Smart respiratory monitoring: clinical development and validation of the IPI™ (Integrated Pulmonary Index) algorithm

Continuous electronic monitoring of patient respiratory status frequently includes PetCO 2 (end tidal CO 2 ), RR (respiration rate), SpO 2 (arterial oxygen saturation), and PR (pulse rate). Interpreting and integrating these vital signs as numbers or waveforms is routinely done by anesthesiologists...

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Vydáno v:Journal of clinical monitoring and computing Ročník 31; číslo 2; s. 435 - 442
Hlavní autoři: Ronen, M., Weissbrod, R., Overdyk, F. J., Ajizian, S.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Dordrecht Springer Netherlands 01.04.2017
Springer Nature B.V
Témata:
Adult
Algorithms
Anesthesiology
Capnography - instrumentation
Capnography - methods
Child
Child, Preschool
Cluster Analysis
Critical Care Medicine
Electronics
Fuzzy Logic
Health Sciences
Humans
Infant
Inference
Intensive
Lung
Mathematical models
Medicine
Medicine & Public Health
Models, Theoretical
Monitoring
Monitoring, Physiologic - instrumentation
Monitoring, Physiologic - methods
Nurses
Original Research
Oximetry - instrumentation
Oximetry - methods
Patients
Physicians
Prospective Studies
Pulmonary Gas Exchange
Reproducibility of Results
Respiration
Respiratory Rate
Respiratory Therapy
Retrospective Studies
ROC Curve
Sensitivity analysis
Signal Processing, Computer-Assisted
Statistics for Life Sciences
Surveys and Questionnaires
Waveforms
Respiratory monitoring
IPI
Capnography
Respiratory compromise
Composite index
ISSN:1387-1307, 1573-2614, 1573-2614
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Abstract Continuous electronic monitoring of patient respiratory status frequently includes PetCO 2 (end tidal CO 2 ), RR (respiration rate), SpO 2 (arterial oxygen saturation), and PR (pulse rate). Interpreting and integrating these vital signs as numbers or waveforms is routinely done by anesthesiologists and intensivists but is challenging for clinicians in low acuity areas such as medical wards, where continuous electronic respiratory monitoring is becoming more common place. We describe a heuristic algorithm that simplifies the interpretation of these four parameters in assessing a patient’s respiratory status, the Integrated Pulmonary Index (IPI). The IPI algorithm is a mathematical model combining SpO 2 , RR, PR, and PetCO 2 into a single value between 1 and 10 that summarizes the adequacy of ventilation and oxygenation at that point in time. The algorithm was designed using a fuzzy logic inference model to incorporate expert clinical opinions. The algorithm was verified by comparison to experts’ scoring of clinical scenarios. The validity of the index was tested in a retrospective analysis of continuous SpO 2 , RR, PR, and PetCO 2 readings obtained from 523 patients in a variety of clinical settings. IPI correlated well with expert interpretation of the continuous respiratory data (R = 0.83, p  <<< 0.001), with agreement of −0.5 ± 1.4. Receiver operating curves analysis resulted in high levels of sensitivity (ranging from 0.83 to 1.00), and corresponding specificity (ranging from 0.96 to 0.74), based on IPI thresholds 3−6. The IPI reliably interpreted the respiratory status of patients in multiple areas of care using off-line continuous respiratory data. Further prospective studies are required to evaluate IPI in real time in clinical settings.
AbstractList Continuous electronic monitoring of patient respiratory status frequently includes PetCO sub(2) (end tidal CO sub(2)), RR (respiration rate), SpO sub(2) (arterial oxygen saturation), and PR (pulse rate). Interpreting and integrating these vital signs as numbers or waveforms is routinely done by anesthesiologists and intensivists but is challenging for clinicians in low acuity areas such as medical wards, where continuous electronic respiratory monitoring is becoming more common place. We describe a heuristic algorithm that simplifies the interpretation of these four parameters in assessing a patient's respiratory status, the Integrated Pulmonary Index (IPI). The IPI algorithm is a mathematical model combining SpO sub(2), RR, PR, and PetCO sub(2) into a single value between 1 and 10 that summarizes the adequacy of ventilation and oxygenation at that point in time. The algorithm was designed using a fuzzy logic inference model to incorporate expert clinical opinions. The algorithm was verified by comparison to experts' scoring of clinical scenarios. The validity of the index was tested in a retrospective analysis of continuous SpO sub(2), RR, PR, and PetCO sub(2) readings obtained from 523 patients in a variety of clinical settings. IPI correlated well with expert interpretation of the continuous respiratory data (R = 0.83, p <<< 0.001), with agreement of -0.5 plus or minus 1.4. Receiver operating curves analysis resulted in high levels of sensitivity (ranging from 0.83 to 1.00), and corresponding specificity (ranging from 0.96 to 0.74), based on IPI thresholds 3-6. The IPI reliably interpreted the respiratory status of patients in multiple areas of care using off-line continuous respiratory data. Further prospective studies are required to evaluate IPI in real time in clinical settings.
