Closed-loop automated critical care as proof-of-concept study for resuscitation in a swine model of ischemia–reperfusion injury
Background Volume expansion and vasopressors for the treatment of shock is an intensive process that requires frequent assessments and adjustments. Strict blood pressure goals in multiple physiologic states of shock (traumatic brain injury, sepsis, and hemorrhagic) have been associated with improved...
Ausführliche Beschreibung
Autor*in: |
Patel, Nathan T. P. [verfasserIn] |
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E-Artikel |
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Englisch |
Erschienen: |
2022 |
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Anmerkung: |
© The Author(s) 2022 |
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Übergeordnetes Werk: |
Enthalten in: Intensive Care Medicine Experimental - Berlin : SpringerOpen, 2013, 10(2022), 1 vom: 08. Juli |
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Übergeordnetes Werk: |
volume:10 ; year:2022 ; number:1 ; day:08 ; month:07 |
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DOI / URN: |
10.1186/s40635-022-00459-2 |
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Katalog-ID: |
SPR047523158 |
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520 | |a Background Volume expansion and vasopressors for the treatment of shock is an intensive process that requires frequent assessments and adjustments. Strict blood pressure goals in multiple physiologic states of shock (traumatic brain injury, sepsis, and hemorrhagic) have been associated with improved outcomes. The availability of continuous physiologic data is amenable to closed-loop automated critical care to improve goal-directed resuscitation. Methods Five adult swine were anesthetized and subjected to a controlled 30% estimated total blood volume hemorrhage followed by 30 min of complete supra-celiac aortic occlusion and then autotransfusion back to euvolemia with removal of aortic balloon. The animals underwent closed-loop critical care for 255 min after removal of the endovascular aortic balloon. The closed-loop critical care algorithm used proximal aortic pressure and central venous pressure as physiologic input data. The algorithm had the option to provide programmatic control of pumps for titration of vasopressors and weight-based crystalloid boluses (5 ml/kg) to maintain a mean arterial pressure between 60 and 70 mmHg. Results During the 255 min of critical care the animals experienced hypotension (< 60 mmHg) 15.3% (interquartile range: 8.6–16.9%), hypertension (> 70 mmHg) 7.7% (interquartile range: 6.7–9.4%), and normotension (60–70 mmHg) 76.9% (interquartile range: 76.5–81.2%) of the time. Excluding the first 60 min of the critical care phase the animals experienced hypotension 1.0% (interquartile range: 0.5–6.7%) of the time. Median intervention rate was 8.47 interventions per hour (interquartile range: 7.8–9.2 interventions per hour). The proportion of interventions was 61.5% (interquartile range: 61.1–66.7%) weight-based crystalloid boluses and 38.5% (interquartile range: 33.3–38.9%) titration of vasopressors. Conclusion This autonomous critical care platform uses critical care adjuncts in an ischemia–reperfusion injury model, utilizing goal-directed closed-loop critical care algorithm and device actuation. This description highlights the potential for this approach to deliver nuanced critical care in the ICU environment, thereby optimizing resuscitative efforts and expanding capabilities through cognitive offloading. Future efforts will focus on optimizing this platform through comparative studies of inputs, therapies, and comparison to manual critical care. | ||
650 | 4 | |a Closed-loop |7 (dpeaa)DE-He213 | |
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650 | 4 | |a Ischemia reperfusion |7 (dpeaa)DE-He213 | |
650 | 4 | |a Swine |7 (dpeaa)DE-He213 | |
700 | 1 | |a Goenaga-Diaz, Eduardo J. |4 aut | |
700 | 1 | |a Lane, Magan R. |4 aut | |
700 | 1 | |a Austin Johnson, M. |4 aut | |
700 | 1 | |a Neff, Lucas P. |4 aut | |
700 | 1 | |a Williams, Timothy K. |4 aut | |
