Risk Assessment
Purpose of Review The central question of preoperative assessment is not “What can be done?” but “What should be done and how?” Predicting a patient’s risk of unwanted outcomes is vital to answering this question. This review discusses risk prediction tools currently available and anticipates future...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Ajitsaria, Pragya [verfasserIn] Eissa, Sabry Z. [verfasserIn] Kerridge, Ross K. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Current anesthesiology reports - New York, NY : Springer, 2013, 8(2018), 1 vom: 30. Jan., Seite 1-8 |
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| Übergeordnetes Werk: |
volume:8 ; year:2018 ; number:1 ; day:30 ; month:01 ; pages:1-8 |
| Links: |
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| DOI / URN: |
10.1007/s40140-018-0246-9 |
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| Katalog-ID: |
SPR033029377 |
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| 520 | |a Purpose of Review The central question of preoperative assessment is not “What can be done?” but “What should be done and how?” Predicting a patient’s risk of unwanted outcomes is vital to answering this question. This review discusses risk prediction tools currently available and anticipates future developments. Recent Findings Simple, parsimonious risk scales and scores are being replaced by complex risk prediction models as high-capacity information systems become ubiquitous. The accuracy of risk estimation will be further increased by improved assessment of physical fitness, frailty, and incorporation of existing and novel biomarkers. However, the limitations of risk prediction for individual patient care must be recognized. Summary Risk prediction is transforming from clinical estimation to statistical science. Predictions should be used within the context of a patient’s baseline risk (life expectancy independent of surgery), personal circumstances, quality of life, their expectations and values, and consideration of outcomes that are meaningful for the patient. | ||
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| 700 | 1 | |a Kerridge, Ross K. |e verfasserin |4 aut | |
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2018 |
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10.1007/s40140-018-0246-9 doi (DE-627)SPR033029377 (SPR)s40140-018-0246-9-e DE-627 ger DE-627 rakwb eng 610 ASE Ajitsaria, Pragya verfasserin aut Risk Assessment 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose of Review The central question of preoperative assessment is not “What can be done?” but “What should be done and how?” Predicting a patient’s risk of unwanted outcomes is vital to answering this question. This review discusses risk prediction tools currently available and anticipates future developments. Recent Findings Simple, parsimonious risk scales and scores are being replaced by complex risk prediction models as high-capacity information systems become ubiquitous. The accuracy of risk estimation will be further increased by improved assessment of physical fitness, frailty, and incorporation of existing and novel biomarkers. However, the limitations of risk prediction for individual patient care must be recognized. Summary Risk prediction is transforming from clinical estimation to statistical science. Predictions should be used within the context of a patient’s baseline risk (life expectancy independent of surgery), personal circumstances, quality of life, their expectations and values, and consideration of outcomes that are meaningful for the patient. Preoperative evaluation (dpeaa)DE-He213 Preoperative risk assessment (dpeaa)DE-He213 Prehabilitation (dpeaa)DE-He213 Surgical risk assessment (dpeaa)DE-He213 Eissa, Sabry Z. verfasserin aut Kerridge, Ross K. verfasserin aut Enthalten in Current anesthesiology reports New York, NY : Springer, 2013 8(2018), 1 vom: 30. Jan., Seite 1-8 (DE-627)769568378 (DE-600)2735483-0 2167-6275 nnns volume:8 year:2018 number:1 day:30 month:01 pages:1-8 https://dx.doi.org/10.1007/s40140-018-0246-9 kostenfrei 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_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 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_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_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_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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 8 2018 1 30 01 1-8 |
| spelling |
10.1007/s40140-018-0246-9 doi (DE-627)SPR033029377 (SPR)s40140-018-0246-9-e DE-627 ger DE-627 rakwb eng 610 ASE Ajitsaria, Pragya verfasserin aut Risk Assessment 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose of Review The central question of preoperative assessment is not “What can be done?” but “What should be done and how?” Predicting a patient’s risk of unwanted outcomes is vital to answering this question. This review discusses risk prediction tools currently available and anticipates future developments. Recent Findings Simple, parsimonious risk scales and scores are being replaced by complex risk prediction models as high-capacity information systems become ubiquitous. The accuracy of risk estimation will be further increased by improved assessment of physical fitness, frailty, and incorporation of existing and novel biomarkers. However, the limitations of risk prediction for individual patient care must be recognized. Summary Risk prediction is transforming from clinical estimation to statistical science. Predictions should be used within the context of a patient’s baseline risk (life expectancy independent of surgery), personal circumstances, quality of life, their expectations and values, and consideration of outcomes that are meaningful for the patient. Preoperative evaluation (dpeaa)DE-He213 Preoperative risk assessment (dpeaa)DE-He213 Prehabilitation (dpeaa)DE-He213 Surgical risk assessment (dpeaa)DE-He213 Eissa, Sabry Z. verfasserin aut Kerridge, Ross K. verfasserin aut Enthalten in Current anesthesiology reports New York, NY : Springer, 2013 8(2018), 1 vom: 30. Jan., Seite 1-8 (DE-627)769568378 (DE-600)2735483-0 2167-6275 nnns volume:8 year:2018 number:1 day:30 month:01 pages:1-8 https://dx.doi.org/10.1007/s40140-018-0246-9 kostenfrei 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_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 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_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_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_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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 8 2018 1 30 01 1-8 |
| allfields_unstemmed |
10.1007/s40140-018-0246-9 doi (DE-627)SPR033029377 (SPR)s40140-018-0246-9-e DE-627 ger DE-627 rakwb eng 610 ASE Ajitsaria, Pragya verfasserin aut Risk Assessment 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose of Review The central question of preoperative assessment is not “What can be done?” but “What should be done and how?” Predicting a patient’s risk of unwanted outcomes is vital to answering this question. This review discusses risk prediction tools currently available and anticipates future developments. Recent Findings Simple, parsimonious risk scales and scores are being replaced by complex risk prediction models as high-capacity information systems become ubiquitous. The accuracy of risk estimation will be further increased by improved assessment of physical fitness, frailty, and incorporation of existing and novel biomarkers. However, the limitations of risk prediction for individual patient care must be recognized. Summary Risk prediction is transforming from clinical estimation to statistical science. Predictions should be used within the context of a patient’s baseline risk (life expectancy independent of surgery), personal circumstances, quality of life, their expectations and values, and consideration of outcomes that are meaningful for the patient. Preoperative evaluation (dpeaa)DE-He213 Preoperative risk assessment (dpeaa)DE-He213 Prehabilitation (dpeaa)DE-He213 Surgical risk assessment (dpeaa)DE-He213 Eissa, Sabry Z. verfasserin aut Kerridge, Ross K. verfasserin aut Enthalten in Current anesthesiology reports New York, NY : Springer, 2013 8(2018), 1 vom: 30. Jan., Seite 1-8 (DE-627)769568378 (DE-600)2735483-0 2167-6275 nnns volume:8 year:2018 number:1 day:30 month:01 pages:1-8 https://dx.doi.org/10.1007/s40140-018-0246-9 kostenfrei 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_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 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_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_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_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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 8 2018 1 30 01 1-8 |
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10.1007/s40140-018-0246-9 doi (DE-627)SPR033029377 (SPR)s40140-018-0246-9-e DE-627 ger DE-627 rakwb eng 610 ASE Ajitsaria, Pragya verfasserin aut Risk Assessment 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose of Review The central question of preoperative assessment is not “What can be done?” but “What should be done and how?” Predicting a patient’s risk of unwanted outcomes is vital to answering this question. This review discusses risk prediction tools currently available and anticipates future developments. Recent Findings Simple, parsimonious risk scales and scores are being replaced by complex risk prediction models as high-capacity information systems become ubiquitous. The accuracy of risk estimation will be further increased by improved assessment of physical fitness, frailty, and incorporation of existing and novel biomarkers. However, the limitations of risk prediction for individual patient care must be recognized. Summary Risk prediction is transforming from clinical estimation to statistical science. Predictions should be used within the context of a patient’s baseline risk (life expectancy independent of surgery), personal circumstances, quality of life, their expectations and values, and consideration of outcomes that are meaningful for the patient. Preoperative evaluation (dpeaa)DE-He213 Preoperative risk assessment (dpeaa)DE-He213 Prehabilitation (dpeaa)DE-He213 Surgical risk assessment (dpeaa)DE-He213 Eissa, Sabry Z. verfasserin aut Kerridge, Ross K. verfasserin aut Enthalten in Current anesthesiology reports New York, NY : Springer, 2013 8(2018), 1 vom: 30. Jan., Seite 1-8 (DE-627)769568378 (DE-600)2735483-0 2167-6275 nnns volume:8 year:2018 number:1 day:30 month:01 pages:1-8 https://dx.doi.org/10.1007/s40140-018-0246-9 kostenfrei 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_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 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_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_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_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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 8 2018 1 30 01 1-8 |
