A clinical and echocardiographic score for assigning risk of major events after dobutamine echocardiograms

OBJECTIVES We sought to develop and validate a risk score combining both clinical and dobutamine echocardiographic (DbE) features in 4,890 patients who underwent DbE at three expert laboratories and were followed for death or myocardial infarction for up to five years. BACKGROUND In contrast to exercise scores, no score exists to combine clinical, stress, and echocardiographic findings with DbE. METHODS Dobutamine echocardiography was performed for evaluation of known or suspected coronary artery disease in 3,156 patients at two sites in the U.S. After exclusion of patients with incomplete follow-up, 1,456 DbEs were randomly selected to develop a multivariate model for prediction of events. After simplification of each model for clinical use, the models were internally validated in the remaining DbE patients in the same series and externally validated in 1,733 patients in an independent series. RESULTS The following score was derived from regression models in the modeling group (160 events): DbE risk = (age (.) 0.02) + (heart failure + rate-pressure product <15,000) (.) 0.4 + (ischemia + scar) (.) 0.6. The presence of each variable was scored as 1 and its absence scored as 0, except for age (continuous variable). Using cutoff values of 1.2 and 2.6, patients were classified into groups with five-year event-free survivals >95%, 75% to 95%, and <75%. Application of the score in the internal validation group (265 events) gave equivalent results, as did its application in the external validation group (494 events, C index = 0.72). CONCLUSIONS A risk score based on clinical and echocardiographic data may be used to quantify the risk of events in patients undergoing DbE. (C) 2004 by the American College of Cardiology Foundation.

An evaluation of Home Office extended interviews for police personnel

In this thesis the validity of an Assessment Centre (called 'Extended Interview') operated on behalf of the British police is investigated. This Assessment Centre (AC) is used to select from amongst internal candidates (serving policemen and policewomen) and external candidates (graduates) for places on an accelerated promotion scheme. The literature is reviewed with respect to history, content, structure, reliability, validity, efficiency and usefulness of ACs, and to contextual issues surrounding AC use. The history of, background to and content of police Extended Interviews (Els) is described, and research issues are identified. Internal validation involved regression of overall EI grades on measures from component tests, exercises, interviews and peer nominations. Four samples numbering 126, 73, 86 and 109 were used in this part of the research. External validation involved regression of three types of criteria - training grades, rank attained, and supervisory ratings - on all EI measures. Follow-up periods for job criteria ranged from 7 to 19 years. Three samples, numbering 223, 157 and 86, were used in this part of the research. In subsidiary investigations, supervisory ratings were factor analysed and criteria intercorrelated. For two of the samples involved in the external validition, clinical/judgemental prediction was compared with mechanical (unit-weighted composite) prediction. Main conclusions are that: (1) EI selection decisions were valid, but only for a job performance criterion; relatively low validity overall was interpreted principally in terms of the questionable job relatedness of the EI procedure; (2) Els as a whole had more validity than was reflected in final EI decisions; (3) assessors' use of information was not optimum, tending to over-emphasize subjectively derived information particularly from interviews; and (4) mechanical prediction was superior to clinical/judgemental prediction for five major criteria.

Population pharmacokinetic and pharmacogenetic analysis of 6-mercaptopurine in paediatric patients with acute lymphoblastic leukaemia

WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT • The cytotoxic effects of 6-mercaptopurine (6-MP) were found to be due to drug-derived intracellular metabolites (mainly 6-thioguanine nucleotides and to some extent 6-methylmercaptopurine nucleotides) rather than the drug itself. • Current empirical dosing methods for oral 6-MP result in highly variable drug and metabolite concentrations and hence variability in treatment outcome. WHAT THIS STUDY ADDS • The first population pharmacokinetic model has been developed for 6-MP active metabolites in paediatric patients with acute lymphoblastic leukaemia and the potential demographic and genetically controlled factors that could lead to interpatient pharmacokinetic variability among this population have been assessed. • The model shows a large reduction in interindividual variability of pharmacokinetic parameters when body surface area and thiopurine methyltransferase polymorphism are incorporated into the model as covariates. • The developed model offers a more rational dosing approach for 6-MP than the traditional empirical method (based on body surface area) through combining it with pharmacogenetically guided dosing based on thiopurine methyltransferase genotype. AIMS - To investigate the population pharmacokinetics of 6-mercaptopurine (6-MP) active metabolites in paediatric patients with acute lymphoblastic leukaemia (ALL) and examine the effects of various genetic polymorphisms on the disposition of these metabolites. METHODS - Data were collected prospectively from 19 paediatric patients with ALL (n = 75 samples, 150 concentrations) who received 6-MP maintenance chemotherapy (titrated to a target dose of 75 mg m−2 day−1). All patients were genotyped for polymorphisms in three enzymes involved in 6-MP metabolism. Population pharmacokinetic analysis was performed with the nonlinear mixed effects modelling program (nonmem) to determine the population mean parameter estimate of clearance for the active metabolites. RESULTS - The developed model revealed considerable interindividual variability (IIV) in the clearance of 6-MP active metabolites [6-thioguanine nucleotides (6-TGNs) and 6-methylmercaptopurine nucleotides (6-mMPNs)]. Body surface area explained a significant part of 6-TGNs clearance IIV when incorporated in the model (IIV reduced from 69.9 to 29.3%). The most influential covariate examined, however, was thiopurine methyltransferase (TPMT) genotype, which resulted in the greatest reduction in the model's objective function (P < 0.005) when incorporated as a covariate affecting the fractional metabolic transformation of 6-MP into 6-TGNs. The other genetic covariates tested were not statistically significant and therefore were not included in the final model. CONCLUSIONS - The developed pharmacokinetic model (if successful at external validation) would offer a more rational dosing approach for 6-MP than the traditional empirical method since it combines the current practice of using body surface area in 6-MP dosing with a pharmacogenetically guided dosing based on TPMT genotype.

VaxiJen:a server for prediction of protective antigens, tumour antigens and subunit vaccines

Background - Vaccine development in the post-genomic era often begins with the in silico screening of genome information, with the most probable protective antigens being predicted rather than requiring causative microorganisms to be grown. Despite the obvious advantages of this approach – such as speed and cost efficiency – its success remains dependent on the accuracy of antigen prediction. Most approaches use sequence alignment to identify antigens. This is problematic for several reasons. Some proteins lack obvious sequence similarity, although they may share similar structures and biological properties. The antigenicity of a sequence may be encoded in a subtle and recondite manner not amendable to direct identification by sequence alignment. The discovery of truly novel antigens will be frustrated by their lack of similarity to antigens of known provenance. To overcome the limitations of alignment-dependent methods, we propose a new alignment-free approach for antigen prediction, which is based on auto cross covariance (ACC) transformation of protein sequences into uniform vectors of principal amino acid properties. Results - Bacterial, viral and tumour protein datasets were used to derive models for prediction of whole protein antigenicity. Every set consisted of 100 known antigens and 100 non-antigens. The derived models were tested by internal leave-one-out cross-validation and external validation using test sets. An additional five training sets for each class of antigens were used to test the stability of the discrimination between antigens and non-antigens. The models performed well in both validations showing prediction accuracy of 70% to 89%. The models were implemented in a server, which we call VaxiJen. Conclusion - VaxiJen is the first server for alignment-independent prediction of protective antigens. It was developed to allow antigen classification solely based on the physicochemical properties of proteins without recourse to sequence alignment. The server can be used on its own or in combination with alignment-based prediction methods.

Identifying candidate subunit vaccines using an alignment-independent method based on principal amino acid properties

Subunit vaccine discovery is an accepted clinical priority. The empirical approach is time- and labor-consuming and can often end in failure. Rational information-driven approaches can overcome these limitations in a fast and efficient manner. However, informatics solutions require reliable algorithms for antigen identification. All known algorithms use sequence similarity to identify antigens. However, antigenicity may be encoded subtly in a sequence and may not be directly identifiable by sequence alignment. We propose a new alignment-independent method for antigen recognition based on the principal chemical properties of protein amino acid sequences. The method is tested by cross-validation on a training set of bacterial antigens and external validation on a test set of known antigens. The prediction accuracy is 83% for the cross-validation and 80% for the external test set. Our approach is accurate and robust, and provides a potent tool for the in silico discovery of medically relevant subunit vaccines.