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10.1186/s40635-022-00459-2 doi (DE-627)SPR047523158 (SPR)s40635-022-00459-2-e DE-627 ger DE-627 rakwb eng Patel, Nathan T. P. verfasserin (orcid)0000-0001-5230-0767 aut Closed-loop automated critical care as proof-of-concept study for resuscitation in a swine model of ischemia–reperfusion injury 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Background Volume expansion and vasopressors for the treatment of shock is an intensive process that requires frequent assessments and adjustments. Strict blood pressure goals in multiple physiologic states of shock (traumatic brain injury, sepsis, and hemorrhagic) have been associated with improved outcomes. The availability of continuous physiologic data is amenable to closed-loop automated critical care to improve goal-directed resuscitation. Methods Five adult swine were anesthetized and subjected to a controlled 30% estimated total blood volume hemorrhage followed by 30 min of complete supra-celiac aortic occlusion and then autotransfusion back to euvolemia with removal of aortic balloon. The animals underwent closed-loop critical care for 255 min after removal of the endovascular aortic balloon. The closed-loop critical care algorithm used proximal aortic pressure and central venous pressure as physiologic input data. The algorithm had the option to provide programmatic control of pumps for titration of vasopressors and weight-based crystalloid boluses (5 ml/kg) to maintain a mean arterial pressure between 60 and 70 mmHg. Results During the 255 min of critical care the animals experienced hypotension (< 60 mmHg) 15.3% (interquartile range: 8.6–16.9%), hypertension (> 70 mmHg) 7.7% (interquartile range: 6.7–9.4%), and normotension (60–70 mmHg) 76.9% (interquartile range: 76.5–81.2%) of the time. Excluding the first 60 min of the critical care phase the animals experienced hypotension 1.0% (interquartile range: 0.5–6.7%) of the time. Median intervention rate was 8.47 interventions per hour (interquartile range: 7.8–9.2 interventions per hour). The proportion of interventions was 61.5% (interquartile range: 61.1–66.7%) weight-based crystalloid boluses and 38.5% (interquartile range: 33.3–38.9%) titration of vasopressors. Conclusion This autonomous critical care platform uses critical care adjuncts in an ischemia–reperfusion injury model, utilizing goal-directed closed-loop critical care algorithm and device actuation. This description highlights the potential for this approach to deliver nuanced critical care in the ICU environment, thereby optimizing resuscitative efforts and expanding capabilities through cognitive offloading. Future efforts will focus on optimizing this platform through comparative studies of inputs, therapies, and comparison to manual critical care. Closed-loop (dpeaa)DE-He213 Critical care (dpeaa)DE-He213 Automated (dpeaa)DE-He213 Ischemia reperfusion (dpeaa)DE-He213 Swine (dpeaa)DE-He213 Goenaga-Diaz, Eduardo J. aut Lane, Magan R. aut Austin Johnson, M. aut Neff, Lucas P. aut Williams, Timothy K. aut Enthalten in Intensive Care Medicine Experimental Berlin : SpringerOpen, 2013 10(2022), 1 vom: 08. Juli (DE-627)771394640 (DE-600)2740385-3 2197-425X nnns volume:10 year:2022 number:1 day:08 month:07 https://dx.doi.org/10.1186/s40635-022-00459-2 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2022 1 08 07 |
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10.1186/s40635-022-00459-2 doi (DE-627)SPR047523158 (SPR)s40635-022-00459-2-e DE-627 ger DE-627 rakwb eng Patel, Nathan T. P. verfasserin (orcid)0000-0001-5230-0767 aut Closed-loop automated critical care as proof-of-concept study for resuscitation in a swine model of ischemia–reperfusion injury 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Background Volume expansion and vasopressors for the treatment of shock is an intensive process that requires frequent assessments and adjustments. Strict blood pressure goals in multiple physiologic states of shock (traumatic brain injury, sepsis, and hemorrhagic) have been associated with improved outcomes. The availability of continuous physiologic data is amenable to closed-loop