Development and Validation of the Mortality Risk for Trauma Comorbidity Index
Pourquoi ce travail est dans la base
Une base qui oublie comment elle a trouvé un travail ne peut pas être vérifiée. Voici les voies qui ont admis celui-ci.
Notice bibliographique
Résumé
OBJECTIVE: The aim of this study was to develop and validate a comorbidity index to predict the risk of mortality associated with chronic health conditions following a traumatic injury. SUMMARY BACKGROUND DATA: Currently available comorbidity adjustment tools do not account for certain chronic conditions, which may influence outcome following traumatic injury or they have not been fully validated for trauma. Controlling for comorbidity in trauma patients is becoming increasingly important as the population ages and elderly patients are more active, as well as to adjust for bias in trauma mortality studies. METHODS: Cohort study using data from the National Study on the Costs and Outcome of Trauma. Subject pool (N = 4644/Weighted Number = 14,069) was randomly divided in half; the first half of subjects was used to derive the risk scale, the second to validate the instrument. To construct the Mortality Risk Score for Trauma (MoRT), univariate analysis and odds ratios were performed to determine relative risk of mortality at hospital discharge comparing those persons with a comorbid condition to those without. Conditions significantly associated with mortality (P < 0.05) were included in the multivariate model. The variables in the final model were used to build the MoRT. The predictive ability of the MoRT and the Charlson Comorbidity Index (CCI) for discharge and 1-year mortality were estimated using the c-statistic in the validation sample. RESULTS: Six comorbidity factors were independently associated with the risk of mortality and formed the basis for the MoRT: severe liver disease, myocardial infarction, cerebrovascular disease, cardiac arrhythmias, dementia, and depression. The MoRT had a similar overall discrimination as the CCI for mortality at hospital discharge in injured adults (c-statistic: 0.56 vs. 0.56) although neither by itself performed well. The addition of age and gender improved the predictive ability of the MoRT (0.59; 95% CI: 0.56, 0.62) and the CCI (0.59; 0.56, 0.62). Similar results were seen at 1-year postinjury. The further addition of Injury Severity Score significantly improved the predictive ability of the MoRT (0.77, 95% CI: 0.74, 0.79) and the CCI (0.77, 95% CI: 0.75, 0.80). CONCLUSIONS: The MoRTs primary advantage over current instruments is its parsimony, containing only 6 items. In the present study, the comorbid conditions found to be predictive of mortality had some overlap with the CCI, but this study identified 2 novel predictors: cardiac arrhythmias and depression. Inclusion and reporting of these items within trauma registries would therefore be an important step to allow further validation and use of the MoRT.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle