| 31. |
- Burkill, S., et al.
(författare)
-
MS and the association of the DQB1*0302 allele with pain
- 2019
-
Ingår i: Multiple Sclerosis Journal. - : Sage Publications. - 1352-4585 .- 1477-0970. ; 25:Suppl. 2, s. 437-438
-
Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Introduction: There is an established association between multiple sclerosis (MS) and pain treatment, in particular neuropathic pain. Murine models have confirmed an association between carriage of the DQB1*0302 allele and development of neuropathic pain-like behavior after peripheral nerve injury. Observational studies in patients with spinal disc herniation identified an association between the DQB1*0302 allele and pain, indicating a possible link in humans. This HLA allele has not been previously investigated for its influence on susceptibility to pain in MS patients.Aim: To determine whether the DQB1*0302 genotype is associated with pain in MS patients or member of the general population without MS.Methods: Three Swedish studies (EIMS, GEMS and IMSE) were combined in which enrolled MS patients were matched with 1-2 randomly selected individuals without MS by sex, age and region of residence. Register data was obtained and prescriptions for pain and neuropathic pain were identified as proxy measures for pain. Blood samples were collected and genotyped. Individuals were included if genotype data were available (MS=3877, non-MS=4548). Logistic regression had pain medication use as the outcome, to examine associations with genotype, stratified by MS status.Results: Homo- or heterozygous MS patients with the DQB1*0302 allele had no significantly increased risk of pain (adjusted OR 1.02, 95% CI 0.85-1.23) or neuropathic pain (OR 1.14, 0.97-1.34) compared with MS patients without the allele. Non-MS comparators carrying at least one allele had an increased risk of pain (OR 1.18, 1.03-1.35). Additionally, a zygosity effect appeared present particularly for women in the non-MS cohort, as homozygous individuals had a higher risk of pain compared with heterozygotes. No association was observed for MS patients.Conclusions: The DQB1*0302 allele was associated with increased risk of pain among the non-MS cohort. Zygocity also impacted on pain risk in this cohort, particularly for women. The same was not observed in MS patients, for which no increased risk was detected. In view of previous data, immune functions seem to be involved in the development of pain and the observed associa-tion is likely due to peripheral nerve injuries or peripheral neu-ropathies. The allele was not associated with pain in the MS population, which often stems from CNS lesions.
|
|
| 32. |
- Burkill, Sarah, et al.
(författare)
-
Pharmacological Treatments Preceding Diagnosis Of Progressive Multifocal Leukencephalopathy
- 2016
-
Ingår i: Pharmacoepidemiology and Drug Safety. - : Wiley-Blackwell. - 1053-8569 .- 1099-1557. ; 25:Suppl. 3, s. 496-497
-
Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Background: Progressive multifocal leukencephalopathy (PML) is a rare, often fatal viral disease, which affects the white matter of the brain. It is caused by John Cunningham (JC) polyomavirus, which is present in most people and is usually harm-less. For immunocompromised persons, such as those who are taking immunosuppressive treatments, the risk of JC virus causing PML is increased, although still rare. As PML diagnosis is not always accurate, epidemiology of PML, including the true incidence and patient characteristics, is incompletely described.Objectives: To identify pharmacological treatments preceding diagnosis of definitive, probable and possible PML, after excluding incorrect PML diagnoses by medical record review.Methods: Patients with a PML diagnosis in Sweden between 1988 and 2013 were identified through the Patient register using ICD 9 code 046D and ICD 10code A81.2 (n = 281). Medical records were reviewed and information on clinical characteristics and pharmacological treatments were collected. Each of the diagnoses was determined as definite PML, possible PML, probable PML or non-PML based on the consensus statement for the AAN neuroinfectious disease section published in 2013. (PMCID: 3662270).Results: Medical records for 251 patients (89%) were available and examined. In total, 84 (33%) of the 251 PML diagnoses were confirmed. For those with a record of being exposed to immunosuppressant drugs, 60 (65%) of the 92 records were confirmed as being definite PML. Among 12 patients exposed to rituximab 11 (92%) had definite and 1 (8%) had probable PML. For the 9 natalizumab users, 8 (89%) had definite PML and 1 (11%) was diagnosed incorrectly.Conclusions: A substantial proportion of PML diagnoses recorded in Sweden are incorrect, however amongst those exposed to immunosuppressants such as rituximab and natalizumab the majority of diagnoses are correct. Assessing immunosuppressive drug history could be an important part of the diagnostic processes for PML.
|
|
| 33. |
- Burkill, Sarah, et al.
