Predicting cardiac risk in noncardiac surgery: a narrative review
Abstract Risk stratification endeavors to categorize patients into groups based on the level of risk for each group. Improved perioperative screening tests using more sensitive cardiac biomarkers have revealed that about 68% of perioperative myocardial infarctions (MI) are asymptomatic and may only...
Ausführliche Beschreibung
Autor*in: |
Faloye, Abimbola O. [verfasserIn] Gebre, Melat A. [verfasserIn] Bechtel, Allison J. [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2020 |
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Übergeordnetes Werk: |
Enthalten in: Journal of anesthesia - Tokyo, 1987, 35(2020), 1 vom: 03. Nov., Seite 122-129 |
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Übergeordnetes Werk: |
volume:35 ; year:2020 ; number:1 ; day:03 ; month:11 ; pages:122-129 |
Links: |
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DOI / URN: |
10.1007/s00540-020-02868-7 |
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Katalog-ID: |
SPR042887313 |
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520 | |a Abstract Risk stratification endeavors to categorize patients into groups based on the level of risk for each group. Improved perioperative screening tests using more sensitive cardiac biomarkers have revealed that about 68% of perioperative myocardial infarctions (MI) are asymptomatic and may only be detected by routine postoperative screening with troponin measurements. This is important since myocardial injury not meeting criteria for myocardial infarction is associated with increased risk of 30-day mortality (Botto et al. in Anesthesiology 120:564–578, 2014). Traditional risk indices including the revised cardiac risk index (RCRI) and the myocardial infarction cardiac arrest (MICA) index were developed based on overt clinical signs of myocardial infarction and significantly underestimate adverse cardiac events. Recently, brain type natriuretic peptides (BNP) and its precursor n- terminal pro-brain type natriuretic peptide (nt-proBNP) have been shown to be powerful prognostic markers. Incorporating serum biomarkers into updated clinical risk indices is likely to improve their performance. Further studies are needed to determine appropriate clinical interventions to treat isolated elevations in cardiac troponin levels and further mitigate the increased risk of morbidity and mortality. The objective of this review is to summarize the current literature on the clinical diagnoses of perioperative myocardial injury in the setting of noncardiac surgery. | ||
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10.1007/s00540-020-02868-7 doi (DE-627)SPR042887313 (DE-599)SPRs00540-020-02868-7-e (SPR)s00540-020-02868-7-e DE-627 ger DE-627 rakwb eng 610 ASE 44.66 bkl Faloye, Abimbola O. verfasserin aut Predicting cardiac risk in noncardiac surgery: a narrative review 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Risk stratification endeavors to categorize patients into groups based on the level of risk for each group. Improved perioperative screening tests using more sensitive cardiac biomarkers have revealed that about 68% of perioperative myocardial infarctions (MI) are asymptomatic and may only be detected by routine postoperative screening with troponin measurements. This is important since myocardial injury not meeting criteria for myocardial infarction is associated with increased risk of 30-day mortality (Botto et al. in Anesthesiology 120:564–578, 2014). Traditional risk indices including the revised cardiac risk index (RCRI) and the myocardial infarction cardiac arrest (MICA) index were developed based on overt clinical signs of myocardial infarction and significantly underestimate adverse cardiac events. Recently, brain type natriuretic peptides (BNP) and its precursor n- terminal pro-brain type natriuretic peptide (nt-proBNP) have been shown to be powerful prognostic markers. Incorporating serum biomarkers into updated clinical risk indices is likely to improve their performance. Further studies are needed to determine appropriate clinical interventions to treat isolated elevations in cardiac troponin levels and further mitigate the increased risk of morbidity and mortality. The objective of this review is to summarize the current literature on the clinical diagnoses of perioperative myocardial injury in the setting of noncardiac surgery. Risk assessment (dpeaa)DE-He213 Cardiac risk assessment (dpeaa)DE-He213 Preoperative risk calculator (dpeaa)DE-He213 Gebre, Melat A. verfasserin aut Bechtel, Allison J. verfasserin aut Enthalten in Journal of anesthesia Tokyo, 1987 35(2020), 1 vom: 03. Nov., Seite 122-129 (DE-627)300185065 (DE-600)1481564-3 1438-8359 nnns volume:35 year:2020 number:1 day:03 month:11 pages:122-129 https://dx.doi.org/10.1007/s00540-020-02868-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_711 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.66 ASE AR 35 2020 1 03 11 122-129 |
spelling |
