the intensive care unit, primarily due to respiratory distress. The model performance of CoMET was assessed based on areas under the receiver operating characteristic curve, which ranged from 0.725 to 0.737.Significance.The models were well-calibrated, and there were dynamic increases in the model scores in the hours preceding the clinical deterioration events. A hypothetical alerting strategy based on a rise in score and duration of the rise would have had good performance, with a positive predictive value more than 10-fold the event rate. We conclude that predictive statistical models developed five years before study initiation had good model performance despite the passage of time and the impact of the COVID-19 pandemic.
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Affiliation(s)
- Jessica Keim-Malpass
- Center for Advanced Medical Analytics, School of Medicine, University of Virginia, Charlottesville, VA, United States of America
- Department of Pediatrics, Hematology-Oncology Division, School of Medicine, University of Virginia, Charlottesville, VA, United States of America
| | - Liza P Moorman
- Nihon Kohden Digital Health Solutions, Irvine, CA, United States of America
| | - J Randall Moorman
- Center for Advanced Medical Analytics, School of Medicine, University of Virginia, Charlottesville, VA, United States of America
- Department of Internal Medicine, Cardiovascular Division, School of Medicine, University of Virginia, Charlottesville, VA, United States of America
| | - Susan Hamil
- Center for Advanced Medical Analytics, School of Medicine, University of Virginia, Charlottesville, VA, United States of America
- Department of Internal Medicine, Cardiovascular Division, School of Medicine, University of Virginia, Charlottesville, VA, United States of America
| | - Gholamreza Yousevfand
- Center for Advanced Medical Analytics, School of Medicine, University of Virginia, Charlottesville, VA, United States of America
- Department of Internal Medicine, Cardiovascular Division, School of Medicine, University of Virginia, Charlottesville, VA, United States of America
| | - Oliver J Monfredi
- Center for Advanced Medical Analytics, School of Medicine, University of Virginia, Charlottesville, VA, United States of America
- Department of Internal Medicine, Cardiovascular Division, School of Medicine, University of Virginia, Charlottesville, VA, United States of America
| | - Sarah J Ratcliffe
- Department of Public Health Sciences, Biostatistics Division, University of Virginia, Charlottesville, VA, United States of America
| | - Katy N Krahn
- Center for Advanced Medical Analytics, School of Medicine, University of Virginia, Charlottesville, VA, United States of America
- Department of Internal Medicine, Cardiovascular Division, School of Medicine, University of Virginia, Charlottesville, VA, United States of America
| | - Marieke K Jones
- Department of Public Health Sciences, Biostatistics Division, University of Virginia, Charlottesville, VA, United States of America
| | - Matthew T Clark
- Nihon Kohden Digital Health Solutions, Irvine, CA, United States of America
| | - Jamieson M Bourque
- Center for Advanced Medical Analytics, School of Medicine, University of Virginia, Charlottesville, VA, United States of America
- Department of Internal Medicine, Cardiovascular Division, School of Medicine, University of Virginia, Charlottesville, VA, United States of America
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Campanaro CK, Nethery DE, Guo F, Kaffashi F, Loparo KA, Jacono FJ, Dick TE, Hsieh YH. Dynamics of ventilatory pattern variability and Cardioventilatory Coupling during systemic inflammation in rats. FRONTIERS IN NETWORK PHYSIOLOGY 2023; 3:1038531. [PMID: 37583625 PMCID: PMC10423997 DOI: 10.3389/fnetp.2023.1038531] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Accepted: 06/20/2023] [Indexed: 08/17/2023]
Abstract
Introduction: Biometrics of common physiologic signals can reflect health status. We have developed analytics to measure the predictability of ventilatory pattern variability (VPV, Nonlinear Complexity Index (NLCI) that quantifies the predictability of a continuous waveform associated with inhalation and exhalation) and the cardioventilatory coupling (CVC, the tendency of the last heartbeat in expiration to occur at preferred latency before the next inspiration). We hypothesized that measures of VPV and CVC are sensitive to the development of endotoxemia, which evoke neuroinflammation. Methods: We implanted Sprague Dawley male rats with BP transducers to monitor arterial blood pressure (BP) and recorded ventilatory waveforms and BP simultaneously using whole-body plethysmography in conjunction with BP transducer receivers. After baseline (BSLN) recordings, we injected lipopolysaccharide (LPS, n = 8) or phosphate buffered saline (PBS, n =3) intraperitoneally on 3 consecutive days. We recorded for 4-6 h after the injection, chose 3 epochs from each hour and analyzed VPV and CVC as well as heart rate variability (HRV). Results: First, the responses to sepsis varied across rats, but within rats the repeated measures of NLCI, CVC, as well as respiratory frequency (fR), HR, BP and HRV had a low coefficient of variation, (<0.2) at each time point. Second, HR, fR, and NLCI increased from BSLN on Days 1-3; whereas CVC decreased on Days 2 and 3. In contrast, changes in BP and the relative low-(LF) and high-frequency (HF) of HRV were not significant. The coefficient of variation decreased from BSLN to Day 3, except for CVC. Interestingly, NLCI increased before fR in LPS-treated rats. Finally, we histologically confirmed lung injury, systemic inflammation via ELISA and the presence of the proinflammatory cytokine, IL-1β, with immunohistochemistry in the ponto-medullary respiratory nuclei. Discussion: Our findings support that NLCI reflects changes in the rat's health induced by systemic injection of LPS and reflected in increases in HR and fR. CVC decreased over the course to the experiment. We conclude that NLCI reflected the increase in predictability of the ventilatory waveform and (together with our previous work) may reflect action of inflammatory cytokines on the network generating respiration.
