Prediction of Osteoporotic Fractures in Elderly Individuals: A Derivation and Internal Validation Study Using Healthcare Administrative Data.

In Canada and other countries, osteoporosis is monitored as part of chronic disease population surveillance programs. Although fractures are the principal manifestation of osteoporosis, very few algorithms are available to identify individuals at high risk of osteoporotic fractures in current surveillance systems. The objective of this study was to derive and validate predictive models to accurately identify individuals at high risk of osteoporotic fracture using information available in healthcare administrative data. More than 270,000 men and women aged ≥66 years were randomly selected from the Quebec Integrated Chronic Disease Surveillance System. Selected individuals were followed between fiscal years 2006-2007 and 2015-2016. Models were constructed for prediction of hip/femur and major osteoporotic fractures for follow-up periods of 5 and 10 years. A total of 62 potential predictors measurable in healthcare administrative databases were identified. Predictor selection was performed using a manual backward algorithm. The predictive performance of the final models was assessed using measures of discrimination, calibration, and overall performance. Between 20 and 25 predictors were retained in the final prediction models (eg, age, sex, social deprivation index, most of the major and minor risk factors for osteoporosis, diabetes, Parkinson's disease, cognitive impairment, anemia, anxio-depressive disorders). Discrimination of the final models was higher for the prediction of hip/femur fracture than major osteoporotic fracture and higher for prediction for a 5-year than a 10-year period (hip/femur fracture for 5 years: c-index = 0.77; major osteoporotic fracture for 5 years: c-index = 0.71; hip/femur fracture for 10 years: c-index = 0.73; major osteoporotic fracture for 10 years: c-index = 0.68). The predicted probabilities globally agreed with the observed probabilities. In conclusion, the derived models had adequate predictive performance in internal validation. As a final step, these models should be validated in an external cohort and used to develop indicators for surveillance of osteoporosis. © 2021 American Society for Bone and Mineral Research (ASBMR).

Public Health Outcomes May Differ After Switching from Brand-Name to Generic Angiotensin II Receptor Blockers.

BACKGROUND: It is unclear whether generics are as safe as brand-name drugs in cardiology. For public health surveillance purposes, we evaluated if switching from the brand-name losartan, valsartan, or candesartan impacted the occurrence of the following outcomes: emergency room (ER) consultations, hospitalizations, or death. STUDY DESIGN: This was a retrospective cohort study. METHODS: This study was conducted in the Quebec Integrated Chronic Disease Surveillance System, including healthcare administrative data of the population of Quebec, Canada. We included brand-name users of losartan, valsartan, or candesartan aged ≥ 66 years who had undergone ≥ 30 days of stable treatment on the brand-name drug prior to cohort entry (substitution time-distribution matching was used to prevent immortal time bias). Outcomes up to 1 year were compared between groups using multivariable Cox proportional hazards regression models (validity assumptions were verified). RESULTS: In our cohorts (losartan, n =15,783; valsartan, n =16,907; candesartan, n =26,178), mean age was 76-78 years, 59-66% were female, 90-92% had hypertension, and 13-15% had heart failure. Validity assumptions were violated for losartan only. For patients switched to generic valsartan, the hazard ratio (95% confidence interval) was 1.07 (0.99-1.14) for ER consultation, 1.26 (1.14-1.39) for hospitalization, and 1.01 (0.61-1.67) for death. The corresponding rates for candesartan were 1.00 (0.95-1.05), 0.96 (0.89-1.03), and 0.57 (0.37-0.88), respectively. CONCLUSIONS: We observed an increased risk of hospitalizations for patients switched to generic valsartan, and a decreased risk of death for patients switched to generic candesartan, compared with those who continued taking the brand-name drug. The differences between generic and brand-name drugs may lead to some differences in public health outcomes, but this safety signal must be further studied using other cohorts and settings.

Are Generic Drugs Used in Cardiology as Effective and Safe as their Brand-name Counterparts? A Systematic Review and Meta-analysis.