automated critical care to improve goal-directed resuscitation. Methods Five adult swine were anesthetized and subjected to a controlled 30% estimated total blood volume hemorrhage followed by 30 min of complete supra-celiac aortic occlusion and then autotransfusion back to euvolemia with removal of aortic balloon. The animals underwent closed-loop critical care for 255 min after removal of the endovascular aortic balloon. The closed-loop critical care algorithm used proximal aortic pressure and central venous pressure as physiologic input data. The algorithm had the option to provide programmatic control of pumps for titration of vasopressors and weight-based crystalloid boluses (5 ml/kg) to maintain a mean arterial pressure between 60 and 70 mmHg. Results During the 255 min of critical care the animals experienced hypotension (< 60 mmHg) 15.3% (interquartile range: 8.6–16.9%), hypertension (> 70 mmHg) 7.7% (interquartile range: 6.7–9.4%), and normotension (60–70 mmHg) 76.9% (interquartile range: 76.5–81.2%) of the time. Excluding the first 60 min of the critical care phase the animals experienced hypotension 1.0% (interquartile range: 0.5–6.7%) of the time. Median intervention rate was 8.47 interventions per hour (interquartile range: 7.8–9.2 interventions per hour). The proportion of interventions was 61.5% (interquartile range: 61.1–66.7%) weight-based crystalloid boluses and 38.5% (interquartile range: 33.3–38.9%) titration of vasopressors. Conclusion This autonomous critical care platform uses critical care adjuncts in an ischemia–reperfusion injury model, utilizing goal-directed closed-loop critical care algorithm and device actuation. This description highlights the potential for this approach to deliver nuanced critical care in the ICU environment, thereby optimizing resuscitative efforts and expanding capabilities through cognitive offloading. Future efforts will focus on optimizing this platform through comparative studies of inputs, therapies, and comparison to manual critical care. Closed-loop (dpeaa)DE-He213 Critical care (dpeaa)DE-He213 Automated (dpeaa)DE-He213 Ischemia reperfusion (dpeaa)DE-He213 Swine (dpeaa)DE-He213 Goenaga-Diaz, Eduardo J. aut Lane, Magan R. aut Austin Johnson, M. aut Neff, Lucas P. aut Williams, Timothy K. aut Enthalten in Intensive Care Medicine Experimental Berlin : SpringerOpen, 2013 10(2022), 1 vom: 08. Juli (DE-627)771394640 (DE-600)2740385-3 2197-425X nnns volume:10 year:2022 number:1 day:08 month:07 https://dx.doi.org/10.1186/s40635-022-00459-2 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2022 1 08 07 |
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10.1186/s40635-022-00459-2 doi (DE-627)SPR047523158 (SPR)s40635-022-00459-2-e DE-627 ger DE-627 rakwb eng Patel, Nathan T. P. verfasserin (orcid)0000-0001-5230-0767 aut Closed-loop automated critical care as proof-of-concept study for resuscitation in a swine model of ischemia–reperfusion injury 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Background Volume expansion and vasopressors for the treatment of shock is an intensive process that requires frequent assessments and adjustments. Strict blood pressure goals in multiple physiologic states of shock (traumatic brain injury, sepsis, and hemorrhagic) have been associated with improved outcomes. The availability of continuous physiologic data is amenable to closed-loop automated critical care to improve goal-directed resuscitation. Methods Five adult swine were anesthetized and subjected to a controlled 30% estimated total blood volume hemorrhage followed by 30 min of complete supra-celiac aortic occlusion and then autotransfusion back to euvolemia with removal of aortic balloon. The animals underwent closed-loop critical care for 255 min after removal of the endovascular aortic balloon. The closed-loop critical care algorithm used proximal aortic pressure and central venous pressure as physiologic input data. The algorithm had the option to provide programmatic control of pumps for titration of vasopressors and weight-based crystalloid boluses (5 ml/kg) to maintain a mean arterial pressure between 60 and 70 mmHg. Results