Continuous electronic monitoring of patient respiratory status frequently includes PetCO2 (end tidal CO2), RR (respiration rate), SpO2 (arterial oxygen saturation), and PR (pulse rate). Interpreting and integrating these vital signs as numbers or waveforms is routinely done by anesthesiologists and intensivists but is challenging for clinicians in low acuity areas such as medical wards, where continuous electronic respiratory monitoring is becoming more common place. We describe a heuristic algorithm that simplifies the interpretation of these four parameters in assessing a patient's respiratory status, the Integrated Pulmonary Index (IPI). The IPI algorithm is a mathematical model combining SpO2, RR, PR, and PetCO2 into a single value between 1 and 10 that summarizes the adequacy of ventilation and oxygenation at that point in time. The algorithm was designed using a fuzzy logic inference model to incorporate expert clinical opinions. The algorithm was verified by comparison to experts' scoring of clinical scenarios. The validity of the index was tested in a retrospective analysis of continuous SpO2, RR, PR, and PetCO2 readings obtained from 523 patients in a variety of clinical settings. IPI correlated well with expert interpretation of the continuous respiratory data (R = 0.83, p <<< 0.001), with agreement of -0.5 ± 1.4. Receiver operating curves analysis resulted in high levels of sensitivity (ranging from 0.83 to 1.00), and corresponding specificity (ranging from 0.96 to 0.74), based on IPI thresholds 3-6. The IPI reliably interpreted the respiratory status of patients in multiple areas of care using off-line continuous respiratory data. Further prospective studies are required to evaluate IPI in real time in clinical settings.Continuous electronic monitoring of patient respiratory status frequently includes PetCO2 (end tidal CO2), RR (respiration rate), SpO2 (arterial oxygen saturation), and PR (pulse rate). Interpreting and integrating these vital signs as numbers or waveforms is routinely done by anesthesiologists and intensivists but is challenging for clinicians in low acuity areas such as medical wards, where continuous electronic respiratory monitoring is becoming more common place. We describe a heuristic algorithm that simplifies the interpretation of these four parameters in assessing a patient's respiratory status, the Integrated Pulmonary Index (IPI). The IPI algorithm is a mathematical model combining SpO2, RR, PR, and PetCO2 into a single value between 1 and 10 that summarizes the adequacy of ventilation and oxygenation at that point in time. The algorithm was designed using a fuzzy logic inference model to incorporate expert clinical opinions. The algorithm was verified by comparison to experts' scoring of clinical scenarios. The validity of the index was tested in a retrospective analysis of continuous SpO2, RR, PR, and PetCO2 readings obtained from 523 patients in a variety of clinical settings. IPI correlated well with expert interpretation of the continuous respiratory data (R = 0.83, p <<< 0.001), with agreement of -0.5 ± 1.4. Receiver operating curves analysis resulted in high levels of sensitivity (ranging from 0.83 to 1.00), and corresponding specificity (ranging from 0.96 to 0.74), based on IPI thresholds 3-6. The IPI reliably interpreted the respiratory status of patients in multiple areas of care using off-line continuous respiratory data. Further prospective studies are required to evaluate IPI in real time in clinical settings.