(författare)
-
The association between exposure to interferon-beta during pregnancy and birth measurements in offspring of women with multiple sclerosis
- 2019
-
Ingår i: PLOS ONE. - : PLOS. - 1932-6203. ; 28:Suppl. 2, s. 371-372
-
Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Interferon-beta (IFN-beta) is a commonly used treatment for multiple sclerosis (MS). Current guidelines recommend cessation of treatment during pregnancy, however the results of past studies on the safety of prenatal exposure to IFN-beta have been conflicting. A large scale study of a population of MS women is therefore warranted.OBJECTIVES: To assess whether, among those born to women with MS, infants prenatally exposed to IFN-beta show evidence of smaller size at birth relative to infants which were not prenatally exposed to any MS disease modifying drugs.METHODS: Swedish and Finnish register data was used. Births to women with MS in Sweden and Finland between 2005-2014 for which a birth measurement for weight, height, and head circumference was available were included. The exposure window was from 6 months prior to LMP to the end of pregnancy.RESULTS: In Sweden, 411 pregnancies were identified as exposed to IFN-beta during the exposure window, and 835 pregnancies were counted as unexposed to any MS DMD. The corresponding numbers for Finland were 232 and 331 respectively. Infants prenatally exposed to interferon-beta were on average 28 grams heavier (p = 0.17), 0.01 cm longer (p = 0.95), and had head circumferences 0.14 cm larger (p = 0.13) in Sweden. In Finland, infants were 50 grams lighter (p = 0.27), 0.02 cm shorter (p = 0.92) and had head circumferences 0.22 cm smaller (p = 0.15) relative to those unexposed.CONCLUSIONS: This study provides evidence that exposure to IFN-beta during pregnancy does not influence birth weight, length, or head circumference.
|
|
| 34. |
- Burkill, Sarah, et al.
(författare)
-
The association between multiple sclerosis and pain medications
- 2019
-
Ingår i: Pain. - : Lippincott Williams & Wilkins. - 0304-3959 .- 1872-6623. ; 160:2, s. 424-432
-
Tidskriftsartikel (refereegranskat)abstract
- Patients with multiple sclerosis (MS) are at greater risk of pain than people without the disease; however, the occurrence and characteristics of pain among these patients are incompletely described. We aimed to assess characteristics of pain amongst MS patients using MS patients who were recruited to participate in 3 studies in Sweden (n = 3877) and were matched with individuals without MS (n = 4548) by sex, year of birth, and region of residence. The Prescribed Drugs Register identified prescribed pain medication, overall and restricted to those given 4 or more prescriptions in 1 year to assess chronic pain. Anatomical therapeutic chemical codes classified whether pain was neuropathic, musculoskeletal, or migraine. Cox-proportional hazard models were used to estimate associations. Our findings showed patients with MS were at increased risk of pain treatment, with a hazard ratio (HR) of 2.52 (95% confidence interval 2.38-2.66). The largest magnitude HR was for neuropathic pain (5.73, 5.07-6.47) for which 34.2% (n = 1326) of the MS and 7.15% (n = 325) of the non-MS cohort were prescribed a treatment. The HR for chronic pain treatment was 3.55 (3.27-3.84), indicating an increased effect size relative to any pain treatment. Chronic neuropathic pain showed the largest HR at 7.43 (6.21-8.89). Neuropathic pain was shown to be the primary mechanism leading to increased risk of pain in patients with MS.
|
|
| 35. |
- Burkill, Sarah, et al.