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Affiliation(s)
- Cara K. Campanaro
- Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, Case Western Reserve University, Cleveland, OH, United States
| | - David E. Nethery
- Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, Case Western Reserve University, Cleveland, OH, United States
| | - Fei Guo
- Institute for Smart, Secure and Connected Systems (ISSACS), Case Western Reserve University, Cleveland, OH, United States
| | - Farhad Kaffashi
- Institute for Smart, Secure and Connected Systems (ISSACS), Case Western Reserve University, Cleveland, OH, United States
| | - Kenneth A. Loparo
- Institute for Smart, Secure and Connected Systems (ISSACS), Case Western Reserve University, Cleveland, OH, United States
| | - Frank J. Jacono
- Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, Case Western Reserve University, Cleveland, OH, United States
- Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, Louis Stokes Cleveland VA Medical Center, Cleveland, OH, United States
| | - Thomas E. Dick
- Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, Case Western Reserve University, Cleveland, OH, United States
- Department of Neurosciences, Case Western Reserve University, Cleveland, OH, United States
| | - Yee-Hsee Hsieh
- Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, Case Western Reserve University, Cleveland, OH, United States
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Predictive Modeling for Readmission to Intensive Care: A Systematic Review. Crit Care Explor 2023; 5:e0848. [PMID: 36699252 PMCID: PMC9829260 DOI: 10.1097/cce.0000000000000848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023] Open
Abstract
To evaluate the methodologic rigor and predictive performance of models predicting ICU readmission; to understand the characteristics of ideal prediction models; and to elucidate relationships between appropriate triage decisions and patient outcomes. DATA SOURCES PubMed, Web of Science, Cochrane, and Embase. STUDY SELECTION Primary literature that reported the development or validation of ICU readmission prediction models within from 2010 to 2021. DATA EXTRACTION Relevant study information was extracted independently by two authors using the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies checklist. Bias was evaluated using the Prediction model Risk Of Bias ASsessment Tool. Data sources, modeling methodology, definition of outcomes, performance, and risk of bias were critically evaluated to elucidate relevant relationships. DATA SYNTHESIS Thirty-three articles describing models were included. Six studies had a high overall risk of bias due to improper inclusion criteria or omission of critical analysis details. Four other studies had an unclear overall risk of bias due to lack of detail describing the analysis. Overall, the most common (50% of studies) source of bias was the filtering of candidate predictors via univariate analysis. The poorest performing models used existing clinical risk or acuity scores such as Acute Physiologic Assessment and Chronic Health Evaluation II, Sequential Organ Failure Assessment, or Stability and Workload Index for Transfer as the sole predictor. The higher-performing ICU readmission prediction models used homogenous patient populations, specifically defined outcomes, and routinely collected predictors that were analyzed over time. CONCLUSIONS Models predicting ICU readmission can achieve performance advantages by using longitudinal time series modeling, homogenous patient populations, and predictor variables tailored to those populations.
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Monfredi OJ, Moore CC, Sullivan BA, Keim-Malpass J, Fairchild KD, Loftus TJ, Bihorac A, Krahn KN, Dubrawski A, Lake DE, Moorman JR, Clermont G. Continuous ECG monitoring should be the heart of bedside AI-based predictive analytics monitoring for early detection of clinical deterioration. J Electrocardiol 2023; 76:35-38. [PMID: 36434848 PMCID: PMC10061545 DOI: 10.1016/j.jelectrocard.2022.10.011] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 10/01/2022] [Accepted: 10/22/2022] [Indexed: 11/24/2022]
Abstract
The idea that we can detect subacute potentially catastrophic illness earlier by using statistical models trained on clinical data is now well-established. We review evidence that supports the role of continuous cardiorespiratory monitoring in these predictive analytics monitoring tools. In particular, we review how continuous ECG monitoring reflects the patient and not the clinician, is less likely to be biased, is unaffected by changes in practice patterns, captures signatures of illnesses that are interpretable by clinicians, and is an underappreciated and underutilized source of detailed information for new mathematical methods to reveal.