During the 255 min of critical care the animals experienced hypotension (< 60 mmHg) 15.3% (interquartile range: 8.6–16.9%), hypertension (> 70 mmHg) 7.7% (interquartile range: 6.7–9.4%), and normotension (60–70 mmHg) 76.9% (interquartile range: 76.5–81.2%) of the time. Excluding the first 60 min of the critical care phase the animals experienced hypotension 1.0% (interquartile range: 0.5–6.7%) of the time. Median intervention rate was 8.47 interventions per hour (interquartile range: 7.8–9.2 interventions per hour). The proportion of interventions was 61.5% (interquartile range: 61.1–66.7%) weight-based crystalloid boluses and 38.5% (interquartile range: 33.3–38.9%) titration of vasopressors. Conclusion This autonomous critical care platform uses critical care adjuncts in an ischemia–reperfusion injury model, utilizing goal-directed closed-loop critical care algorithm and device actuation. This description highlights the potential for this approach to deliver nuanced critical care in the ICU environment, thereby optimizing resuscitative efforts and expanding capabilities through cognitive offloading. Future efforts will focus on optimizing this platform through comparative studies of inputs, therapies, and comparison to manual critical care. Closed-loop (dpeaa)DE-He213 Critical care (dpeaa)DE-He213 Automated (dpeaa)DE-He213 Ischemia reperfusion (dpeaa)DE-He213 Swine (dpeaa)DE-He213 Goenaga-Diaz, Eduardo J. aut Lane, Magan R. aut Austin Johnson, M. aut Neff, Lucas P. aut Williams, Timothy K. aut Enthalten in Intensive Care Medicine Experimental Berlin : SpringerOpen, 2013 10(2022), 1 vom: 08. Juli (DE-627)771394640 (DE-600)2740385-3 2197-425X nnns volume:10 year:2022 number:1 day:08 month:07 https://dx.doi.org/10.1186/s40635-022-00459-2 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2022 1 08 07 |
allfieldsGer |
10.1186/s40635-022-00459-2 doi (DE-627)SPR047523158 (SPR)s40635-022-00459-2-e DE-627 ger DE-627 rakwb eng Patel, Nathan T. P. verfasserin (orcid)0000-0001-5230-0767 aut Closed-loop automated critical care as proof-of-concept study for resuscitation in a swine model of ischemia–reperfusion injury 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Background Volume expansion and vasopressors for the treatment of shock is an intensive process that requires frequent assessments and adjustments. Strict blood pressure goals in multiple physiologic states of shock (traumatic brain injury, sepsis, and hemorrhagic) have been associated with improved outcomes. The availability of continuous physiologic data is amenable to closed-loop automated critical care to improve goal-directed resuscitation. Methods Five adult swine were anesthetized and subjected to a controlled 30% estimated total blood volume hemorrhage followed by 30 min of complete supra-celiac aortic occlusion and then autotransfusion back to euvolemia with removal of aortic balloon. The animals underwent closed-loop critical care for 255 min after removal of the endovascular aortic balloon. The closed-loop critical care algorithm used proximal aortic pressure and central venous pressure as physiologic input data. The algorithm had the option to provide programmatic control of pumps for titration of vasopressors and weight-based crystalloid boluses (5 ml/kg) to maintain a mean arterial pressure between 60 and 70 mmHg. Results During the 255 min of critical care the animals experienced hypotension (< 60 mmHg) 15.3% (interquartile range: 8.6–16.9%), hypertension (> 70 mmHg) 7.7% (interquartile range: 6.7–9.4%), and normotension (60–70 mmHg) 76.9% (interquartile range: 76.5–81.2%) of the time. Excluding the first 60 min of the critical care phase the animals experienced hypotension 1.0% (interquartile range: 0.5–6.7%) of the time. Median intervention rate was 8.47 interventions per hour (interquartile range: 7.8–9.2 interventions per hour). The proportion of interventions was 61.5% (interquartile range: 61.1–66.7%) weight-based crystalloid boluses and 38.5% (interquartile range: 33.3–38.9%) titration of vasopressors. Conclusion This autonomous critical care platform uses critical care adjuncts in an ischemia–reperfusion