(författare)
-
The DQB1* 03:02 Genotype and Treatment for Pain in People With and Without Multiple Sclerosis
- 2020
-
Ingår i: Frontiers in Neurology. - : Frontiers. - 1664-2295. ; 11
-
Tidskriftsartikel (refereegranskat)abstract
- Murine models have demonstrated that the major histocompatibility complex (MHC) is associated with pain-like behavior in peripheral nerve injury, however, the same association has not been shown when considering injury to the central nervous system (CNS), which more closely mimics the damage to the CNS experienced by MS patients. Previous research has indicated the DQB1*03:02 allele of the class II HLA genes as being associated with development of neuropathic pain in persons undergoing inguinal hernia surgery or with lumbar spinal disk herniation. Whether this HLA allele plays a part in susceptibility to pain, has not, as far as we are aware, been previously investigated. This study utilizes information on DQB1*03:02 alleles as part of the EIMS, GEMS, and IMSE studies in Sweden. It also uses register data for 3,877 MS patients, and 4,548 matched comparators without MS, to assess whether the DQB1*03:02 allele is associated with prescribed pain medication use, and whether associations with this genotype differ depending on MS status. Our results showed no association between the DQB1*03:02 genotype and pain medication in MS patients, with an adjusted odds ratio (OR) of 1.02 (95% CI 0.85-1.24). In contrast, there was a statistically significant association of low magnitude in individuals without MS [adjusted OR 1.18 (95% CI 1.03-1.35)], which provides support for HLA influence on susceptibility to pain in the general population. Additionally, the effect of zygosity was evident for the non-MS cohort, but not among MS patients, suggesting the DQB1*03:02 allele effect is modified by the presence of MS.
|
|
| 36. |
- Cao, Yang, Associate Professor, 1972-, et al.
(författare)
-
COVID-19 case-fatality rate and demographic and socioeconomic influencers : worldwide spatial regression analysis based on country-level data
- 2020
-
Ingår i: BMJ Open. - : BMJ Publishing Group Ltd. - 2044-6055. ; 10:11
-
Tidskriftsartikel (refereegranskat)abstract
- OBJECTIVE: To investigate the influence of demographic and socioeconomic factors on the COVID-19 case-fatality rate (CFR) globally.DESIGN: Publicly available register-based ecological study.SETTING: Two hundred and nine countries/territories in the world.PARTICIPANTS: Aggregated data including 10 445 656 confirmed COVID-19 cases.PRIMARY AND SECONDARY OUTCOME MEASURES: COVID-19 CFR and crude cause-specific death rate were calculated using country-level data from the Our World in Data website.RESULTS: The average of country/territory-specific COVID-19 CFR is about 2%-3% worldwide and higher than previously reported at 0.7%-1.3%. A doubling in size of a population is associated with a 0.48% (95% CI 0.25% to 0.70%) increase in COVID-19 CFR, and a doubling in the proportion of female smokers is associated with a 0.55% (95% CI 0.09% to 1.02%) increase in COVID-19 CFR. The open testing policies are associated with a 2.23% (95% CI 0.21% to 4.25%) decrease in CFR. The strictness of anti-COVID-19 measures was not statistically significantly associated with CFR overall, but the higher Stringency Index was associated with higher CFR in higher-income countries with active testing policies (regression coefficient beta=0.14, 95% CI 0.01 to 0.27). Inverse associations were found between cardiovascular disease death rate and diabetes prevalence and CFR.CONCLUSION: The association between population size and COVID-19 CFR may imply the healthcare strain and lower treatment efficiency in countries with large populations. The observed association between smoking in women and COVID-19 CFR might be due to the finding that the proportion of female smokers reflected broadly the income level of a country. When testing is warranted and healthcare resources are sufficient, strict quarantine and/or lockdown measures might result in excess deaths in underprivileged populations. Spatial dependence and temporal trends in the data should be taken into account in global joint strategy and/or policy making against the COVID-19 pandemic.
|
|
| 37. |
- Cao, Yang, Associate Professor, 1972-, et al.
(författare)
-
Deep Learning Neural Networks to Predict Serious Complications After Bariatric Surgery : Analysis of Scandinavian Obesity Surgery Registry Data
- 2020
-
Ingår i: JMIR Medical Informatics. - : JMIR Publications. - 2291-9694. ; 8:5
-
Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Obesity is one of today's most visible public health problems worldwide. Although modern bariatric surgery is ostensibly considered safe, serious complications and mortality still occur in some patients.OBJECTIVE: This study aimed to explore whether serious postoperative complications of bariatric surgery recorded in a national quality registry can be predicted preoperatively using deep learning methods.METHODS: Patients who were registered in the Scandinavian Obesity Surgery Registry (SOReg) between 2010 and 2015 were included in this study. The patients who underwent a bariatric procedure between 2010 and 2014 were used as training data, and those who underwent a bariatric procedure in 2015 were used as test data. Postoperative complications were graded according to the Clavien-Dindo classification, and complications requiring intervention under general anesthesia or resulting in organ failure or death were considered serious. Three supervised deep learning neural networks were applied and compared in our study: multilayer perceptron (MLP), convolutional neural network (CNN), and recurrent neural network (RNN). The synthetic minority oversampling technique (SMOTE) was used to artificially augment the patients with serious complications. The performances of the neural networks were evaluated using accuracy, sensitivity, specificity, Matthews correlation coefficient, and area under the receiver operating characteristic curve.RESULTS: In total, 37,811 and 6250 patients were used as the training data and test data, with incidence rates of serious complication of 3.2% (1220/37,811) and 3.0% (188/6250), respectively. When trained using the SMOTE data, the MLP appeared to have a desirable performance, with an area under curve (AUC) of 0.84 (95% CI 0.83-0.85). However, its performance was low for the test data, with an AUC of 0.54 (95% CI 0.53-0.55). The performance of CNN was similar to that of MLP. It generated AUCs of 0.79 (95% CI 0.78-0.80) and 0.57 (95% CI 0.59-0.61) for the SMOTE data and test data, respectively. Compared with the MLP and CNN, the RNN showed worse performance, with AUCs of 0.65 (95% CI 0.64-0.66) and 0.55 (95% CI 0.53-0.57) for the SMOTE data and test data, respectively.CONCLUSIONS: MLP and CNN showed improved, but limited, ability for predicting the postoperative serious complications after bariatric surgery in the Scandinavian Obesity Surgery Registry data. However, the overfitting issue is still apparent and needs to be overcome by incorporating intra- and perioperative information.
|
|
| 38. |
- Cao, Yang, Associate Professor, 1972-, et al.
(författare)
-
Development and Validation of an XGBoost-Algorithm-Powered Survival Model for Predicting In-Hospital Mortality Based on 545,388 Isolated Severe Traumatic Brain Injury Patients from the TQIP Database
- 2023
-
Ingår i: Journal of Personalized Medicine. - : MDPI. - 2075-4426. ; 13:9
-
Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Traumatic brain injury (TBI) represents a significant global health issue; the traditional tools such as the Glasgow Coma Scale (GCS) and Abbreviated Injury Scale (AIS) which have been used for injury severity grading, struggle to capture outcomes after TBI.AIM AND METHODS: This paper aims to implement extreme gradient boosting (XGBoost), a powerful machine learning algorithm that combines the predictions of multiple weak models to create a strong predictive model with high accuracy and efficiency, in order to develop and validate a predictive model for in-hospital mortality in patients with isolated severe traumatic brain injury and to identify the most influential predictors. In total, 545,388 patients from the 2013-2021 American College of Surgeons Trauma Quality Improvement Program (TQIP) database were included in the current study, with 80% of the patients used for model training and 20% of the patients for the final model test. The primary outcome of the study was in-hospital mortality. Predictors were patients' demographics, admission status, as well as comorbidities, and clinical characteristics. Penalized Cox regression models were used to investigate the associations between the survival outcomes and the predictors and select the best predictors. An extreme gradient boosting (XGBoost)-powered Cox regression model was then used to predict the survival outcome. The performance of the models was evaluated using the Harrell's concordance index (C-index). The time-dependent area under the receiver operating characteristic curve (AUC) was used to evaluate the dynamic cumulative performance of the models. The importance of the predictors in the final prediction model was evaluated using the Shapley additive explanations (SHAP) value.RESULTS: On average, the final XGBoost-powered Cox regression model performed at an acceptable level for patients with a length of stay up to 250 days (mean time-dependent AUC = 0.713) in the test dataset. However, for patients with a length of stay between 20 and 213 days, the performance of the model was relatively poor (time-dependent AUC < 0.7). When limited to patients with a length of stay ≤20 days, which accounts for 95.4% of all the patients, the model achieved an excellent performance (mean time-dependent AUC = 0.813). When further limited to patients with a length of stay ≤5 days, which accounts for two-thirds of all the patients, the model achieved an outstanding performance (mean time-dependent AUC = 0.917).CONCLUSION: The XGBoost-powered Cox regression model can achieve an outstanding predictive ability for in-hospital mortality during the first 5 days, primarily based on the severity of the injury, the GCS on admission, and the patient's age. These variables continue to demonstrate an excellent predictive ability up to 20 days after admission, a period of care that accounts for over 95% of severe TBI patients. Past 20 days of care, other factors appear to be the primary drivers of in-hospital mortality, indicating a potential window of opportunity for improving outcomes.