10.1007/s00540-020-02868-7 doi (DE-627)SPR042887313 (DE-599)SPRs00540-020-02868-7-e (SPR)s00540-020-02868-7-e DE-627 ger DE-627 rakwb eng 610 ASE 44.66 bkl Faloye, Abimbola O. verfasserin aut Predicting cardiac risk in noncardiac surgery: a narrative review 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Risk stratification endeavors to categorize patients into groups based on the level of risk for each group. Improved perioperative screening tests using more sensitive cardiac biomarkers have revealed that about 68% of perioperative myocardial infarctions (MI) are asymptomatic and may only be detected by routine postoperative screening with troponin measurements. This is important since myocardial injury not meeting criteria for myocardial infarction is associated with increased risk of 30-day mortality (Botto et al. in Anesthesiology 120:564–578, 2014). Traditional risk indices including the revised cardiac risk index (RCRI) and the myocardial infarction cardiac arrest (MICA) index were developed based on overt clinical signs of myocardial infarction and significantly underestimate adverse cardiac events. Recently, brain type natriuretic peptides (BNP) and its precursor n- terminal pro-brain type natriuretic peptide (nt-proBNP) have been shown to be powerful prognostic markers. Incorporating serum biomarkers into updated clinical risk indices is likely to improve their performance. Further studies are needed to determine appropriate clinical interventions to treat isolated elevations in cardiac troponin levels and further mitigate the increased risk of morbidity and mortality. The objective of this review is to summarize the current literature on the clinical diagnoses of perioperative myocardial injury in the setting of noncardiac surgery. Risk assessment (dpeaa)DE-He213 Cardiac risk assessment (dpeaa)DE-He213 Preoperative risk calculator (dpeaa)DE-He213 Gebre, Melat A. verfasserin aut Bechtel, Allison J. verfasserin aut Enthalten in Journal of anesthesia Tokyo, 1987 35(2020), 1 vom: 03. Nov., Seite 122-129 (DE-627)300185065 (DE-600)1481564-3 1438-8359 nnns volume:35 year:2020 number:1 day:03 month:11 pages:122-129 https://dx.doi.org/10.1007/s00540-020-02868-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_711 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.66 ASE AR 35 2020 1 03 11 122-129 |
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10.1007/s00540-020-02868-7 doi (DE-627)SPR042887313 (DE-599)SPRs00540-020-02868-7-e (SPR)s00540-020-02868-7-e DE-627 ger DE-627 rakwb eng 610 ASE 44.66 bkl Faloye, Abimbola O. verfasserin aut Predicting cardiac risk in noncardiac surgery: a narrative review 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Risk stratification endeavors to categorize patients into groups based on the level of risk for each group. Improved perioperative screening tests using more sensitive cardiac biomarkers have revealed that about 68% of perioperative myocardial infarctions (MI) are asymptomatic and may only be detected by routine postoperative screening with troponin measurements. This is important since myocardial injury not meeting criteria for myocardial infarction is associated with increased risk of 30-day mortality (Botto et al. in Anesthesiology 120:564–578, 2014). Traditional risk indices including the revised cardiac risk index (RCRI) and the myocardial infarction cardiac arrest (MICA) index were developed based on overt clinical signs of myocardial infarction and significantly underestimate adverse cardiac events. Recently, brain type natriuretic peptides (BNP) and its precursor n- terminal pro-brain type natriuretic peptide (nt-proBNP) have been shown to be powerful prognostic markers. Incorporating serum biomarkers into updated clinical risk indices is likely to improve their performance. Further studies are needed to determine appropriate clinical interventions to treat isolated elevations in cardiac troponin levels and further mitigate the increased risk of morbidity and mortality. The objective of this review is to summarize the current literature on the clinical diagnoses of perioperative myocardial injury in the setting of noncardiac surgery. Risk assessment (dpeaa)DE-He213 Cardiac risk assessment (dpeaa)DE-He213 Preoperative risk calculator (dpeaa)DE-He213 Gebre, Melat A. verfasserin aut Bechtel, Allison J. verfasserin aut Enthalten in Journal of anesthesia Tokyo, 1987 35(2020), 1 vom: 03. Nov., Seite 122-129 (DE-627)300185065 (DE-600)1481564-3 1438-8359 nnns volume:35 year:2020 number:1 day:03 month:11 pages:122-129 https://dx.doi.org/10.1007/s00540-020-02868-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_711 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.66 ASE AR 35 2020 1 03 11 122-129 |
allfieldsGer |
10.1007/s00540-020-02868-7 doi (DE-627)SPR042887313 (DE-599)SPRs00540-020-02868-7-e (SPR)s00540-020-02868-7-e DE-627 ger DE-627 rakwb eng 610 ASE 44.66 bkl Faloye, Abimbola O. verfasserin aut Predicting cardiac risk in noncardiac surgery: a narrative review 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Risk stratification endeavors to categorize patients into groups based on the level of risk for each group. Improved perioperative screening tests using more sensitive cardiac biomarkers have revealed that about 68% of perioperative myocardial infarctions (MI) are asymptomatic and may only be detected by routine postoperative screening with troponin measurements. This is important since myocardial injury not meeting criteria for myocardial infarction is associated with increased risk of 30-day mortality (Botto et al. in Anesthesiology 120:564–578, 2014). Traditional risk indices including the revised cardiac risk index (RCRI) and the myocardial infarction cardiac arrest (MICA) index were developed based on overt clinical signs of myocardial infarction and significantly underestimate adverse cardiac events. Recently, brain