Preoperative pain level and patient expectation predict hospital length of stay after total hip arthroplasty.

The purpose of this study was to identify preoperative predictors of length of stay after primary total hip arthroplasty in a patient population reflecting current trends toward shorter hospitalization and using readily obtainable factors that do not require scoring systems. A retrospective review of 112 consecutive patients was performed. High preoperative pain level and patient expectation of discharge to extended care facilities (ECFs) were the only significant multivariable predictors of hospitalization extending beyond 2 days (P=0.001 and P<0.001 respectively). Patient expectation remained significant after adjusting for Medicare's 3-day requirement for discharge to ECFs (P<0.001). The study was adequately powered to analyze the variables in the multivariable logistic regression model, which had a concordance index of 0.857.

Patient expectation is the most important predictor of discharge destination after primary total joint arthroplasty.

The purpose of this study was to identify preoperative predictors of discharge destination after total joint arthroplasty. A retrospective study of three hundred and seventy-two consecutive patients who underwent primary total hip and knee arthroplasty was performed. The mean length of stay was 2.9 days and 29.0% of patients were discharged to extended care facilities. Age, caregiver support at home, and patient expectation of discharge destination were the only significant multivariable predictors regardless of the type of surgery (total knee versus total hip arthroplasty). Among those variables, patient expectation was the most important predictor (P < 0.001; OR 169.53). The study was adequately powered to analyze the variables in the multivariable logistic regression model, which had a high concordance index of 0.969.

Preoperative predictors of extended hospital length of stay following total knee arthroplasty.

The purpose of this study was to identify the preoperative predictors of hospital length of stay after primary total knee arthroplasty in a patient population reflecting current trends toward shorter hospitalization and using readily obtainable factors that do not require scoring systems. A single-center, multi-surgeon retrospective chart review of two hundred and sixty consecutive patients who underwent primary total knee arthroplasty was performed. The mean length of stay was 3.0 days. Among the different variables studied, increasing comorbidities, lack of adequate assistance at home, and bilateral surgery were the only multivariable significant predictors of longer length of stay. The study was adequately powered for statistical analyses and the concordance index of the multivariable logistic regression model was 0.815.

Does commitment to rehabilitation influence clinical outcome of total hip resurfacing arthroplasty?

BACKGROUND: The purpose of this study was to evaluate whether compliance and rehabilitative efforts were predictors of early clinical outcome of total hip resurfacing arthroplasty. METHODS: A cross-sectional survey was utilized to collect information from 147 resurfacing patients, who were operated on by a single surgeon, regarding their level of commitment to rehabilitation following surgery. Patients were followed for a mean of 52 months (range, 24 to 90 months). Clinical outcomes and functional capabilities were assessed utilizing the Harris hip objective rating system, the SF-12 Health Survey, and an eleven-point satisfaction score. A linear regression analysis was used to determine whether there was any correlation between the rehabilitation commitment scores and any of the outcome measures, and a multivariate regression model was used to control for potentially confounding factors. RESULTS: Overall, an increased level of commitment to rehabilitation was positively correlated with each of the following outcome measures: SF-12 Mental Component Score, SF-12 Physical Component Score, Harris Hip score, and satisfaction scores. These correlations remained statistically significant in the multivariate regression model. CONCLUSIONS: Patients who were more committed to their therapy after hip resurfacing returned to higher levels of functionality and were more satisfied following their surgery.

Micro-Anatomical Quantitative Imaging Towards Enabling Automated Diagnosis of Thick Tissues at the Point of Care

Histopathology is the clinical standard for tissue diagnosis. However, histopathology has several limitations including that it requires tissue processing, which can take 30 minutes or more, and requires a highly trained pathologist to diagnose the tissue. Additionally, the diagnosis is qualitative, and the lack of quantitation leads to possible observer-specific diagnosis. Taken together, it is difficult to diagnose tissue at the point of care using histopathology.

Several clinical situations could benefit from more rapid and automated histological processing, which could reduce the time and the number of steps required between obtaining a fresh tissue specimen and rendering a diagnosis. For example, there is need for rapid detection of residual cancer on the surface of tumor resection specimens during excisional surgeries, which is known as intraoperative tumor margin assessment. Additionally, rapid assessment of biopsy specimens at the point-of-care could enable clinicians to confirm that a suspicious lesion is successfully sampled, thus preventing an unnecessary repeat biopsy procedure. Rapid and low cost histological processing could also be potentially useful in settings lacking the human resources and equipment necessary to perform standard histologic assessment. Lastly, automated interpretation of tissue samples could potentially reduce inter-observer error, particularly in the diagnosis of borderline lesions.