Continuous electronic monitoring of patient respiratory status frequently includes PetCO2 (end tidal CO2), RR (respiration rate), SpO2 (arterial oxygen saturation), and PR (pulse rate). Interpreting and integrating these vital signs as numbers or waveforms is routinely done by anesthesiologists and intensivists but is challenging for clinicians in low acuity areas such as medical wards, where continuous electronic respiratory monitoring is becoming more common place. We describe a heuristic algorithm that simplifies the interpretation of these four parameters in assessing a patient’s respiratory status, the Integrated Pulmonary Index (IPI). The IPI algorithm is a mathematical model combining SpO2, RR, PR, and PetCO2 into a single value between 1 and 10 that summarizes the adequacy of ventilation and oxygenation at that point in time. The algorithm was designed using a fuzzy logic inference model to incorporate expert clinical opinions. The algorithm was verified by comparison to experts’ scoring of clinical scenarios. The validity of the index was tested in a retrospective analysis of continuous SpO2, RR, PR, and PetCO2 readings obtained from 523 patients in a variety of clinical settings. IPI correlated well with expert interpretation of the continuous respiratory data (R = 0.83, p <<< 0.001), with agreement of −0.5 ± 1.4. Receiver operating curves analysis resulted in high levels of sensitivity (ranging from 0.83 to 1.00), and corresponding specificity (ranging from 0.96 to 0.74), based on IPI thresholds 3−6. The IPI reliably interpreted the respiratory status of patients in multiple areas of care using off-line continuous respiratory data. Further prospective studies are required to evaluate IPI in real time in clinical settings.
Continuous electronic monitoring of patient respiratory status frequently includes PetCO2 (end tidal CO2), RR (respiration rate), SpO2 (arterial oxygen saturation), and PR (pulse rate). Interpreting and integrating these vital signs as numbers or waveforms is routinely done by anesthesiologists and intensivists but is challenging for clinicians in low acuity areas such as medical wards, where continuous electronic respiratory monitoring is becoming more common place. We describe a heuristic algorithm that simplifies the interpretation of these four parameters in assessing a patient's respiratory status, the Integrated Pulmonary Index (IPI). The IPI algorithm is a mathematical model combining SpO2, RR, PR, and PetCO2 into a single value between 1 and 10 that summarizes the adequacy of ventilation and oxygenation at that point in time. The algorithm was designed using a fuzzy logic inference model to incorporate expert clinical opinions. The algorithm was verified by comparison to experts' scoring of clinical scenarios. The validity of the index was tested in a retrospective analysis of continuous SpO2, RR, PR, and PetCO2 readings obtained from 523 patients in a variety of clinical settings. IPI correlated well with expert interpretation of the continuous respiratory data (R = 0.83, p <<< 0.001), with agreement of -0.5 ± 1.4. Receiver operating curves analysis resulted in high levels of sensitivity (ranging from 0.83 to 1.00), and corresponding specificity (ranging from 0.96 to 0.74), based on IPI thresholds 3-6. The IPI reliably interpreted the respiratory status of patients in multiple areas of care using off-line continuous respiratory data. Further prospective studies are required to evaluate IPI in real time in clinical settings.
Continuous electronic monitoring of patient respiratory status frequently includes PetCO (end tidal CO ), RR (respiration rate), SpO (arterial oxygen saturation), and PR (pulse rate). Interpreting and integrating these vital signs as numbers or waveforms is routinely done by anesthesiologists and intensivists but is challenging for clinicians in low acuity areas such as medical wards, where continuous electronic respiratory monitoring is becoming more common place. We describe a heuristic algorithm that simplifies the interpretation of these four parameters in assessing a patient's respiratory status, the Integrated Pulmonary Index (IPI). The IPI algorithm is a mathematical model combining SpO , RR, PR, and PetCO into a single value between 1 and 10 that summarizes the adequacy of ventilation and oxygenation at that point in time. The algorithm was designed using a fuzzy logic inference model to incorporate expert clinical opinions. The algorithm was verified by comparison to experts' scoring of clinical scenarios. The validity of the index was tested in a retrospective analysis of continuous SpO , RR, PR, and PetCO readings obtained from 523 patients in a variety of clinical settings. IPI correlated well with expert interpretation of the continuous respiratory data (R = 0.83, p <<< 0.001), with agreement of -0.5 ± 1.4. Receiver operating curves analysis resulted in high levels of sensitivity (ranging from 0.83 to 1.00), and corresponding specificity (ranging from 0.96 to 0.74), based on IPI thresholds 3-6. The IPI reliably interpreted the respiratory status of patients in multiple areas of care using off-line continuous respiratory data. Further prospective studies are required to evaluate IPI in real time in clinical settings.