type natriuretic peptides (BNP) and its precursor n- terminal pro-brain type natriuretic peptide (nt-proBNP) have been shown to be powerful prognostic markers. Incorporating serum biomarkers into updated clinical risk indices is likely to improve their performance. Further studies are needed to determine appropriate clinical interventions to treat isolated elevations in cardiac troponin levels and further mitigate the increased risk of morbidity and mortality. The objective of this review is to summarize the current literature on the clinical diagnoses of perioperative myocardial injury in the setting of noncardiac surgery. Risk assessment (dpeaa)DE-He213 Cardiac risk assessment (dpeaa)DE-He213 Preoperative risk calculator (dpeaa)DE-He213 Gebre, Melat A. verfasserin aut Bechtel, Allison J. verfasserin aut Enthalten in Journal of anesthesia Tokyo, 1987 35(2020), 1 vom: 03. Nov., Seite 122-129 (DE-627)300185065 (DE-600)1481564-3 1438-8359 nnns volume:35 year:2020 number:1 day:03 month:11 pages:122-129 https://dx.doi.org/10.1007/s00540-020-02868-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_711 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.66 ASE AR 35 2020 1 03 11 122-129 |
allfieldsSound |
10.1007/s00540-020-02868-7 doi (DE-627)SPR042887313 (DE-599)SPRs00540-020-02868-7-e (SPR)s00540-020-02868-7-e DE-627 ger DE-627 rakwb eng 610 ASE 44.66 bkl Faloye, Abimbola O. verfasserin aut Predicting cardiac risk in noncardiac surgery: a narrative review 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Risk stratification endeavors to categorize patients into groups based on the level of risk for each group. Improved perioperative screening tests using more sensitive cardiac biomarkers have revealed that about 68% of perioperative myocardial infarctions (MI) are asymptomatic and may only be detected by routine postoperative screening with troponin measurements. This is important since myocardial injury not meeting criteria for myocardial infarction is associated with increased risk of 30-day mortality (Botto et al. in Anesthesiology 120:564–578, 2014). Traditional risk indices including the revised cardiac risk index (RCRI) and the myocardial infarction cardiac arrest (MICA) index were developed based on overt clinical signs of myocardial infarction and significantly underestimate adverse cardiac events. Recently, brain type natriuretic peptides (BNP) and its precursor n- terminal pro-brain type natriuretic peptide (nt-proBNP) have been shown to be powerful prognostic markers. Incorporating serum biomarkers into updated clinical risk indices is likely to improve their performance. Further studies are needed to determine appropriate clinical interventions to treat isolated elevations in cardiac troponin levels and further mitigate the increased risk of morbidity and mortality. The objective of this review is to summarize the current literature on the clinical diagnoses of perioperative myocardial injury in the setting of noncardiac surgery. Risk assessment (dpeaa)DE-He213 Cardiac risk assessment (dpeaa)DE-He213 Preoperative risk calculator (dpeaa)DE-He213 Gebre, Melat A. verfasserin aut Bechtel, Allison J. verfasserin aut Enthalten in Journal of anesthesia Tokyo, 1987 35(2020), 1 vom: 03. Nov., Seite 122-129 (DE-627)300185065 (DE-600)1481564-3 1438-8359 nnns volume:35 year:2020 number:1 day:03 month:11 pages:122-129 https://dx.doi.org/10.1007/s00540-020-02868-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_711 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.66 ASE AR 35 2020 1 03 11 122-129 |
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Faloye, Abimbola O. @@aut@@ Gebre, Melat A. @@aut@@ Bechtel, Allison J. @@aut@@ |
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Improved perioperative screening tests using more sensitive cardiac biomarkers have revealed that about 68% of perioperative myocardial infarctions (MI) are asymptomatic and may only be detected by routine postoperative screening with troponin measurements. This is important since myocardial injury not meeting criteria for myocardial infarction is associated with increased risk of 30-day mortality (Botto et al. in Anesthesiology 120:564–578, 2014). Traditional risk indices including the revised cardiac risk index (RCRI) and the myocardial infarction cardiac arrest (MICA) index were developed based on overt clinical signs of myocardial infarction and significantly underestimate adverse cardiac events. Recently, brain type natriuretic peptides (BNP) and its precursor n- terminal pro-brain type natriuretic peptide (nt-proBNP) have been shown to be powerful prognostic markers. Incorporating serum biomarkers into updated clinical risk indices is likely to improve their performance. Further studies are needed to determine appropriate clinical interventions to treat isolated elevations in cardiac troponin levels and further mitigate the increased risk of morbidity and mortality. 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Faloye, Abimbola O. |
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Faloye, Abimbola O. ddc 610 bkl 44.66 misc Risk assessment misc Cardiac risk assessment misc Preoperative risk calculator Predicting cardiac risk in noncardiac surgery: a narrative review |
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610 ASE 44.66 bkl Predicting cardiac risk in noncardiac surgery: a narrative review Risk assessment (dpeaa)DE-He213 Cardiac risk assessment (dpeaa)DE-He213 Preoperative risk calculator (dpeaa)DE-He213 |