injury model, utilizing goal-directed closed-loop critical care algorithm and device actuation. This description highlights the potential for this approach to deliver nuanced critical care in the ICU environment, thereby optimizing resuscitative efforts and expanding capabilities through cognitive offloading. Future efforts will focus on optimizing this platform through comparative studies of inputs, therapies, and comparison to manual critical care. Closed-loop (dpeaa)DE-He213 Critical care (dpeaa)DE-He213 Automated (dpeaa)DE-He213 Ischemia reperfusion (dpeaa)DE-He213 Swine (dpeaa)DE-He213 Goenaga-Diaz, Eduardo J. aut Lane, Magan R. aut Austin Johnson, M. aut Neff, Lucas P. aut Williams, Timothy K. aut Enthalten in Intensive Care Medicine Experimental Berlin : SpringerOpen, 2013 10(2022), 1 vom: 08. Juli (DE-627)771394640 (DE-600)2740385-3 2197-425X nnns volume:10 year:2022 number:1 day:08 month:07 https://dx.doi.org/10.1186/s40635-022-00459-2 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2022 1 08 07 |
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10.1186/s40635-022-00459-2 doi (DE-627)SPR047523158 (SPR)s40635-022-00459-2-e DE-627 ger DE-627 rakwb eng Patel, Nathan T. P. verfasserin (orcid)0000-0001-5230-0767 aut Closed-loop automated critical care as proof-of-concept study for resuscitation in a swine model of ischemia–reperfusion injury 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Background Volume expansion and vasopressors for the treatment of shock is an intensive process that requires frequent assessments and adjustments. Strict blood pressure goals in multiple physiologic states of shock (traumatic brain injury, sepsis, and hemorrhagic) have been associated with improved outcomes. The availability of continuous physiologic data is amenable to closed-loop automated critical care to improve goal-directed resuscitation. Methods Five adult swine were anesthetized and subjected to a controlled 30% estimated total blood volume hemorrhage followed by 30 min of complete supra-celiac aortic occlusion and then autotransfusion back to euvolemia with removal of aortic balloon. The animals underwent closed-loop critical care for 255 min after removal of the endovascular aortic balloon. The closed-loop critical care algorithm used proximal aortic pressure and central venous pressure as physiologic input data. The algorithm had the option to provide programmatic control of pumps for titration of vasopressors and weight-based crystalloid boluses (5 ml/kg) to maintain a mean arterial pressure between 60 and 70 mmHg. Results During the 255 min of critical care the animals experienced hypotension (< 60 mmHg) 15.3% (interquartile range: 8.6–16.9%), hypertension (> 70 mmHg) 7.7% (interquartile range: 6.7–9.4%), and normotension (60–70 mmHg) 76.9% (interquartile range: 76.5–81.2%) of the time. Excluding the first 60 min of the critical care phase the animals experienced hypotension 1.0% (interquartile range: 0.5–6.7%) of the time. Median intervention rate was 8.47 interventions per hour (interquartile range: 7.8–9.2 interventions per hour). The proportion of interventions was 61.5% (interquartile range: 61.1–66.7%) weight-based crystalloid boluses and 38.5% (interquartile range: 33.3–38.9%) titration of vasopressors. Conclusion This autonomous critical care platform uses critical care adjuncts in an ischemia–reperfusion injury model, utilizing goal-directed closed-loop critical care algorithm and device actuation. This description highlights the potential for this approach to deliver nuanced critical care in the ICU environment, thereby optimizing resuscitative efforts and expanding capabilities through cognitive offloading. Future efforts will focus on optimizing this platform through comparative studies of inputs, therapies, and comparison to manual critical care. Closed-loop (dpeaa)DE-He213 Critical care (dpeaa)DE-He213 Automated (dpeaa)DE-He213 Ischemia reperfusion (dpeaa)DE-He213 Swine (dpeaa)DE-He213 Goenaga-Diaz, Eduardo J. aut Lane, Magan R. aut Austin Johnson, M. aut Neff, Lucas P. aut Williams, Timothy K. aut Enthalten in Intensive Care Medicine Experimental Berlin : SpringerOpen, 2013 10(2022), 1 vom: 08. Juli (DE-627)771394640 (DE-600)2740385-3 2197-425X nnns volume:10 year:2022 number:1 day:08 month:07 https://dx.doi.org/10.1186/s40635-022-00459-2 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2022 1 08 07 |