BACKGROUND: Previous systematic reviews (2008; 2016) concluded similarity in outcomes between brand-name and generic drugs in cardiology, but they included ≥ 50% comparative bioavailability studies, not designed or powered to detect a difference in efficacy or safety between drug types. We aimed to summarise best-evidence regarding the effectiveness and safety of generic versus brand-name drugs used in cardiology. METHODS: For this systematic review of the literature, scientific databases (MEDLINE and EMBASE) were searched from January 1984 to October 2018. Original research reports comparing the clinical impact of brand-name versus generic cardiovascular drugs on humans treated in a real-life setting, were selected. Meta-analyses and subgroup analyses were performed. Heterogeneity (I2) and risk of bias were tested. RESULTS: Among the 3148 screened abstracts, 72 met the inclusion criteria (n ≥ 1,000,000 patients, mean age 65 ± 10 years; 42% women). A total of 60% of studies showed no difference between drug types, while 26% concluded that the brand-name drug was more effective or safe, 13% were inconclusive and only 1% concluded that generics did better. The overall crude risk ratio of all-cause hospital visits for generic versus brand-name drug was 1.14 (95% confidence interval: 1.06-1.23; I2: 98%), while it was 1.05 (0.98-1.14; I2: 68%) for cardiovascular hospital visits. The crude risk ratio was not statistically significant for randomised controlled trials only (n = 4; 0.92 [0.63-1.34], I2: 35%). CONCLUSION: The crude risk of hospital visits was higher for patients exposed to generic compared to brand-name cardiovascular drugs. However, the evidence is insufficient and too heterogeneous to draw any firm conclusion regarding the effectiveness and safety of generic drugs in cardiology.

Comparison of Fracture Prediction Tools in Individuals Without and With Early Chronic Kidney Disease: A Population-Based Analysis of CARTaGENE.

Whether fracture prediction tools developed for the management of osteoporosis can be used in chronic kidney disease (CKD) is poorly known. We aimed to compare the performance of fracture prediction tools in non-CKD and CKD. We analyzed CARTaGENE, a population-based survey of 40-year-old to 69-year-old individuals recruited between 2009 and 2010. Renal function was assessed using baseline creatinine and categorized according to Kidney Disease Improving Global Outcomes (KDIGO) guidelines (non-CKD, stage 2, stage 3). Individuals without creatinine measurements or with advanced CKD (stage 4 or 5; prevalence <0.25%) were excluded. Predicted 5-year fracture probabilities (using Fracture Risk Assessment Tool [FRAX], QFracture, and Garvan) were computed at baseline. Fracture incidence (major fracture [MOF] or any fracture) was evaluated in administrative databases from recruitment to March 2016. Discrimination (hazard ratios [HRs] per standard deviation [SD] increase in Cox models; c-statistics) and calibration (standardized incidence ratios [SIRs] before and after recalibration) were assessed in each CKD strata. We included 19,393 individuals (9522 non-CKD; 9114 stage 2; 757 stage 3). A total of 830 patients had any fracture during follow-up, including 352 MOF. FRAX (HR = 1.89 [1.63-2.20] non-CKD; 1.64 [1.41-1.91] stage 2; 1.76 [1.10-2.82] stage 3) and QFracture (HR = 1.90 [1.62-2.22] non-CKD; 1.57 [1.35-1.82] stage 2; 1.86 [1.19-2.91] stage 3) discriminated MOF similarly in non-CKD and CKD. In contrast, the discrimination of Garvan for any fracture tended to be lower in CKD stage 3 compared to non-CKD and CKD stage 2 (HR = 1.36 [1.22-1.52] non-CKD; 1.34 [1.20-1.50] stage 2; 1.11 [0.79-1.55] stage 3). Before recalibration, FRAX globally overestimated fracture risk while QFracture and Garvan globally underestimated fracture risk. After recalibration, FRAX and QFracture were adequately calibrated for MOF in all CKD strata whereas Garvan tended to underestimate any fracture risk in CKD stage 3 (SIR = 1.31 [0.95-1.81]). In conclusion, the discrimination and calibration of FRAX and QFracture is similar in non-CKD and CKD. Garvan may have a lower discrimination in CKD stage 3 and underestimate fracture risk in these patients. © 2020 American Society for Bone and Mineral Research.

Number, Location, and Time Since Prior Fracture as Predictors of Future Fracture in the Elderly From the General Population.

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. © 2018 American Society for Bone and Mineral Research.

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 Paget's 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.

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.