|
|
| 39. |
- Cao, Yang, Associate Professor, 1972-, et al.
(författare)
-
Predicting Long-Term Health-Related Quality of Life after Bariatric Surgery Using a Conventional Neural Network : A Study Based on the Scandinavian Obesity Surgery Registry
- 2019
-
Ingår i: Journal of Clinical Medicine. - : MDPI. - 2077-0383. ; 8:12
-
Tidskriftsartikel (refereegranskat)abstract
- Severe obesity has been associated with numerous comorbidities and reduced health-related quality of life (HRQoL). Although many studies have reported changes in HRQoL after bariatric surgery, few were long-term prospective studies. We examined the performance of the convolution neural network (CNN) for predicting 5-year HRQoL after bariatric surgery based on the available preoperative information from the Scandinavian Obesity Surgery Registry (SOReg). CNN was used to predict the 5-year HRQoL after bariatric surgery in a training dataset and evaluated in a test dataset. In general, performance of the CNN model (measured as mean squared error, MSE) increased with more convolution layer filters, computation units, and epochs, and decreased with a larger batch size. The CNN model showed an overwhelming advantage in predicting all the HRQoL measures. The MSEs of the CNN model for training data were 8% to 80% smaller than those of the linear regression model. When the models were evaluated using the test data, the CNN model performed better than the linear regression model. However, the issue of overfitting was apparent in the CNN model. We concluded that the performance of the CNN is better than the traditional multivariate linear regression model in predicting long-term HRQoL after bariatric surgery; however, the overfitting issue needs to be mitigated using more features or more patients to train the model.
|
|
| 40. |
- Cao, Yang, Associate Professor, 1972-, et al.
(författare)
-
Predictive Values of Preoperative Characteristics for 30-Day Mortality in Traumatic Hip Fracture Patients
- 2021
-
Ingår i: Journal of Personalized Medicine. - : MDPI. - 2075-4426. ; 11:5
-
Tidskriftsartikel (refereegranskat)abstract
- Hip fracture patients have a high risk of mortality after surgery, with 30-day postoperative rates as high as 10%. This study aimed to explore the predictive ability of preoperative characteristics in traumatic hip fracture patients as they relate to 30-day postoperative mortality using readily available variables in clinical practice. All adult patients who underwent primary emergency hip fracture surgery in Sweden between 2008 and 2017 were included in the analysis. Associations between the possible predictors and 30-day mortality was performed using a multivariate logistic regression (LR) model; the bidirectional stepwise method was used for variable selection. An LR model and convolutional neural network (CNN) were then fitted for prediction. The relative importance of individual predictors was evaluated using the permutation importance and Gini importance. A total of 134,915 traumatic hip fracture patients were included in the study. The CNN and LR models displayed an acceptable predictive ability for predicting 30-day postoperative mortality using a test dataset, displaying an area under the ROC curve (AUC) of as high as 0.76. The variables with the highest importance in prediction were age, sex, hypertension, dementia, American Society of Anesthesiologists (ASA) classification, and the Revised Cardiac Risk Index (RCRI). Both the CNN and LR models achieved an acceptable performance in identifying patients at risk of mortality 30 days after hip fracture surgery. The most important variables for prediction, based on the variables used in the current study are age, hypertension, dementia, sex, ASA classification, and RCRI.
|
|
