Background: The International Classification of Diseases, Ninth Edition, Clinical Modification (ICD-9-CM) Injury Severity Score (ICISS) is a risk adjustment model when injuries are recorded using ICD-9-CM coding. The trauma mortality prediction model (TMPM-ICD9) provides better calibration and discrimination compared with ICISS and injury severity score (ISS). Though TMPM-ICD9 is statistically rigorous, it is not precise enough mathematically and has the tendency to overestimate injury severity. The purpose of this study is to develop a new ICD-10-CM injury model which estimates injury severities for every injury in the ICD-10-CM lexicon by a combination of rigorous statistical probit models and mathematical properties and improves the prediction accuracy.
Methods: We developed an injury mortality prediction (IMP-ICDX) using data of 794,098 patients admitted to 738 hospitals in the National Trauma Data Bank from 2015 to 2016. Empiric measures of severity for each of the trauma ICD-10-CM codes were estimated using a weighted median death probability (WMDP) measurement and then used as the basis for IMP-ICDX. ISS (version 2005) and the single worst injury (SWI) model were re-estimated. The performance of each of these models was compared by using the area under the receiver operating characteristic (AUC), the Hosmer-Lemeshow (HL) statistic, and the Akaike information criterion statistic.
Results: IMP-ICDX exhibits significantly better discrimination (AUC, 0.893, and 95% confidence interval (CI), 0.887 to 0.898; AUC, 0.853, and 95% CI, 0.846 to 0.860; and AUC, 0.886, and 95% CI, 0.881 to 0.892) and calibration (HL, 68, and 95% CI, 36 to 98; HL, 252, and 95% CI, 191 to 310; and HL, 92, and 95% CI, 53 to 128) compared with ISS and SWI. All models were improved after the extension of age, gender, and injury mechanism, but the augmented IMP-ICDX still dominated ISS and SWI by every performance.
Conclusions: The IMP-ICDX has a better discrimination and calibration compared to ISS. Therefore, we believe that IMP-ICDX could be a new viable trauma research assessment method.
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http://dx.doi.org/10.1186/s13017-019-0265-y | DOI Listing |
Anesth Analg
February 2025
SC Terapia Intensiva Neurochirurgica, Ospedale San Carlo Borromeo, ASST Santi Paolo e Carlo, Milano, Italy.
Background: Computed tomography (CT)-derived low muscle mass is associated with adverse outcomes in critically ill patients. Muscle ultrasound is a promising strategy for quantitating muscle mass. We evaluated the association between baseline ultrasound rectus femoris cross-sectional area (RF-CSA) and intensive care unit (ICU) mortality.
View Article and Find Full Text PDFGeroscience
January 2025
Department of Emergency Medicine, IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy.
As the elderly population expands, enhancing emergency department (ED) care by assessing frailty becomes increasingly vital. To address this, we developed a novel electronic Frailty Index (eFI) from ED health records, specifically designed to assess frailty and predict hospitalization, in-hospital mortality, ICU admissions, and 30-day ED readmissions. This retrospective, single-center study included patients 65 years old or older who presented to the ED of IRCCS Humanitas Research Hospital in Milan, Italy, between January 2015 and December 2019.
View Article and Find Full Text PDFBackground And Aims: The importance of risk stratification in patients with chest pain extends beyond diagnosis and immediate treatment. This study sought to evaluate the prognostic value of electrocardiogram feature-based machine learning models to risk-stratify all-cause mortality in those with chest pain.
Methods: This was a prospective observational cohort study of consecutive, non-traumatic patients with chest pain.
Eur Heart J
January 2025
Department of Cardiovascular Diseases, Mayo Clinic Minnesota, 200 1st St SW, Rochester, MN 55901, USA.
Ann Med
December 2025
Institute of Clinical Virology, Department of Infectious Diseases, The Second Affiliated Hospital of Anhui Medical University, Hefei, China.
Objective: We aimed at identifying acute phase biomarkers in Severe Fever with Thrombocytopenia Syndrome (SFTS), and to establish a model to predict mortality outcomes.
Methods: A retrospective analysis was conducted on multicenter clinical data. Group-based trajectory modeling (GBTM) was utilized to demonstrate the overall trend of laboratory indicators and their correlation with mortality.
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