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Affiliation(s)
- Oliver J Monfredi
- Center for Advanced Medical Analytics, University of Virginia, United States of America; Department of Medicine, University of Virginia, United States of America
| | - Christopher C Moore
- Center for Advanced Medical Analytics, University of Virginia, United States of America; Department of Medicine, University of Virginia, United States of America
| | - Brynne A Sullivan
- Center for Advanced Medical Analytics, University of Virginia, United States of America; Department of Pediatrics, University of Virginia, United States of America
| | - Jessica Keim-Malpass
- Center for Advanced Medical Analytics, University of Virginia, United States of America; School of Nursing, University of Virginia, United States of America
| | - Karen D Fairchild
- Center for Advanced Medical Analytics, University of Virginia, United States of America; Department of Pediatrics, University of Virginia, United States of America
| | - Tyler J Loftus
- Department of Surgery, University of Florida, United States of America
| | - Azra Bihorac
- Department of Medicine, University of Florida, United States of America
| | - Katherine N Krahn
- Center for Advanced Medical Analytics, University of Virginia, United States of America; Department of Medicine, University of Virginia, United States of America
| | - Artur Dubrawski
- Robotics Institute, Carnegie Mellon University, United States of America
| | - Douglas E Lake
- Center for Advanced Medical Analytics, University of Virginia, United States of America; Department of Medicine, University of Virginia, United States of America
| | - J Randall Moorman
- Center for Advanced Medical Analytics, University of Virginia, United States of America; Department of Medicine, University of Virginia, United States of America.
| | - Gilles Clermont
- Department of Critical Care, University of Pittsburgh, United States of America
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Spaeder MC, Moorman JR, Moorman LP, Adu-Darko MA, Keim-Malpass J, Lake DE, Clark MT. Signatures of illness in children requiring unplanned intubation in the pediatric intensive care unit: A retrospective cohort machine-learning study. Front Pediatr 2022; 10:1016269. [PMID: 36440325 PMCID: PMC9682496 DOI: 10.3389/fped.2022.1016269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 09/26/2022] [Indexed: 11/09/2022] Open
Abstract
Acute respiratory failure requiring the initiation of invasive mechanical ventilation remains commonplace in the pediatric intensive care unit (PICU). Early recognition of patients at risk for respiratory failure may provide clinicians with the opportunity to intervene and potentially improve outcomes. Through the development of a random forest model to identify patients at risk for requiring unplanned intubation, we tested the hypothesis that subtle signatures of illness are present in physiological and biochemical time series of PICU patients in the early stages of respiratory decompensation. We included 116 unplanned intubation events as recorded in the National Emergency Airway Registry for Children in 92 PICU admissions over a 29-month period at our institution. We observed that children have a physiologic signature of illness preceding unplanned intubation in the PICU. Generally, it comprises younger age, and abnormalities in electrolyte, hematologic and vital sign parameters. Additionally, given the heterogeneity of the PICU patient population, we found differences in the presentation among the major patient groups - medical, cardiac surgical, and non-cardiac surgical. At four hours prior to the event, our random forest model demonstrated an area under the receiver operating characteristic curve of 0.766 (0.738 for medical, 0.755 for cardiac surgical, and 0.797 for non-cardiac surgical patients). The multivariable statistical models that captured the physiological and biochemical dynamics leading up to the event of urgent unplanned intubation in a PICU can be repurposed for bedside risk prediction.
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Affiliation(s)
- Michael C. Spaeder
- Department of Pediatrics, Division of Pediatric Critical Care, School of Medicine, University of Virginia, Charlottesville, VA, United States
- Center for Advanced Medical Analytics, School of Medicine, University of Virginia, Charlottesville, VA, United States
| | - J. Randall Moorman
- Center for Advanced Medical Analytics, School of Medicine, University of Virginia, Charlottesville, VA, United States
- Department of Medicine, Division of Cardiovascular Medicine, School of Medicine, University of Virginia, Charlottesville, VA, United States
| | - Liza P. Moorman
- Center for Advanced Medical Analytics, School of Medicine, University of Virginia, Charlottesville, VA, United States
- Nihon Kohden Digital Health Solutions, Irvine, CA, United States
| | - Michelle A. Adu-Darko
- Department of Pediatrics, Division of Pediatric Critical Care, School of Medicine, University of Virginia, Charlottesville, VA, United States
| | - Jessica Keim-Malpass
- Center for Advanced Medical Analytics, School of Medicine, University of Virginia, Charlottesville, VA, United States
- Department of Acute and Specialty Care, School of Nursing, University of Virginia, Charlottesville, VA, United States
| | - Douglas E. Lake
- Center for Advanced Medical Analytics, School of Medicine, University of Virginia, Charlottesville, VA, United States
- Department of Medicine, Division of Cardiovascular Medicine, School of Medicine, University of Virginia, Charlottesville, VA, United States
| | - Matthew T. Clark
- Center for Advanced Medical Analytics, School of Medicine, University of Virginia, Charlottesville, VA, United States
- Nihon Kohden Digital Health Solutions, Irvine, CA, United States
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The principles of whole-hospital predictive analytics monitoring for clinical medicine originated in the neonatal ICU. NPJ Digit Med 2022; 5:41. [PMID: 35361861 PMCID: PMC8971442 DOI: 10.1038/s41746-022-00584-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 02/23/2022] [Indexed: 11/17/2022] Open
Abstract
In 2011, a multicenter group spearheaded at the University of Virginia demonstrated reduced mortality from real-time continuous cardiorespiratory monitoring in the neonatal ICU using what we now call Artificial Intelligence, Big Data, and Machine Learning. The large, randomized heart rate characteristics trial made real, for the first time that we know of, the promise that early detection of illness would allow earlier and more effective intervention and improved patient outcomes. Currently, though, we hear as much of failures as we do of successes in the rapidly growing field of predictive analytics monitoring that has followed. This Perspective aims to describe the principles of how we developed heart rate characteristics monitoring for neonatal sepsis and then applied them throughout adult ICU and hospital medicine. It primarily reflects the work since the 1990s of the University of Virginia group: the theme is that sudden and catastrophic deteriorations can be preceded by subclinical but measurable physiological changes apparent in the continuous cardiorespiratory monitoring and electronic health record.
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Kausch SL, Sullivan B, Spaeder MC, Keim-Malpass J. Individual illness dynamics: An analysis of children with sepsis admitted to the pediatric intensive care unit. PLOS DIGITAL HEALTH 2022; 1:e0000019. [PMID: 36812513 PMCID: PMC9931234 DOI: 10.1371/journal.pdig.0000019] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/29/2021] [Accepted: 01/30/2022] [Indexed: 12/16/2022]
Abstract
Illness dynamics and patterns of recovery may be essential features in understanding the critical illness course. We propose a method to characterize individual illness dynamics in patients who experienced sepsis in the pediatric intensive care unit. We defined illness states based on illness severity scores generated from a multi-variable prediction model. For each patient, we calculated transition probabilities to characterize movement among illness states. We calculated the Shannon entropy of the transition probabilities. Using the entropy parameter, we determined phenotypes of illness dynamics based on hierarchical clustering. We also examined the association between individual entropy scores and a composite variable of negative outcomes. Entropy-based clustering identified four illness dynamic phenotypes in a cohort of 164 intensive care unit admissions where at least one sepsis event occurred. Compared to the low-risk phenotype, the high-risk phenotype was defined by the highest entropy values and had the most ill patients as defined by a composite variable of negative outcomes. Entropy was significantly associated with the negative outcome composite variable in a regression analysis. Information-theoretical approaches to characterize illness trajectories offer a novel way of assessing the complexity of a course of illness. Characterizing illness dynamics with entropy offers additional information in conjunction with static assessments of illness severity. Additional attention is needed to test and incorporate novel measures representing the dynamics of illness.
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Affiliation(s)
- Sherry L. Kausch
- University of Virginia School of Nursing, Charlottesville, VA, United States of America
- Center for Advanced Medical Analytics, University of Virginia, Charlottesville, VA, United States of America
- * E-mail:
| | - Brynne Sullivan
- Center for Advanced Medical Analytics, University of Virginia, Charlottesville, VA, United States of America
- Department of Pediatrics, Division of Neonatology, University of Virginia School of Medicine, Charlottesville, VA, United States of America
| | - Michael C. Spaeder
- Center for Advanced Medical Analytics, University of Virginia, Charlottesville, VA, United States of America
- Department of Pediatrics, Division of Pediatric Critical Care, University of Virginia School of Medicine, Charlottesville, VA, United States of America
| | - Jessica Keim-Malpass
- University of Virginia School of Nursing, Charlottesville, VA, United States of America
- Center for Advanced Medical Analytics, University of Virginia, Charlottesville, VA, United States of America
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