Claudia Beaudoin

Performance of International Classification of Diseases-based injury severity measures used to predict in-hospital mortality: A systematic review and meta-analysis.

BACKGROUND The International Classification of Diseases (ICD) is the main classification system used for population-based injury surveillance activities but does not contain information on injury severity. ICD-based injury severity measures can be empirically derived or mapped, but no single approach has been formally recommended. This study aimed to compare the performance of ICD-based injury severity measures to predict in-hospital mortality among injury-related admissions. METHODS A systematic review and a meta-analysis were conducted. MEDLINE, EMBASE, and Global Health databases were searched from their inception through September 2014. Observational studies that assessed the performance of ICD-based injury severity measures to predict in-hospital mortality and reported discriminative ability using the area under a receiver operating characteristic curve (AUC) were included. Metrics of model performance were extracted. Pooled AUC were estimated under random-effects models. RESULTS Twenty-two eligible studies reported 72 assessments of discrimination on ICD-based injury severity measures. Reported AUC ranged from 0.681 to 0.958. Of the 72 assessments, 46 showed excellent (0.80 ⩽ AUC < 0.90) and 6 outstanding (AUC ≥ 0.90) discriminative ability. Pooled AUC for ICD-based Injury Severity Score (ICISS) based on the product of traditional survival proportions was significantly higher than measures based on ICD mapped to Abbreviated Injury Scale (AIS) scores (0.863 vs. 0.825 for ICDMAP-ISS [p = 0.005] and ICDMAP-NISS [p = 0.016]). Similar results were observed when studies were stratified by the type of data used (trauma registry or hospital discharge) or the provenance of survival proportions (internally or externally derived). However, among studies published after 2003 the Trauma Mortality Prediction Model based on ICD-9 codes (TMPM-9) demonstrated superior discriminative ability than ICISS using the product of traditional survival proportions (0.850 vs. 0.802, p = 0.002). Models generally showed poor calibration. CONCLUSION ICISS using the product of traditional survival proportions and TMPM-9 predict mortality more accurately than those mapped to AIS codes and should be preferred for describing injury severity when ICD is used to record injury diagnoses. LEVEL OF EVIDENCE Systematic review and meta-analysis, level III.

Performance of International Classification of Diseases–based injury severity measures used to predict in-hospital mortality and intensive care admission among traumatic brain-injured patients

BACKGROUND The International Classification of Diseases (ICD) is the main classification system used for population-based traumatic brain injury (TBI) surveillance activities but does not contain direct information on injury severity. International Classification of Diseases–based injury severity measures can be empirically derived or mapped to the Abbreviated Injury Scale, but no single approach has been formally recommended for TBI. OBJECTIVE The aim of this study was to compare the accuracy of different ICD-based injury severity measures for predicting in-hospital mortality and intensive care unit (ICU) admission in TBI patients. METHODS We conducted a population-based retrospective cohort study. We identified all patients 16 years or older with a TBI diagnosis who received acute care between April 1, 2006, and March 31, 2013, from the Quebec Hospital Discharge Database. The accuracy of five ICD-based injury severity measures for predicting mortality and ICU admission was compared using measures of discrimination (area under the receiver operating characteristic curve [AUC]) and calibration (calibration plot and the Hosmer-Lemeshow goodness-of-fit statistic). RESULTS Of 31,087 traumatic brain-injured patients in the study population, 9.0% died in hospital, and 34.4% were admitted to the ICU. Among ICD-based severity measures that were assessed, the multiplied derivative of ICD-based Injury Severity Score (ICISS-Multiplicative) demonstrated the best discriminative ability for predicting in-hospital mortality (AUC, 0.858; 95% confidence interval, 0.852–0.864) and ICU admissions (AUC, 0.813; 95% confidence interval, 0.808–0.818). Calibration assessments showed good agreement between observed and predicted in-hospital mortality for ICISS measures. All severity measures presented high agreement between observed and expected probabilities of ICU admission for all deciles of risk. CONCLUSIONS The ICD-based injury severity measures can be used to accurately predict in-hospital mortality and ICU admission in TBI patients. The ICISS-Multiplicative generally outperformed other ICD-based injury severity measures and should be preferred to control for differences in baseline characteristics between TBI patients in surveillance activities or injury research when only ICD codes are available. LEVEL OF EVIDENCE Prognostic study, level III.

