How to detect a polytrauma patient at risk of complications: A validation and database analysis of four published scales.
INTRODUCTION:Early accurate assessment of the clinical status of severely injured patients is crucial for guiding the surgical treatment strategy. Several scales are available to differentiate between risk categories. They vary between expert recommendations and scores developed on the basis of pati...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2020-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0228082 |
| _version_ | 1819260289370030080 |
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| author | Sascha Halvachizadeh Larissa Baradaran Paolo Cinelli Roman Pfeifer Kai Sprengel Hans-Christoph Pape |
| author_facet | Sascha Halvachizadeh Larissa Baradaran Paolo Cinelli Roman Pfeifer Kai Sprengel Hans-Christoph Pape |
| author_sort | Sascha Halvachizadeh |
| collection | DOAJ |
| description | INTRODUCTION:Early accurate assessment of the clinical status of severely injured patients is crucial for guiding the surgical treatment strategy. Several scales are available to differentiate between risk categories. They vary between expert recommendations and scores developed on the basis of patient data (level II). We compared four established scoring systems in regard to their predictive abilities for early (e.g., hemorrhage-induced mortality) versus late (Multiple Organ Failure (MOF), sepsis, late death) in-hospital complications. METHODS:A database from a level I trauma center was used. The inclusion criteria implied an injury severity score (ISS) of ≥16 points, primary admission, and a complete data set from admission to hospital-day 21. The following four scales were tested: the clinical grading scale (CGS; covers acidosis, shock, coagulation, and soft tissue injuries), the modified clinical grading scale (mCGS; covers CGS with modifications), the polytrauma grading score (PTGS; covers shock, coagulation, and ISS), and the early appropriate care protocol (EAC; covers acid-base changes). Admission values were selected from each scale and the following endpoints were compared: mortality, pneumonia, sepsis, death from hemorrhagic shock, and multiple organ failure. STATISTICS:Shapiro-Wilk test for normal distribution, Pearson Chi square, odds ratios (OR) for all endpoints, 95% confidence intervals. Fitted, generalized linear models were used for prediction analysis. Krippendorff was used for comparison of CGS and mCGS. Alpha set at 0.05. RESULTS:In total, 3668 severely injured patients were included (mean age, 45.8±20 years; mean ISS, 28.2±15.1 points; incidence of pneumonia, 19.0%; incidence of sepsis, 14.9%; death from hem. shock, 4.1%; death from multiple organ failure (MOF), 1.9%; mortality rate, 26.8%). Our data show distinct differences in the prediction of complications, including mortality, for these scores (OR ranging from 0.5 to 9.1). The PTGS demonstrated the highest predictive value for any late complication (OR = 2.0), sepsis (OR = 2.6, p = 0.05), or pneumonia (OR = 2.0, p = 0.2). The EAC demonstrated good prediction for hemorrhage-induced early mortality (OR = 7.1, p<0.0001), but did not predict late complications (sepsis, OR = 0.8 and p = 0.52; pneumonia, OR = 1.1 and p = 0.7) CGS and mCGS are not comparable and should not be used interchangeably (Krippendorff α = 0.045). CONCLUSION:Our data show that prediction of complications is more precise after using values that covers different physiological systems (coagulation, hemorrhage, acid-base changes, and soft tissue damage) when compared with using values of only one physiological system (e.g., acidosis). When acid-base changes alone were tested in terms of complications, they were predictive of complications within 72 hours but failed to predict late complications. These findings should be considered when performing early assessment of trauma patients or for the development of new scores. |
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| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-23T19:23:32Z |
| publishDate | 2020-01-01 |