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Enthalten in Current anesthesiology reports 8(2018), 1 vom: 30. Jan., Seite 1-8 volume:8 year:2018 number:1 day:30 month:01 pages:1-8 |
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Enthalten in Current anesthesiology reports 8(2018), 1 vom: 30. Jan., Seite 1-8 volume:8 year:2018 number:1 day:30 month:01 pages:1-8 |
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Ajitsaria, Pragya @@aut@@ Eissa, Sabry Z. @@aut@@ Kerridge, Ross K. @@aut@@ |
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Purpose of Review The central question of preoperative assessment is not “What can be done?” but “What should be done and how?” Predicting a patient’s risk of unwanted outcomes is vital to answering this question. This review discusses risk prediction tools currently available and anticipates future developments. Recent Findings Simple, parsimonious risk scales and scores are being replaced by complex risk prediction models as high-capacity information systems become ubiquitous. The accuracy of risk estimation will be further increased by improved assessment of physical fitness, frailty, and incorporation of existing and novel biomarkers. However, the limitations of risk prediction for individual patient care must be recognized. Summary Risk prediction is transforming from clinical estimation to statistical science. Predictions should be used within the context of a patient’s baseline risk (life expectancy independent of surgery), personal circumstances, quality of life, their expectations and values, and consideration of outcomes that are meaningful for the patient. |
| abstractGer |
Purpose of Review The central question of preoperative assessment is not “What can be done?” but “What should be done and how?” Predicting a patient’s risk of unwanted outcomes is vital to answering this question. This review discusses risk prediction tools currently available and anticipates future developments. Recent Findings Simple, parsimonious risk scales and scores are being replaced by complex risk prediction models as high-capacity information systems become ubiquitous. The accuracy of risk estimation will be further increased by improved assessment of physical fitness, frailty, and incorporation of existing and novel biomarkers. However, the limitations of risk prediction for individual patient care must be recognized. Summary Risk prediction is transforming from clinical estimation to statistical science. Predictions should be used within the context of a patient’s baseline risk (life expectancy independent of surgery), personal circumstances, quality of life, their expectations and values, and consideration of outcomes that are meaningful for the patient. |
| abstract_unstemmed |
Purpose of Review The central question of preoperative assessment is not “What can be done?” but “What should be done and how?” Predicting a patient’s risk of unwanted outcomes is vital to answering this question. This review discusses risk prediction tools currently available and anticipates future developments. Recent Findings Simple, parsimonious risk scales and scores are being replaced by complex risk prediction models as high-capacity information systems become ubiquitous. The accuracy of risk estimation will be further increased by improved assessment of physical fitness, frailty, and incorporation of existing and novel biomarkers. However, the limitations of risk prediction for individual patient care must be recognized. Summary Risk prediction is transforming from clinical estimation to statistical science. Predictions should be used within the context of a patient’s baseline risk (life expectancy independent of surgery), personal circumstances, quality of life, their expectations and values, and consideration of outcomes that are meaningful for the patient. |
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This review discusses risk prediction tools currently available and anticipates future developments. Recent Findings Simple, parsimonious risk scales and scores are being replaced by complex risk prediction models as high-capacity information systems become ubiquitous. The accuracy of risk estimation will be further increased by improved assessment of physical fitness, frailty, and incorporation of existing and novel biomarkers. However, the limitations of risk prediction for individual patient care must be recognized. Summary Risk prediction is transforming from clinical estimation to statistical science. 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7.3997793 |