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Closed-loop automated critical care as proof-of-concept study for resuscitation in a swine model of ischemia–reperfusion injury |
abstract |
Background Volume expansion and vasopressors for the treatment of shock is an intensive process that requires frequent assessments and adjustments. Strict blood pressure goals in multiple physiologic states of shock (traumatic brain injury, sepsis, and hemorrhagic) have been associated with improved outcomes. The availability of continuous physiologic data is amenable to closed-loop automated critical care to improve goal-directed resuscitation. Methods Five adult swine were anesthetized and subjected to a controlled 30% estimated total blood volume hemorrhage followed by 30 min of complete supra-celiac aortic occlusion and then autotransfusion back to euvolemia with removal of aortic balloon. The animals underwent closed-loop critical care for 255 min after removal of the endovascular aortic balloon. The closed-loop critical care algorithm used proximal aortic pressure and central venous pressure as physiologic input data. The algorithm had the option to provide programmatic control of pumps for titration of vasopressors and weight-based crystalloid boluses (5 ml/kg) to maintain a mean arterial pressure between 60 and 70 mmHg. Results During the 255 min of critical care the animals experienced hypotension (< 60 mmHg) 15.3% (interquartile range: 8.6–16.9%), hypertension (> 70 mmHg) 7.7% (interquartile range: 6.7–9.4%), and normotension (60–70 mmHg) 76.9% (interquartile range: 76.5–81.2%) of the time. Excluding the first 60 min of the critical care phase the animals experienced hypotension 1.0% (interquartile range: 0.5–6.7%) of the time. Median intervention rate was 8.47 interventions per hour (interquartile range: 7.8–9.2 interventions per hour). The proportion of interventions was 61.5% (interquartile range: 61.1–66.7%) weight-based crystalloid boluses and 38.5% (interquartile range: 33.3–38.9%) titration of vasopressors. Conclusion This autonomous critical care platform uses critical care adjuncts in an ischemia–reperfusion injury model, utilizing goal-directed closed-loop critical care algorithm and device actuation. This description highlights the potential for this approach to deliver nuanced critical care in the ICU environment, thereby optimizing resuscitative efforts and expanding capabilities through cognitive offloading. Future efforts will focus on optimizing this platform through comparative studies of inputs, therapies, and comparison to manual critical care. © The Author(s) 2022 |
abstractGer |
Background Volume expansion and vasopressors for the treatment of shock is an intensive process that requires frequent assessments and adjustments. Strict blood pressure goals in multiple physiologic states of shock (traumatic brain injury, sepsis, and hemorrhagic) have been associated with improved outcomes. The availability of continuous physiologic data is amenable to closed-loop automated critical care to improve goal-directed resuscitation. Methods Five adult swine were anesthetized and subjected to a controlled 30% estimated total blood volume hemorrhage followed by 30 min of complete supra-celiac aortic occlusion and then autotransfusion back to euvolemia with removal of aortic balloon. The animals underwent closed-loop critical care for 255 min after removal of the endovascular aortic balloon. The closed-loop critical care algorithm used proximal aortic pressure and central venous pressure as physiologic input data. The algorithm had the option to provide programmatic control of pumps for titration of vasopressors and weight-based crystalloid boluses (5 ml/kg) to maintain a mean arterial pressure between 60 and 70 mmHg. Results During the 255 min of critical care the animals experienced hypotension (< 60 mmHg) 15.3% (interquartile