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| spelling | doaj.art-0af09fb8d25a443f9e98015048e2e8952022-12-21T17:34:06ZengPublic Library of Science (PLoS)PLoS ONE1932-62032020-01-01151e022808210.1371/journal.pone.0228082How to detect a polytrauma patient at risk of complications: A validation and database analysis of four published scales.Sascha HalvachizadehLarissa BaradaranPaolo CinelliRoman PfeiferKai SprengelHans-Christoph PapeINTRODUCTION:Early accurate assessment of the clinical status of severely injured patients is crucial for guiding the surgical treatment strategy. Several scales are available to differentiate between risk categories. They vary between expert recommendations and scores developed on the basis of patient data (level II). We compared four established scoring systems in regard to their predictive abilities for early (e.g., hemorrhage-induced mortality) versus late (Multiple Organ Failure (MOF), sepsis, late death) in-hospital complications. METHODS:A database from a level I trauma center was used. The inclusion criteria implied an injury severity score (ISS) of ≥16 points, primary admission, and a complete data set from admission to hospital-day 21. The following four scales were tested: the clinical grading scale (CGS; covers acidosis, shock, coagulation, and soft tissue injuries), the modified clinical grading scale (mCGS; covers CGS with modifications), the polytrauma grading score (PTGS; covers shock, coagulation, and ISS), and the early appropriate care protocol (EAC; covers acid-base changes). Admission values were selected from each scale and the following endpoints were compared: mortality, pneumonia, sepsis, death from hemorrhagic shock, and multiple organ failure. STATISTICS:Shapiro-Wilk test for normal distribution, Pearson Chi square, odds ratios (OR) for all endpoints, 95% confidence intervals. Fitted, generalized linear models were used for prediction analysis. Krippendorff was used for comparison of CGS and mCGS. Alpha set at 0.05. RESULTS:In total, 3668 severely injured patients were included (mean age, 45.8±20 years; mean ISS, 28.2±15.1 points; incidence of pneumonia, 19.0%; incidence of sepsis, 14.9%; death from hem. shock, 4.1%; death from multiple organ failure (MOF), 1.9%; mortality rate, 26.8%). Our data show distinct differences in the prediction of complications, including mortality, for these scores (OR ranging from 0.5 to 9.1). The PTGS demonstrated the highest predictive value for any late complication (OR = 2.0), sepsis (OR = 2.6, p = 0.05), or pneumonia (OR = 2.0, p = 0.2). The EAC demonstrated good prediction for hemorrhage-induced early mortality (OR = 7.1, p<0.0001), but did not predict late complications (sepsis, OR = 0.8 and p = 0.52; pneumonia, OR = 1.1 and p = 0.7) CGS and mCGS are not comparable and should not be used interchangeably (Krippendorff α = 0.045). CONCLUSION:Our data show that prediction of complications is more precise after using values that covers different physiological systems (coagulation, hemorrhage, acid-base changes, and soft tissue damage) when compared with using values of only one physiological system (e.g., acidosis). When acid-base changes alone were tested in terms of complications, they were predictive of complications within 72 hours but failed to predict late complications. These findings should be considered when performing early assessment of trauma patients or for the development of new scores.https://doi.org/10.1371/journal.pone.0228082 |
| spellingShingle | Sascha Halvachizadeh Larissa Baradaran Paolo Cinelli Roman Pfeifer Kai Sprengel Hans-Christoph Pape How to detect a polytrauma patient at risk of complications: A validation and database analysis of four published scales. PLoS ONE |
| title | How to detect a polytrauma patient at risk of complications: A validation and database analysis of four published scales. |
| title_full | How to detect a polytrauma patient at risk of complications: A validation and database analysis of four published scales. |
| title_fullStr | How to detect a polytrauma patient at risk of complications: A validation and database analysis of four published scales. |
| title_full_unstemmed | How to detect a polytrauma patient at risk of complications: A validation and database analysis of four published scales. |
| title_short | How to detect a polytrauma patient at risk of complications: A validation and database analysis of four published scales. |
| title_sort | how to detect a polytrauma patient at risk of complications a validation and database analysis of four published scales |
| url | https://doi.org/10.1371/journal.pone.0228082 |
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