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Predicting cardiac risk in noncardiac surgery: a narrative review |
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Predicting cardiac risk in noncardiac surgery: a narrative review |
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predicting cardiac risk in noncardiac surgery: a narrative review |
title_auth |
Predicting cardiac risk in noncardiac surgery: a narrative review |
abstract |
Abstract Risk stratification endeavors to categorize patients into groups based on the level of risk for each group. Improved perioperative screening tests using more sensitive cardiac biomarkers have revealed that about 68% of perioperative myocardial infarctions (MI) are asymptomatic and may only be detected by routine postoperative screening with troponin measurements. This is important since myocardial injury not meeting criteria for myocardial infarction is associated with increased risk of 30-day mortality (Botto et al. in Anesthesiology 120:564–578, 2014). Traditional risk indices including the revised cardiac risk index (RCRI) and the myocardial infarction cardiac arrest (MICA) index were developed based on overt clinical signs of myocardial infarction and significantly underestimate adverse cardiac events. Recently, brain type natriuretic peptides (BNP) and its precursor n- terminal pro-brain type natriuretic peptide (nt-proBNP) have been shown to be powerful prognostic markers. Incorporating serum biomarkers into updated clinical risk indices is likely to improve their performance. Further studies are needed to determine appropriate clinical interventions to treat isolated elevations in cardiac troponin levels and further mitigate the increased risk of morbidity and mortality. The objective of this review is to summarize the current literature on the clinical diagnoses of perioperative myocardial injury in the setting of noncardiac surgery. |
abstractGer |
Abstract Risk stratification endeavors to categorize patients into groups based on the level of risk for each group. Improved perioperative screening tests using more sensitive cardiac biomarkers have revealed that about 68% of perioperative myocardial infarctions (MI) are asymptomatic and may only be detected by routine postoperative screening with troponin measurements. This is important since myocardial injury not meeting criteria for myocardial infarction is associated with increased risk of 30-day mortality (Botto et al. in Anesthesiology 120:564–578, 2014). Traditional risk indices including the revised cardiac risk index (RCRI) and the myocardial infarction cardiac arrest (MICA) index were developed based on overt clinical signs of myocardial infarction and significantly underestimate adverse cardiac events. Recently, brain type natriuretic peptides (BNP) and its precursor n- terminal pro-brain type natriuretic peptide (nt-proBNP) have been shown to be powerful prognostic markers. Incorporating serum biomarkers into updated clinical risk indices is likely to improve their performance. Further studies are needed to determine appropriate clinical interventions to treat isolated elevations in cardiac troponin levels and further mitigate the increased risk of morbidity and mortality. The objective of this review is to summarize the current literature on the clinical diagnoses of perioperative myocardial injury in the setting of noncardiac surgery. |
abstract_unstemmed |
Abstract Risk stratification endeavors to categorize patients into groups based on the level of risk for each group. Improved perioperative screening tests using more sensitive cardiac biomarkers have revealed that about 68% of perioperative myocardial infarctions (MI) are asymptomatic and may only be detected by routine postoperative screening with troponin measurements. This is important since myocardial injury not meeting criteria for myocardial infarction is associated with increased risk of 30-day mortality (Botto et al. in Anesthesiology 120:564–578, 2014). Traditional risk indices including the revised cardiac risk index (RCRI) and the myocardial infarction cardiac arrest (MICA) index were developed based on overt clinical signs of myocardial infarction and significantly underestimate adverse cardiac events. Recently, brain type natriuretic peptides (BNP) and its precursor n- terminal pro-brain type natriuretic peptide (nt-proBNP) have been shown to be powerful prognostic markers. Incorporating serum biomarkers into updated clinical risk indices is likely to improve their performance. Further studies are needed to determine appropriate clinical interventions to treat isolated elevations in cardiac troponin levels and further mitigate the increased risk of morbidity and mortality. The objective of this review is to summarize the current literature on the clinical diagnoses of perioperative myocardial injury in the setting of noncardiac surgery. |
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title_short |
Predicting cardiac risk in noncardiac surgery: a narrative review |
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https://dx.doi.org/10.1007/s00540-020-02868-7 |
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Gebre, Melat A. Bechtel, Allison J. |
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up_date |
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|
score |
7.40059 |