range: 8.6–16.9%), hypertension (> 70 mmHg) 7.7% (interquartile range: 6.7–9.4%), and normotension (60–70 mmHg) 76.9% (interquartile range: 76.5–81.2%) of the time. Excluding the first 60 min of the critical care phase the animals experienced hypotension 1.0% (interquartile range: 0.5–6.7%) of the time. Median intervention rate was 8.47 interventions per hour (interquartile range: 7.8–9.2 interventions per hour). The proportion of interventions was 61.5% (interquartile range: 61.1–66.7%) weight-based crystalloid boluses and 38.5% (interquartile range: 33.3–38.9%) titration of vasopressors. Conclusion This autonomous critical care platform uses critical care adjuncts in an ischemia–reperfusion injury model, utilizing goal-directed closed-loop critical care algorithm and device actuation. This description highlights the potential for this approach to deliver nuanced critical care in the ICU environment, thereby optimizing resuscitative efforts and expanding capabilities through cognitive offloading. Future efforts will focus on optimizing this platform through comparative studies of inputs, therapies, and comparison to manual critical care. © The Author(s) 2022 |
abstract_unstemmed |
Background Volume expansion and vasopressors for the treatment of shock is an intensive process that requires frequent assessments and adjustments. Strict blood pressure goals in multiple physiologic states of shock (traumatic brain injury, sepsis, and hemorrhagic) have been associated with improved outcomes. The availability of continuous physiologic data is amenable to closed-loop automated critical care to improve goal-directed resuscitation. Methods Five adult swine were anesthetized and subjected to a controlled 30% estimated total blood volume hemorrhage followed by 30 min of complete supra-celiac aortic occlusion and then autotransfusion back to euvolemia with removal of aortic balloon. The animals underwent closed-loop critical care for 255 min after removal of the endovascular aortic balloon. The closed-loop critical care algorithm used proximal aortic pressure and central venous pressure as physiologic input data. The algorithm had the option to provide programmatic control of pumps for titration of vasopressors and weight-based crystalloid boluses (5 ml/kg) to maintain a mean arterial pressure between 60 and 70 mmHg. Results During the 255 min of critical care the animals experienced hypotension (< 60 mmHg) 15.3% (interquartile range: 8.6–16.9%), hypertension (> 70 mmHg) 7.7% (interquartile range: 6.7–9.4%), and normotension (60–70 mmHg) 76.9% (interquartile range: 76.5–81.2%) of the time. Excluding the first 60 min of the critical care phase the animals experienced hypotension 1.0% (interquartile range: 0.5–6.7%) of the time. Median intervention rate was 8.47 interventions per hour (interquartile range: 7.8–9.2 interventions per hour). The proportion of interventions was 61.5% (interquartile range: 61.1–66.7%) weight-based crystalloid boluses and 38.5% (interquartile range: 33.3–38.9%) titration of vasopressors. Conclusion This autonomous critical care platform uses critical care adjuncts in an ischemia–reperfusion injury model, utilizing goal-directed closed-loop critical care algorithm and device actuation. This description highlights the potential for this approach to deliver nuanced critical care in the ICU environment, thereby optimizing resuscitative efforts and expanding capabilities through cognitive offloading. Future efforts will focus on optimizing this platform through comparative studies of inputs, therapies, and comparison to manual critical care. © The Author(s) 2022 |
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title_short |
Closed-loop automated critical care as proof-of-concept study for resuscitation in a swine model of ischemia–reperfusion injury |
url |
https://dx.doi.org/10.1186/s40635-022-00459-2 |
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Goenaga-Diaz, Eduardo J. Lane, Magan R. Austin Johnson, M. Neff, Lucas P. Williams, Timothy K. |
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up_date |
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