To address these needs, high quality microscopic images of the tissue must be obtained in rapid timeframes, in order for a pathologic assessment to be useful for guiding the intervention. Optical microscopy is a powerful technique to obtain high-resolution images of tissue morphology in real-time at the point of care, without the need for tissue processing. In particular, a number of groups have combined fluorescence microscopy with vital fluorescent stains to visualize micro-anatomical features of thick (i.e. unsectioned or unprocessed) tissue. However, robust methods for segmentation and quantitative analysis of heterogeneous images are essential to enable automated diagnosis. Thus, the goal of this work was to obtain high resolution imaging of tissue morphology through employing fluorescence microscopy and vital fluorescent stains and to develop a quantitative strategy to segment and quantify tissue features in heterogeneous images, such as nuclei and the surrounding stroma, which will enable automated diagnosis of thick tissues.

To achieve these goals, three specific aims were proposed. The first aim was to develop an image processing method that can differentiate nuclei from background tissue heterogeneity and enable automated diagnosis of thick tissue at the point of care. A computational technique called sparse component analysis (SCA) was adapted to isolate features of interest, such as nuclei, from the background. SCA has been used previously in the image processing community for image compression, enhancement, and restoration, but has never been applied to separate distinct tissue types in a heterogeneous image. In combination with a high resolution fluorescence microendoscope (HRME) and a contrast agent acriflavine, the utility of this technique was demonstrated through imaging preclinical sarcoma tumor margins. Acriflavine localizes to the nuclei of cells where it reversibly associates with RNA and DNA. Additionally, acriflavine shows some affinity for collagen and muscle. SCA was adapted to isolate acriflavine positive features or APFs (which correspond to RNA and DNA) from background tissue heterogeneity. The circle transform (CT) was applied to the SCA output to quantify the size and density of overlapping APFs. The sensitivity of the SCA+CT approach to variations in APF size, density and background heterogeneity was demonstrated through simulations. Specifically, SCA+CT achieved the lowest errors for higher contrast ratios and larger APF sizes. When applied to tissue images of excised sarcoma margins, SCA+CT correctly isolated APFs and showed consistently increased density in tumor and tumor + muscle images compared to images containing muscle. Next, variables were quantified from images of resected primary sarcomas and used to optimize a multivariate model. The sensitivity and specificity for differentiating positive from negative ex vivo resected tumor margins was 82% and 75%. The utility of this approach was further tested by imaging the in vivo tumor cavities from 34 mice after resection of a sarcoma with local recurrence as a bench mark. When applied prospectively to images from the tumor cavity, the sensitivity and specificity for differentiating local recurrence was 78% and 82%. The results indicate that SCA+CT can accurately delineate APFs in heterogeneous tissue, which is essential to enable automated and rapid surveillance of tissue pathology.

Two primary challenges were identified in the work in aim 1. First, while SCA can be used to isolate features, such as APFs, from heterogeneous images, its performance is limited by the contrast between APFs and the background. Second, while it is feasible to create mosaics by scanning a sarcoma tumor bed in a mouse, which is on the order of 3-7 mm in any one dimension, it is not feasible to evaluate an entire human surgical margin. Thus, improvements to the microscopic imaging system were made to (1) improve image contrast through rejecting out-of-focus background fluorescence and to (2) increase the field of view (FOV) while maintaining the sub-cellular resolution needed for delineation of nuclei. To address these challenges, a technique called structured illumination microscopy (SIM) was employed in which the entire FOV is illuminated with a defined spatial pattern rather than scanning a focal spot, such as in confocal microscopy.

Thus, the second aim was to improve image contrast and increase the FOV through employing wide-field, non-contact structured illumination microscopy and optimize the segmentation algorithm for new imaging modality. Both image contrast and FOV were increased through the development of a wide-field fluorescence SIM system. Clear improvement in image contrast was seen in structured illumination images compared to uniform illumination images. Additionally, the FOV is over 13X larger than the fluorescence microendoscope used in aim 1. Initial segmentation results of SIM images revealed that SCA is unable to segment large numbers of APFs in the tumor images. Because the FOV of the SIM system is over 13X larger than the FOV of the fluorescence microendoscope, dense collections of APFs commonly seen in tumor images could no longer be sparsely represented, and the fundamental sparsity assumption associated with SCA was no longer met. Thus, an algorithm called maximally stable extremal regions (MSER) was investigated as an alternative approach for APF segmentation in SIM images. MSER was able to accurately segment large numbers of APFs in SIM images of tumor tissue. In addition to optimizing MSER for SIM image segmentation, an optimal frequency of the illumination pattern used in SIM was carefully selected because the image signal to noise ratio (SNR) is dependent on the grid frequency. A grid frequency of 31.7 mm-1 led to the highest SNR and lowest percent error associated with MSER segmentation.

Once MSER was optimized for SIM image segmentation and the optimal grid frequency was selected, a quantitative model was developed to diagnose mouse sarcoma tumor margins that were imaged ex vivo with SIM. Tumor margins were stained with acridine orange (AO) in aim 2 because AO was found to stain the sarcoma tissue more brightly than acriflavine. Both acriflavine and AO are intravital dyes, which have been shown to stain nuclei, skeletal muscle, and collagenous stroma. A tissue-type classification model was developed to differentiate localized regions (75x75 µm) of tumor from skeletal muscle and adipose tissue based on the MSER segmentation output. Specifically, a logistic regression model was used to classify each localized region. The logistic regression model yielded an output in terms of probability (0-100%) that tumor was located within each 75x75 µm region. The model performance was tested using a receiver operator characteristic (ROC) curve analysis that revealed 77% sensitivity and 81% specificity. For margin classification, the whole margin image was divided into localized regions and this tissue-type classification model was applied. In a subset of 6 margins (3 negative, 3 positive), it was shown that with a tumor probability threshold of 50%, 8% of all regions from negative margins exceeded this threshold, while over 17% of all regions exceeded the threshold in the positive margins. Thus, 8% of regions in negative margins were considered false positives. These false positive regions are likely due to the high density of APFs present in normal tissues, which clearly demonstrates a challenge in implementing this automatic algorithm based on AO staining alone.

Thus, the third aim was to improve the specificity of the diagnostic model through leveraging other sources of contrast. Modifications were made to the SIM system to enable fluorescence imaging at a variety of wavelengths. Specifically, the SIM system was modified to enabling imaging of red fluorescent protein (RFP) expressing sarcomas, which were used to delineate the location of tumor cells within each image. Initial analysis of AO stained panels confirmed that there was room for improvement in tumor detection, particularly in regards to false positive regions that were negative for RFP. One approach for improving the specificity of the diagnostic model was to investigate using a fluorophore that was more specific to staining tumor. Specifically, tetracycline was selected because it appeared to specifically stain freshly excised tumor tissue in a matter of minutes, and was non-toxic and stable in solution. Results indicated that tetracycline staining has promise for increasing the specificity of tumor detection in SIM images of a preclinical sarcoma model and further investigation is warranted.

In conclusion, this work presents the development of a combination of tools that is capable of automated segmentation and quantification of micro-anatomical images of thick tissue. When compared to the fluorescence microendoscope, wide-field multispectral fluorescence SIM imaging provided improved image contrast, a larger FOV with comparable resolution, and the ability to image a variety of fluorophores. MSER was an appropriate and rapid approach to segment dense collections of APFs from wide-field SIM images. Variables that reflect the morphology of the tissue, such as the density, size, and shape of nuclei and nucleoli, can be used to automatically diagnose SIM images. The clinical utility of SIM imaging and MSER segmentation to detect microscopic residual disease has been demonstrated by imaging excised preclinical sarcoma margins. Ultimately, this work demonstrates that fluorescence imaging of tissue micro-anatomy combined with a specialized algorithm for delineation and quantification of features is a means for rapid, non-destructive and automated detection of microscopic disease, which could improve cancer management in a variety of clinical scenarios.

Genetic variants in the TEP1 gene are associated with prostate cancer risk and recurrence.

BACKGROUND: Telomere-related genes play an important role in carcinogenesis and progression of prostate cancer (PCa). It is not fully understood whether genetic variations in telomere-related genes are associated with development and progression in PCa patients. METHODS: Six potentially functional single-nucleotide polymorphisms (SNPs) of three key telomere-related genes were evaluated in 1015 PCa cases and 1052 cancer-free controls, to test their associations with risk of PCa. Among 426 PCa patients who underwent radical prostatectomy (RP), the prognostic significance of the studied SNPs on biochemical recurrence (BCR) was also assessed using the Kaplan-Meier analysis and Cox proportional hazards regression model. The relative telomere lengths (RTLs) were measured in peripheral blood leukocytes using real-time PCR in the RP patients. RESULTS: TEP1 rs1760904 AG/AA genotypes were significantly associated with a decreased risk of PCa (odds ratio (OR): 0.77, 95% confidence interval (CI): 0.64-0.93, P=0.005) compared with the GG genotype. By using median RTL as a cutoff level, RP patients with TEP1 rs1760904 AG/AA genotypes tended to have a longer RTL than those with the GG genotype (OR: 1.55, 95% CI: 1.04-2.30, P=0.031). A significant interaction between TEP1 rs1713418 and age in modifying PCa risk was observed (P=0.005). After adjustment for clinicopathologic risk factors, the presence of heterozygotes or rare homozygotes of TEP1 rs1760904 and TNKS2 rs1539042 were associated with BCR in the RP cohorts (hazard ratio: 0.53, 95% CI: 0.36-0.79, P=0.002 and hazard ratio: 1.67, 95% CI: 1.07-2.48, P=0.017, respectively). CONCLUSIONS: These data suggest that genetic variations in the TEP1 gene may be biomarkers for risk of PCa and BCR after RP.

Polymorphisms in the kinesin-like factor 1 B gene and risk of epithelial ovarian cancer in Eastern Chinese women.

The kinesin-like factor 1 B (KIF1B) gene plays an important role in the process of apoptosis and the transformation and progression of malignant cells. Genetic variations in KIF1B may contribute to risk of epithelial ovarian cancer (EOC). In this study of 1,324 EOC patients and 1,386 cancer-free female controls, we investigated associations between two potentially functional single nucleotide polymorphisms in KIF1B and EOC risk by the conditional logistic regression analysis. General linear regression model was used to evaluate the correlation between the number of variant alleles and KIF1B mRNA expression levels. We found that the rs17401966 variant AG/GG genotypes were significantly associated with a decreased risk of EOC (adjusted odds ratio (OR) = 0.81, 95 % confidence interval (CI) = 0.68-0.97), compared with the AA genotype, but no associations were observed for rs1002076. Women who carried both rs17401966 AG/GG and rs1002076 AG/AA genotypes of KIF1B had a 0.82-fold decreased risk (adjusted 95 % CI = 0.69-0.97), compared with others. Additionally, there was no evidence of possible interactions between about-mentioned co-variants. Further genotype-phenotype correlation analysis indicated that the number of rs17401966 variant G allele was significantly associated with KIF1B mRNA expression levels (P for GLM = 0.003 and 0.001 in all and Chinese subjects, respectively), with GG carriers having the lowest level of KIF1B mRNA expression. Taken together, the rs17401966 polymorphism likely regulates KIF1B mRNA expression and thus may be associated with EOC risk in Eastern Chinese women. Larger, independent studies are warranted to validate our findings.

Investigating the inter-relationships between water attenuated irradiance, primary production and DMS(P)

Both solar irradiance and primary production have been proposed as independent controls on seawater dimethyl sulphide (DMS) and dimethylsulphoniopropionate (DMSP) concentrations. However, irradiance also drives photosynthesis, and thus influences a complex set of inter-related processes that modulate marine DMS. We investigate the potential inter-relationships between the rate of primary production (carbon assimilation), water-attenuated irradiance and DMS/DMSP dynamics by applying correlation analysis to a high resolution, concurrently sampled in situ data set from a range of latitudes covering multiple biogeochemical provinces from 3 of the 4 Longhurst biogeochemical domains. The combination of primary production (PP) and underwater irradiance (Iz) within a multivariate regression model is able to explain 55% of the variance in DMS concentrations from all depths within the euphotic zone and 66% of the variance in surface DMS concentrations. Contrary to some previous studies we find a variable representing biological processes is necessary to better account for the variance in DMS. We find that the inclusion of Iz accounts for variance in DMS that is independent from the variance explained by PP. This suggests an important role for solar irradiance (beyond the influence of irradiance upon primary production) in mediating the relationship between the productivity of the ecosystem, DMS/DMSP production and ambient seawater DMS concentrations.

MENTAL HEALTH AND CRIMINALITY AMONG PSYCHIATRIC OFFENDERS IN SOUTHEASTERN ONTARIO

OBJECTIVES: (1) Describe the population of mentally ill offenders over whom Ontario Review Board (ORB) held jurisdiction. (2) Assess the influences of psychopathology and criminal factors on criminal career. METHOD: This study was a retrospective case series design that reviewed all offenders who were court ordered for psychiatric evaluation at Mental Health Services Site of Providence Care in Kingston, Ontario from 1993 to 2007 (N=347). Eighty five subjects were found not criminally responsible on the account of mental disorder and were included in statistical analysis (n=85). Bivariate associations between five key variables and two outcome variables, seriousness of crime and recidivism, were examined. Logistic regressions were conducted to test the role of the predictor variables on the outcome variables. RESULTS: Age and change in principal psychiatric diagnosis over time were shown to be associated with seriousness of crime. Timing of psychiatric onset, early signs of deviance and change in diagnosis were shown to be associated with recidivism. On the whole, study population did not markedly vary in their distribution of variables by the outcome variables. Regression model included timing of psychiatric onset; psychiatric history; existence of criminal associate; child abuse history; and early signs of deviance. Recidivism was shown to be predicted by early signs of deviance (OR=8.154, p<0.05). Existence of criminal associates was shown to have substantial values of odds ratio at marginal significance (OR=7.577, p=0.13). CONCLUSION: Seriousness of crime is a complex factor that could not be sufficiently predicted by any one or combinations of study variables. Recidivism is better predicted by criminality factors than psychopathology. In the future, an exploratory analysis that more broadly examines the psychopathology and criminal factors in Canadian forensic population is needed. Findings from this study have important clinical and legal implications.

Do Total and High Density Lipoprotein Cholesterol and Triglycerides Act Independently in the Prediction of Ischemic Heart Disease? Ten-Year Follow-Up of Caerphilly and Speedwell Cohorts

Several studies have suggested that men with raised plasma triglycerides (TGs) in combination with adverse levels of other lipids may be at special risk of subsequent ischemic heart disease (IHD). We examined the independent and combined effects of plasma lipids at 10 years of follow-up. We measured fasting TGs, total cholesterol (TC), and high density lipoprotein cholesterol (HDLC) in 4362 men (aged 45 to 63 years) from 2 study populations and reexamined them at intervals during a 10-year follow-up. Major IHD events (death from IHD, clinical myocardial infarction, or ECG-defined myocardial infarction) were recorded. Five hundred thirty-three major IHD events occurred. All 3 lipids were strongly and independently predictive of IHD after 10 years of follow-up. Subjects were then divided into 27 groups (ie, 33) by the tertiles of TGs, TC, and HDLC. The number of events observed in each group was compared with that predicted by a logistic regression model, which included terms for the 3 lipids (without interactions) and potential confounding variables. The incidence of IHD was 22.6% in the group with the lipid risk factor combination with the highest expected risk (high TGs, high TC, and low HDLC) and 4.7% in the group with the lowest expected risk (P

Further assessment of the complement component 2 and factor B region associated with age-related macular degeneration

PURPOSE. Polymorphic variation in genes involved in regulation of the complement system has been implicated as a major cause of genetic risk, in addition to the LOC387715/HTRA1 locus and other environmental influences. Previous studies have identified polymorphisms in the complement component 2 (CC2) and factor B (CFB) genes, as potential functional variants associated with AMD, in particular CFB R32Q and CC2 rs547154, both of which share strong linkage disequilibrium (LD). METHODS. Data derived from the HapMap Project were used to select 18 haplotype-tagging SNPs across the extended CC2/ CFB region for genotyping, to measure the strength of LD in 318 patients with neovascular AMD and 243 age-matched control subjects to identify additional potential functional variants in addition to those originally reported. RESULTS. Strong LD was measured across this region as far as the superkiller viralicidic activity 2-like gene (SKIV2L). Nine SNPs were identified to be significantly associated with the genetic effect observed at this locus. Of these, a nonsynonymous coding variant SKIV2L R151Q (rs438999; OR, 0.48; 95% confidence interval [CI], 0.31- 0.74; P < 0.001), was in strong LD with CFB R32Q, rs641153 (r2 = 0.95) and may exert a functional effect. When assessed within a logistic regression model measuring the effects of genetic variation at the CFH and LOC387715/HTRA1 loci and smoking, the effect remained significant (OR, 0.38; 95% CI, 0.22- 0.65; P < 0.001). Additional variation identified within this region may also confer a weaker but independent effect and implicate additional genes within the pathogenesis of AMD. CONCLUSIONS. Because of the high level of LD within the extended CC2/CFB region, variation within SKIV2L may exert a functional effect in AMD. Copyright © Association for Research in Vision and Ophthalmology.