Continuous electronic monitoring of patient respiratory status frequently includes PetCO 2 (end tidal CO 2 ), RR (respiration rate), SpO 2 (arterial oxygen saturation), and PR (pulse rate). Interpreting and integrating these vital signs as numbers or waveforms is routinely done by anesthesiologists and intensivists but is challenging for clinicians in low acuity areas such as medical wards, where continuous electronic respiratory monitoring is becoming more common place. We describe a heuristic algorithm that simplifies the interpretation of these four parameters in assessing a patient’s respiratory status, the Integrated Pulmonary Index (IPI). The IPI algorithm is a mathematical model combining SpO 2 , RR, PR, and PetCO 2 into a single value between 1 and 10 that summarizes the adequacy of ventilation and oxygenation at that point in time. The algorithm was designed using a fuzzy logic inference model to incorporate expert clinical opinions. The algorithm was verified by comparison to experts’ scoring of clinical scenarios. The validity of the index was tested in a retrospective analysis of continuous SpO 2 , RR, PR, and PetCO 2 readings obtained from 523 patients in a variety of clinical settings. IPI correlated well with expert interpretation of the continuous respiratory data (R = 0.83, p  <<< 0.001), with agreement of −0.5 ± 1.4. Receiver operating curves analysis resulted in high levels of sensitivity (ranging from 0.83 to 1.00), and corresponding specificity (ranging from 0.96 to 0.74), based on IPI thresholds 3−6. The IPI reliably interpreted the respiratory status of patients in multiple areas of care using off-line continuous respiratory data. Further prospective studies are required to evaluate IPI in real time in clinical settings.
Author Ronen, M.
Overdyk, F. J.
Ajizian, S.
Weissbrod, R.
Author_xml – sequence: 1
  givenname: M.
  surname: Ronen
  fullname: Ronen, M.
  organization: Medtronic
– sequence: 2
  givenname: R.
  surname: Weissbrod
  fullname: Weissbrod, R.
  email: Rachel.weissbrod@medtronic.com
  organization: Medtronic
– sequence: 3
  givenname: F. J.
  surname: Overdyk
  fullname: Overdyk, F. J.
  organization: Department of Anesthesiology, Roper St Francis Medical Center
– sequence: 4
  givenname: S.
  surname: Ajizian
  fullname: Ajizian, S.
  organization: Medtronic
BackLink https://www.ncbi.nlm.nih.gov/pubmed/26961501$$D View this record in MEDLINE/PubMed
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ContentType Journal Article
Copyright The Author(s) 2016
Journal of Clinical Monitoring and Computing is a copyright of Springer, 2017.
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Issue 2
Keywords Respiratory monitoring
IPI
Capnography
Respiratory compromise
Composite index
Language English
License Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
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PublicationSubtitle Including a Specialty Section on Surgical Neuromonitoring
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Snippet Continuous electronic monitoring of patient respiratory status frequently includes PetCO 2 (end tidal CO 2 ), RR (respiration rate), SpO 2 (arterial oxygen...
Continuous electronic monitoring of patient respiratory status frequently includes PetCO (end tidal CO ), RR (respiration rate), SpO (arterial oxygen...
Continuous electronic monitoring of patient respiratory status frequently includes PetCO2 (end tidal CO2), RR (respiration rate), SpO2 (arterial oxygen...
Continuous electronic monitoring of patient respiratory status frequently includes PetCO sub(2) (end tidal CO sub(2)), RR (respiration rate), SpO sub(2)...
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StartPage 435
SubjectTerms Adult
Algorithms
Anesthesiology
Capnography - instrumentation
Capnography - methods
Child
Child, Preschool
Cluster Analysis
Critical Care Medicine
Electronics
Fuzzy Logic
Health Sciences
Humans
Infant
Inference
Intensive
Lung
Mathematical models
Medicine
Medicine & Public Health
Models, Theoretical
Monitoring
Monitoring, Physiologic - instrumentation
Monitoring, Physiologic - methods
Nurses
Original Research
Oximetry - instrumentation
Oximetry - methods
Patients
Physicians
Prospective Studies
Pulmonary Gas Exchange
Reproducibility of Results
Respiration
Respiratory Rate
Respiratory Therapy
Retrospective Studies
ROC Curve
Sensitivity analysis
Signal Processing, Computer-Assisted
Statistics for Life Sciences
Surveys and Questionnaires
Waveforms
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