EpiJen:a server for multistep T cell epitope prediction

Background - The main processing pathway for MHC class I ligands involves degradation of proteins by the proteasome, followed by transport of products by the transporter associated with antigen processing (TAP) to the endoplasmic reticulum (ER), where peptides are bound by MHC class I molecules, and then presented on the cell surface by MHCs. The whole process is modeled here using an integrated approach, which we call EpiJen. EpiJen is based on quantitative matrices, derived by the additive method, and applied successively to select epitopes. EpiJen is available free online. Results - To identify epitopes, a source protein is passed through four steps: proteasome cleavage, TAP transport, MHC binding and epitope selection. At each stage, different proportions of non-epitopes are eliminated. The final set of peptides represents no more than 5% of the whole protein sequence and will contain 85% of the true epitopes, as indicated by external validation. Compared to other integrated methods (NetCTL, WAPP and SMM), EpiJen performs best, predicting 61 of the 99 HIV epitopes used in this study. Conclusion - EpiJen is a reliable multi-step algorithm for T cell epitope prediction, which belongs to the next generation of in silico T cell epitope identification methods. These methods aim to reduce subsequent experimental work by improving the success rate of epitope prediction.

An artificial neural network stratifies the risks of reintervention and mortality after endovascular aneurysm repair:a retrospective observational study

Background Lifelong surveillance after endovascular repair (EVAR) of abdominal aortic aneurysms (AAA) is considered mandatory to detect potentially life-threatening endograft complications. A minority of patients require reintervention but cannot be predictively identified by existing methods. This study aimed to improve the prediction of endograft complications and mortality, through the application of machine-learning techniques. Methods Patients undergoing EVAR at 2 centres were studied from 2004-2010. Pre-operative aneurysm morphology was quantified and endograft complications were recorded up to 5 years following surgery. An artificial neural networks (ANN) approach was used to predict whether patients would be at low- or high-risk of endograft complications (aortic/limb) or mortality. Centre 1 data were used for training and centre 2 data for validation. ANN performance was assessed by Kaplan-Meier analysis to compare the incidence of aortic complications, limb complications, and mortality; in patients predicted to be low-risk, versus those predicted to be high-risk. Results 761 patients aged 75 +/- 7 years underwent EVAR. Mean follow-up was 36+/- 20 months. An ANN was created from morphological features including angulation/length/areas/diameters/ volume/tortuosity of the aneurysm neck/sac/iliac segments. ANN models predicted endograft complications and mortality with excellent discrimination between a low-risk and high-risk group. In external validation, the 5-year rates of freedom from aortic complications, limb complications and mortality were 95.9% vs 67.9%; 99.3% vs 92.0%; and 87.9% vs 79.3% respectively (p0.001) Conclusion This study presents ANN models that stratify the 5-year risk of endograft complications or mortality using routinely available pre-operative data.

A Bayesian neural network algorithm identifies patients in whom post-EVAR surveillance may be unnecessary

Lifelong surveillance is not cost-effective after endovascular aneurysm repair (EVAR), but is required to detect aortic complications which are fatal if untreated (type 1/3 endoleak, sac expansion, device migration). Aneurysm morphology determines the probability of aortic complications and therefore the need for surveillance, but existing analyses have proven incapable of identifying patients at sufficiently low risk to justify abandoning surveillance. This study aimed to improve the prediction of aortic complications, through the application of machine-learning techniques. Patients undergoing EVAR at 2 centres were studied from 2004–2010. Aneurysm morphology had previously been studied to derive the SGVI Score for predicting aortic complications. Bayesian Neural Networks were designed using the same data, to dichotomise patients into groups at low- or high-risk of aortic complications. Network training was performed only on patients treated at centre 1. External validation was performed by assessing network performance independently of network training, on patients treated at centre 2. Discrimination was assessed by Kaplan-Meier analysis to compare aortic complications in predicted low-risk versus predicted high-risk patients. 761 patients aged 75 +/− 7 years underwent EVAR in 2 centres. Mean follow-up was 36+/− 20 months. Neural networks were created incorporating neck angu- lation/length/diameter/volume; AAA diameter/area/volume/length/tortuosity; and common iliac tortuosity/diameter. A 19-feature network predicted aor- tic complications with excellent discrimination and external validation (5-year freedom from aortic complications in predicted low-risk vs predicted high-risk patients: 97.9% vs. 63%; p < 0.0001). A Bayesian Neural-Network algorithm can identify patients in whom it may be safe to abandon surveillance after EVAR. This proposal requires prospective study.

Chlorine contribution to quantitative structure and activity relationship models of disinfection by -products' quantum chemical descriptors and toxicities

Quantitative Structure-Activity Relationship (QSAR) has been applied extensively in predicting toxicity of Disinfection By-Products (DBPs) in drinking water. Among many toxicological properties, acute and chronic toxicities of DBPs have been widely used in health risk assessment of DBPs. These toxicities are correlated with molecular properties, which are usually correlated with molecular descriptors. The primary goals of this thesis are: (1) to investigate the effects of molecular descriptors (e.g., chlorine number) on molecular properties such as energy of the lowest unoccupied molecular orbital (E LUMO) via QSAR modelling and analysis; (2) to validate the models by using internal and external cross-validation techniques; (3) to quantify the model uncertainties through Taylor and Monte Carlo Simulation. One of the very important ways to predict molecular properties such as ELUMO is using QSAR analysis. In this study, number of chlorine (NCl ) and number of carbon (NC) as well as energy of the highest occupied molecular orbital (EHOMO) are used as molecular descriptors. There are typically three approaches used in QSAR model development: (1) Linear or Multi-linear Regression (MLR); (2) Partial Least Squares (PLS); and (3) Principle Component Regression (PCR). In QSAR analysis, a very critical step is model validation after QSAR models are established and before applying them to toxicity prediction. The DBPs to be studied include five chemical classes: chlorinated alkanes, alkenes, and aromatics. In addition, validated QSARs are developed to describe the toxicity of selected groups (i.e., chloro-alkane and aromatic compounds with a nitro- or cyano group) of DBP chemicals to three types of organisms (e.g., Fish, T. pyriformis, and P.pyosphoreum) based on experimental toxicity data from the literature. The results show that: (1) QSAR models to predict molecular property built by MLR, PLS or PCR can be used either to select valid data points or to eliminate outliers; (2) The Leave-One-Out Cross-Validation procedure by itself is not enough to give a reliable representation of the predictive ability of the QSAR models, however, Leave-Many-Out/K-fold cross-validation and external validation can be applied together to achieve more reliable results; (3) E LUMO are shown to correlate highly with the NCl for several classes of DBPs; and (4) According to uncertainty analysis using Taylor method, the uncertainty of QSAR models is contributed mostly from NCl for all DBP classes.

Chlorine Contribution to Quantitative Structure and Activity Relationship Models of Disinfection By-Products' Quantum Chemical Descriptors and Toxicities

Quantitative Structure-Activity Relationship (QSAR) has been applied extensively in predicting toxicity of Disinfection By-Products (DBPs) in drinking water. Among many toxicological properties, acute and chronic toxicities of DBPs have been widely used in health risk assessment of DBPs. These toxicities are correlated with molecular properties, which are usually correlated with molecular descriptors. The primary goals of this thesis are: 1) to investigate the effects of molecular descriptors (e.g., chlorine number) on molecular properties such as energy of the lowest unoccupied molecular orbital (ELUMO) via QSAR modelling and analysis; 2) to validate the models by using internal and external cross-validation techniques; 3) to quantify the model uncertainties through Taylor and Monte Carlo Simulation. One of the very important ways to predict molecular properties such as ELUMO is using QSAR analysis. In this study, number of chlorine (NCl) and number of carbon (NC) as well as energy of the highest occupied molecular orbital (EHOMO) are used as molecular descriptors. There are typically three approaches used in QSAR model development: 1) Linear or Multi-linear Regression (MLR); 2) Partial Least Squares (PLS); and 3) Principle Component Regression (PCR). In QSAR analysis, a very critical step is model validation after QSAR models are established and before applying them to toxicity prediction. The DBPs to be studied include five chemical classes: chlorinated alkanes, alkenes, and aromatics. In addition, validated QSARs are developed to describe the toxicity of selected groups (i.e., chloro-alkane and aromatic compounds with a nitro- or cyano group) of DBP chemicals to three types of organisms (e.g., Fish, T. pyriformis, and P.pyosphoreum) based on experimental toxicity data from the literature. The results show that: 1) QSAR models to predict molecular property built by MLR, PLS or PCR can be used either to select valid data points or to eliminate outliers; 2) The Leave-One-Out Cross-Validation procedure by itself is not enough to give a reliable representation of the predictive ability of the QSAR models, however, Leave-Many-Out/K-fold cross-validation and external validation can be applied together to achieve more reliable results; 3) ELUMO are shown to correlate highly with the NCl for several classes of DBPs; and 4) According to uncertainty analysis using Taylor method, the uncertainty of QSAR models is contributed mostly from NCl for all DBP classes.

Origin authentication of distillers' dried grains and solubles (DDGS) - Application and comparison of different analytical strategies

In the context of products from certain regions or countries being banned because of an identified or non-identified hazard, proof of geographical origin is essential with regard to feed and food safety issues. Usually, the product labeling of an affected feed lot shows origin, and the paper documentation shows traceability. Incorrect product labeling is common in embargo situations, however, and alternative analytical strategies for controlling feed authenticity are therefore needed. In this study, distillers' dried grains and solubles (DDGS) were chosen as the product on which to base a comparison of analytical strategies aimed at identifying the most appropriate one. Various analytical techniques were investigated for their ability to authenticate DDGS, including spectroscopic and spectrometric techniques combined with multivariate data analysis, as well as proven techniques for authenticating food, such as DNA analysis and stable isotope ratio analysis. An external validation procedure (called the system challenge) was used to analyze sample sets blind and to compare analytical techniques. All the techniques were adapted so as to be applicable to the DDGS matrix. They produced positive results in determining the botanical origin of DDGS (corn vs. wheat), and several of them were able to determine the geographical origin of the DDGS in the sample set. The maintenance and extension of the databanks generated in this study through the analysis of new authentic samples from a single location are essential in order to monitor developments and processing that could affect authentication.

Non-contact Pressure-based Sleep/Wake Discrimination

Poor sleep is increasingly being recognised as an important prognostic parameter of health. For those with suspected sleep disorders, patients are referred to sleep clinics which guide treatment. However, sleep clinics are not always a viable option due to their high cost, a lack of experienced practitioners, lengthy waiting lists and an unrepresentative sleeping environment. A home-based non-contact sleep/wake monitoring system may be used as a guide for treatment potentially stratifying patients by clinical need or highlighting longitudinal changes in sleep and nocturnal patterns. This paper presents the evaluation of an under-mattress sleep monitoring system for non-contact sleep/wake discrimination. A large dataset of sensor data with concomitant sleep/wake state was collected from both younger and older adults participating in a circadian sleep study. A thorough training/testing/validation procedure was configured and optimised feature extraction and sleep/wake discrimination algorithms evaluated both within and across the two cohorts. An accuracy, sensitivity and specificity of 74.3%, 95.5%, and 53.2% is reported over all subjects using an external validation
dataset (71.9%, 87.9% and 56%, and 77.5%, 98% and 57% is reported for younger and older subjects respectively). These results compare favourably with similar research, however this system provides an ambient alternative suitable for long term continuous sleep monitoring, particularly amongst vulnerable populations.

Prediction of peptide and protein propensity for amyloid formation

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Identification of patients at risk of poor outcomes following hip or knee arthroplasty