Does governance matter for aggregate health capital?

The point of departure of our analysis is the seminal work of Rodgers (1979) on the absolute and relative income hypotheses. We find that substituting the governance index for the Gini index is statistically the preferred regression model. Our findings lend support to the argument that governance matters. Further investigation provides evidence for two types of threshold effects: in terms of both absolute income and governance. For those countries below a threshold, absolute income is the most significant determinant of health, while for those above it, governance matters the most. The regression analyses are conducted on a sample of 112 states, which is representative of a wide range of absolute income and governance levels.

A hierarchical multiclass support vector machine incorporated with holistic triple learning units

This paper proposes a new hierarchical learning structure, namely the holistic triple learning (HTL), for extending the binary support vector machine (SVM) to multi-classification problems. For an N-class problem, a HTL constructs a decision tree up to a depth of A leaf node of the decision tree is allowed to be placed with a holistic triple learning unit whose generalisation abilities are assessed and approved. Meanwhile, the remaining nodes in the decision tree each accommodate a standard binary SVM classifier. The holistic triple classifier is a regression model trained on three classes, whose training algorithm is originated from a recently proposed implementation technique, namely the least-squares support vector machine (LS-SVM). A major novelty with the holistic triple classifier is the reduced number of support vectors in the solution. For the resultant HTL-SVM, an upper bound of the generalisation error can be obtained. The time complexity of training the HTL-SVM is analysed, and is shown to be comparable to that of training the one-versus-one (1-vs.-1) SVM, particularly on small-scale datasets. Empirical studies show that the proposed HTL-SVM achieves competitive classification accuracy with a reduced number of support vectors compared to the popular 1-vs-1 alternative.

Six ways to leave unemployment

This paper uses a unique Portuguese dataset to examine the effect of access to unemployment benefits (UBs) and their maximum potential duration on escape rates from unemployment. In examining the time profile of transitions out of unemployment, the principal contributions of the paper are twofold. First, it provides a detailed state space of potential outcomes: open-ended employment, fixed-term contracts, part-time work, government-provided jobs, self employment, and labour force withdrawal. Second, it is able to exploit major exogenous discontinuities in the maximum duration of unemployment benefits to identify disincentive effects. While confirming strong disincentive effects, it is shown that use of an aggregate hazard function regression model compounds very different and even contradictory effects of the determinants of unemployment.

Climate change-linked range expansion of Nathusius' pipistrelle bat, Pipistrellus nathusii (Keyserling & Blasius, 1839)

Aim To examine the effect of climate change on the occurrence and distribution of Pipistrellus nathusii (Nathusius' pipistrelle) in the United Kingdom (UK).Location We modelled habitat and climatic associations of P. nathusii in the UK and applied this model to the species' historical range in continental Europe.Methods A binomial logistic regression model was constructed relating the occurrence of P. nathusii to climate and habitat characteristics using historical species occurrence records (1940-2006) and CORINE land cover data. This model was applied to historical and projected climate data to examine changes in suitable range (1940-2080) of this species. We tested the predictive ability of the model with known records in the UK after 2006 and applied the model to the species' known range in Europe.Results The distribution of P. nathusii was related positively to the area of water bodies, woodland and small areas of urbanization, and negatively related to the area of peat/heathland. Species records were associated with higher minimum temperatures, low seasonal variation in temperature and intermediate rainfall. We found that suitable areas have existed in the UK since the 1940s and that these have expanded. The model had high predictive power when applied to new records after 2006, with a correct classification rate of 70%, estimated by receiver operating characteristic analysis. Based on climate projections, our model suggests a potential twofold increase in the area suitable for P. nathusii in the UK by 2050. The single most influential climate variable contributing to range increase was the projected increase in minimum temperature. When applied to Europe, the model predictions had best predictive capability of known records in western areas of the species' range, where P. nathusii is present during the winter.Main conclusions We show that a mobile, migratory species has adapted its range in response to recent climate change on a continental scale. We believe this may be the first study to demonstrate a case of range change linked to contemporary climate change in a mammal species in Europe.