Environmental factors associated with familial or non-familial forms of Paget's disease of bone.

OBJECTIVES The most frequent mutation linked to Pagets disease of bone (PDB), p.Pro392Leu within SQSTM1 gene, leads to phenotypic characteristics of PDB, but this mutation is seemingly insufficient to result in complete pagetic osteoclast phenotype, suggesting that possible environmental factors play a role in PDB pathogenesis. We performed an exploratory study to identify environmental factors potentially associated with familial or non-familial form of PDB in the French-Canadian population. METHODS We investigated environmental factors through a questionnaire in 176 pagetic patients, including 86 patients with a familial form, and 147 healthy controls. All participants lived in the same geographic area, within a 120km radius of Quebec City. Associations between environmental factors and familial and non-familial forms of PDB were searched. RESULTS In the multivariate model adjusted for intra-familial correlation, PDB was associated with wood fired heating in childhood and/or adolescence (OR=2.10; 95% CI 1.13-3.90, P=0.02). In the multivariate model without considering correlation for family relatedness, familial form of PDB was associated with residency near a mine (OR=11.70; 95% CI 2.92-46.80, P<0.01) and hunting (OR=2.92; 95% CI 1.14-7.47, P=0.03). Wood fired heating during childhood and/or adolescence (P=0.02) was associated with both familial and non-familial forms. CONCLUSIONS In conclusion, PDB was significantly associated with wood fired heating in childhood and/or adolescence, regardless of the form of PDB, familial or not.

Number, Location, and Time Since Prior Fracture as Predictors of Future Fracture in the Elderly From the General Population: PREDICTORS OF FUTURE FRACTURE IN ELDERLY

Prognostic tools are available to identify individuals at high risk of osteoporotic fracture and to assist physicians in management decisions. Some authors have suggested improving the predictive ability of these tools by integrating characteristics of prior fractures (number, location, and time since prior fracture). The objectives of this study were: (1) to evaluate the sex‐ and age‐specific associations between characteristics of prior fractures and the occurrence of a future osteoporotic fracture; and (2) to assess whether the characteristics of prior fractures could increase the discriminative ability of fracture risk prediction tools. A retrospective cohort study was conducted using administrative data. Men and women aged ≥66 years were selected and grouped into two cohorts. In cohort #1 (N = 759,500), history of fractures was measured between fiscal years 1997–1998 and 2003–2004, and future fractures were identified between 2004–2005 and 2013–2014. In cohort #2 (N = 807,245), history of fractures was measured between 1997–1998 and 2008–2009, and future fractures were identified between 2009–2010 and 2013–2014. Time until a first hip/femur and major osteoporotic fracture were the outcomes of interest. Adjusted HRs and c‐indices were calculated. The association between history of prior fractures and future fracture was stronger in men and younger individuals. The locations of prior fractures associated with the lowest and highest risks were foot/ankle/tibia/fibula (maximal HR = 1.64) and hip/femur (maximal HR = 9.02), respectively. The association was stronger for recent fractures (maximal HR = 4.93), but was still significant for fractures occurring 10 to 12 years prior to the beginning of follow‐up (maximal HR = 1.99). Characteristics of prior fractures did not increase model discrimination. Our study confirms that the risk of future fracture increases with the number of prior fractures, varies according to prior fracture location, and decreases with time since prior fracture. However, the integration of these characteristics in current fracture risk prediction tools is not required because it does not improve predictive ability.

Assessment of the educational impact of an information leaflet on the knowledge of complications in systemic sclerosis

Joint Bone Spine - In Press.Proof corrected by the author Available online since vendredi 23 janvier 2015