Les arthroplasties totales de la hanche (ATH) et du genou (ATG) sont souvent offertes aux patients atteints de dégénérescence articulaire sévère. Bien qu’efficace chez la majorité des patients, ces interventions mènent à des résultats sous-optimaux dans de nombreux cas. Il demeure difficile d’identifier les patients à risque de résultats sous-optimaux à l’heure actuelle. L’identification de ces patients avant la chirurgie pourrait permettre d’optimiser la gamme de soins et de services offerts et de possiblement améliorer les résultats de leur chirurgie. Ce mémoire a comme objectifs : 1) de réaliser une revue systématique des déterminants associés à la douleur et aux incapacités fonctionnelles rapportées par les patients à moyen-terme suivant ces deux types d’arthroplastie et 2) de développer des modèles de prédiction clinique permettant l’identification des patients à risque de mauvais résultats en terme de douleur et d’incapacités fonctionnelles suivant l’ATH et l’ATG. Une revue systématique de la littérature identifiant les déterminants de la douleur et de la fonction suivant l’ATH et l’ATG a été réalisée dans quatre bases de données jusqu’en avril 2015 et octobre 2014, respectivement. Afin de développer un algorithme de prédiction pouvant identifier les patients à risque de résultats sous-optimaux, nous avons aussi utilisé des données rétrospectives provenant de 265 patients ayant subi une ATH à l’Hôpital Maisonneuve-Rosemont (HMR) de 2004 à 2010. Finalement, des données prospectives sur 141 patients recrutés au moment de leur inclusion sur une liste d’attente pour une ATG dans trois hôpitaux universitaires à Québec, Canada et suivis jusqu’à six mois après la chirurgie ont permis l’élaboration d’une règle de prédiction clinique permettant l’identification des patients à risque de mauvais résultats en terme de douleur et d’incapacités fonctionnelles. Vingt-deux (22) études d’une qualité méthodologique moyenne à excellente ont été incluses dans la revue. Les principaux déterminants de douleur et d’incapacités fonctionnelles après l’ATH incluaient: le niveau préopératoire de douleur et de fonction, un indice de la masse corporelle plus élevé, des comorbidités médicales plus importantes, un état de santé générale diminué, une scolarité plus faible, une arthrose radiographique moins sévère et la présence d’arthrose à la hanche controlatérale. Trente-quatre (34) études évaluant les déterminants de douleur et d’incapacités fonctionnelles après l’ATG avec une qualité méthodologique moyenne à excellente ont été évaluées et les déterminants suivant ont été identifiés: le niveau préopératoire de douleur et de fonction, des comorbidités médicales plus importantes, un état de santé générale diminué, un plus grands niveau d’anxiété et/ou de symptômes dépressifs, la présence de douleur au dos, plus de pensées catastrophiques ou un faible niveau socioéconomique. Pour la création d’une règle de prédiction clinique, un algorithme préliminaire composé de l’âge, du sexe, de l’indice de masse corporelle ainsi que de trois questions du WOMAC préopératoire a permis l’identification des patients à risque de résultats chirurgicaux sous-optimaux (pire quartile du WOMAC postopératoire et percevant leur hanche opérée comme artificielle avec des limitations fonctionnelles mineures ou majeures) à une durée moyenne ±écart type de 446±171 jours après une ATH avec une sensibilité de 75.0% (95% IC: 59.8 – 85.8), une spécificité de 77.8% (95% IC: 71.9 – 82.7) et un rapport de vraisemblance positif de 3.38 (98% IC: 2.49 – 4.57). Une règle de prédiction clinique formée de cinq items du questionnaire WOMAC préopratoire a permis l’identification des patients en attente d’une ATG à risque de mauvais résultats (pire quintile du WOMAC postopératoire) six mois après l’ATG avec une sensibilité de 82.1 % (95% IC: 66.7 – 95.8), une spécificité de 71.7% (95% IC: 62.8 – 79.8) et un rapport de vraisemblance positif de 2.9 (95% IC: 1.8 – 4.7). Les résultats de ce mémoire ont permis d’identifier, à partir de la littérature, une liste de déterminants de douleur et d’incapacités fonctionnelles après l’ATH et l’ATG avec le plus haut niveau d’évidence à ce jour. De plus, deux modèles de prédiction avec de très bonnes capacités prédictives ont été développés afin d’identifier les patients à risque de mauvais résultats chirurgicaux après l’ATH et l’ATG. L’identification de ces patients avant la chirurgie pourrait permettre d’optimiser leur prise en charge et de possiblement améliorer les résultats de leur chirurgie.

Identification of patients at risk of poor outcomes following hip or knee arthroplasty

Les arthroplasties totales de la hanche (ATH) et du genou (ATG) sont souvent offertes aux patients atteints de dégénérescence articulaire sévère. Bien qu’efficace chez la majorité des patients, ces interventions mènent à des résultats sous-optimaux dans de nombreux cas. Il demeure difficile d’identifier les patients à risque de résultats sous-optimaux à l’heure actuelle. L’identification de ces patients avant la chirurgie pourrait permettre d’optimiser la gamme de soins et de services offerts et de possiblement améliorer les résultats de leur chirurgie. Ce mémoire a comme objectifs : 1) de réaliser une revue systématique des déterminants associés à la douleur et aux incapacités fonctionnelles rapportées par les patients à moyen-terme suivant ces deux types d’arthroplastie et 2) de développer des modèles de prédiction clinique permettant l’identification des patients à risque de mauvais résultats en terme de douleur et d’incapacités fonctionnelles suivant l’ATH et l’ATG. Une revue systématique de la littérature identifiant les déterminants de la douleur et de la fonction suivant l’ATH et l’ATG a été réalisée dans quatre bases de données jusqu’en avril 2015 et octobre 2014, respectivement. Afin de développer un algorithme de prédiction pouvant identifier les patients à risque de résultats sous-optimaux, nous avons aussi utilisé des données rétrospectives provenant de 265 patients ayant subi une ATH à l’Hôpital Maisonneuve-Rosemont (HMR) de 2004 à 2010. Finalement, des données prospectives sur 141 patients recrutés au moment de leur inclusion sur une liste d’attente pour une ATG dans trois hôpitaux universitaires à Québec, Canada et suivis jusqu’à six mois après la chirurgie ont permis l’élaboration d’une règle de prédiction clinique permettant l’identification des patients à risque de mauvais résultats en terme de douleur et d’incapacités fonctionnelles. Vingt-deux (22) études d’une qualité méthodologique moyenne à excellente ont été incluses dans la revue. Les principaux déterminants de douleur et d’incapacités fonctionnelles après l’ATH incluaient: le niveau préopératoire de douleur et de fonction, un indice de la masse corporelle plus élevé, des comorbidités médicales plus importantes, un état de santé générale diminué, une scolarité plus faible, une arthrose radiographique moins sévère et la présence d’arthrose à la hanche controlatérale. Trente-quatre (34) études évaluant les déterminants de douleur et d’incapacités fonctionnelles après l’ATG avec une qualité méthodologique moyenne à excellente ont été évaluées et les déterminants suivant ont été identifiés: le niveau préopératoire de douleur et de fonction, des comorbidités médicales plus importantes, un état de santé générale diminué, un plus grands niveau d’anxiété et/ou de symptômes dépressifs, la présence de douleur au dos, plus de pensées catastrophiques ou un faible niveau socioéconomique. Pour la création d’une règle de prédiction clinique, un algorithme préliminaire composé de l’âge, du sexe, de l’indice de masse corporelle ainsi que de trois questions du WOMAC préopératoire a permis l’identification des patients à risque de résultats chirurgicaux sous-optimaux (pire quartile du WOMAC postopératoire et percevant leur hanche opérée comme artificielle avec des limitations fonctionnelles mineures ou majeures) à une durée moyenne ±écart type de 446±171 jours après une ATH avec une sensibilité de 75.0% (95% IC: 59.8 – 85.8), une spécificité de 77.8% (95% IC: 71.9 – 82.7) et un rapport de vraisemblance positif de 3.38 (98% IC: 2.49 – 4.57). Une règle de prédiction clinique formée de cinq items du questionnaire WOMAC préopratoire a permis l’identification des patients en attente d’une ATG à risque de mauvais résultats (pire quintile du WOMAC postopératoire) six mois après l’ATG avec une sensibilité de 82.1 % (95% IC: 66.7 – 95.8), une spécificité de 71.7% (95% IC: 62.8 – 79.8) et un rapport de vraisemblance positif de 2.9 (95% IC: 1.8 – 4.7). Les résultats de ce mémoire ont permis d’identifier, à partir de la littérature, une liste de déterminants de douleur et d’incapacités fonctionnelles après l’ATH et l’ATG avec le plus haut niveau d’évidence à ce jour. De plus, deux modèles de prédiction avec de très bonnes capacités prédictives ont été développés afin d’identifier les patients à risque de mauvais résultats chirurgicaux après l’ATH et l’ATG. L’identification de ces patients avant la chirurgie pourrait permettre d’optimiser leur prise en charge et de possiblement améliorer les résultats de leur chirurgie.