| 1. |
- Karlqvist, Sara, 1992-
(author)
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Clinical aspects of biological treatment in inflammatory bowel disease
- 2024
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Doctoral thesis (other academic/artistic)abstract
- Inflammatory bowel disease (IBD) including its main subtypes, Crohn’s disease and ulcerative colitis, is a chronic and recurrent inflammatory condition that affects the entire gastrointestinal system. Biological treatment has revolutionized the therapeutic armamentarium in the past two decades. The growing number of therapeutic options advocates for head-to-head comparisons, evaluation in clinical practice and assessment of safety. Therefore, this thesis aims to evaluate different facets of biological treatment in real-world cohorts.In Paper I, we examined the potential effectiveness of golimumab in Crohn’s disease using data from The Swedish National Quality Register for Inflammatory Bowel Disease (SWIBREG). The findings indicate a drug retention rate of 35% after a median follow-up of 89 (IQR: 32–158) weeks. Paper II constituted a prospective, multicentre, observational cohort study investigating the effectiveness of vedolizumab and its impact on quality of life in a Swedish clinical setting. The percentage of patients in clinical remission after 52 weeks was 41% for Crohn's disease and 47% for ulcerative colitis. Improvements in biochemical markers and health-related quality of life measures were observed at 12 and 52 weeks in both subtypes of IBD. In Paper III, second-line biological treatments were compared in propensity score-matched cohorts based on combined data from multiple high-quality Swedish nationwide registers. The effectiveness and safety of secondline anti-TNF and vedolizumab were similar at 12 months in Crohn’s disease (n=198) and ulcerative colitis (n=202). Based on propensity score-matched data from nationwide health registers, Paper IV showed that vedolizumab was associated with higher hazard ratios of serious infections than anti-TNF in Crohn’s disease but not in ulcerative colitis.To conclude, this thesis suggests that golimumab might have a role in treating Crohn’s disease. It also increased knowledge about the real-world effectiveness of vedolizumab. Lastly, the thesis underscored aspects of efficacy and safety when contrasting vedolizumab with anti-TNF.
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| 2. |
- Lybeck, Charlotte, 1979-
(author)
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Towards the elimination of hepatitis C : identifying the infected population, and remaining hepatitis C related risks after successful treatment
- 2022
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Doctoral thesis (other academic/artistic)abstract
- Chronic Hepatitis C virus (HCV) infection can lead to liver fibrosis and cirrhosis with increased risk of hepatocellular carcinoma (HCC) and liver failure. The World Health Organisation (WHO) has set a goal to eliminate viral hepatitis as a global health threat by 2030. To reach this goal for HCV we need to prevent new infections and identify and treat the infected population. Individuals with pre-treatment cirrhosis still have an elevated risk for HCC after HCV cure. This thesis aims to assess the health outcomes after cured HCV infection, study HCV prevalence and find a way to identify undiagnosed infections.In Paper I, 97 patients were followed through clinical visits (n=54) or through national registers (n=43) to study the long-term outcomes after cure from HCV and to assess the presence and impact of occult HCV infection (OCI). Three non-cirrhotic patients were diagnosed with HCC 8-11 years after HCV cure. Two patients had OCI at 8-9 years after cure. They had liver fibrosis stage 2, but no association with HCC. In Paper II, pregnant women (n=4,108) and partners (n=1,027) at antenatal clinics in southern Stockholm and Örebro County were tested for HCV and interviewed about risk factors to assess prevalence and evaluate screening strategies to identify undiagnosed infections. Anti-HCV prevalence was 0.7% and 0.4% were viraemic. The most effective risk factor-based screening was to ask for drug use, country of birth, and having a partner with HCV. Paper III presents a nationwide register study of the risk of extrahepatic cancer (EHC) the first 3 years after HCV treatment with direct acting antiviral (DAAs). We compared 4,013 DAA-treated, with 3,071 interferon-treated and 12,601 untreated patients. No increased risk for EHC was found after adjustments for age and comorbidities. An increased EHC risk in DAA-treated compared with general population was seen. Paper IV presents a register based study of the risk of HCC and association with pre-treatment liver stiffness measurement (LSM) in 7,227 HCV infected patients cured by DAAs. We found that pre-treatment LSM values correlated well with HCC risk. The incidence rate for patients with LSM values ≥12.5 kPa and <12.5 kPa was 1.6 and 0.15/100 person years, respectively.To conclude, cured HCV infection usually leads to regression of fibrosis. The DAAs are safe and highly effective against HCV. However, the HCC risk remains elevated for many years after cure in cirrhotic and sometimes in non-cirrhotic patients. Furthermore, HCV screening of pregnant women and partners is useful to identify patients who would benefit from therapy.
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| 3. |
- Shrestha, Sarita, 1991-
(author)
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Impact of age and inflammation on extraintestinal manifestations of inflammatory bowel disease
- 2024
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Doctoral thesis (other academic/artistic)abstract
- Inflammatory bowel disease (IBD), encompassing Crohn's disease and ulcerative colitis, is often complicated by extraintestinal manifestations (EIMs) that affect organs beyond the gastrointestinal tract. The EIMs can significantly impair quality of life and complicate disease management in patients with IBD. As the population ages, understanding the interplay between age, chronic inflammation, and EIMs becomes increasingly important for optimizing patient care and outcomes. Thus, the overall aim of the thesis was to assess the impact of age and inflammation on EIM of IBD.We conducted all studies using Swedish national registers. To evaluate the accuracy of the National patient register (NPR), we compared International classification of diseases (ICD) coded IBD data with clinical records from 1403 IBD patients. For investigating spondyloarthritis (SpA) comorbidity, a cohort of 39,203 IBD patients diagnosed between 2006-2016 and 390,490 matched reference individuals were analyzed. The familial risk of SpA was assessed among 147,080 first-degree relatives (FDRs) and 25,945 spouses of IBD patients. The influence of colectomy on SpA and other EIMs was studied in 3246 ulcerative colitis patients, comparing EIM rates before and after surgery.The NPR showed high positive predictive values (PPVs) for Crohn's disease (97%) and ulcerative colitis (98%) but a low PPV for IBD-unclassified (8%). Crohn's disease location and behaviour had variable PPVs, indicating frequent misclassification. A high PPV (95%) was observed for age at diagnosis of IBD. Patients with IBD had significantly higher relative risk estimates of SpA both before (odds ratio [OR]: 3.48) and after (hazard ratio [HR]: 7.15) IBD diagnosis compared to the general population. FDRs of IBD patients exhibited a higher risk of SpA (HR: 1.35). Spouses also had an elevated SpA risk, suggesting environmental influences. The highest risks were observed in Crohn's disease and pediatric-onset IBD. Post-colectomy, patients with ulcerative colitis experienced increased EIM rates (rate ratios [RR]: 1.83). In addition, de novo EIMs frequently occurred in patients with no history of EIMs before colectomy.The NPR is a reliable source for subtype of IBD, although improvements are needed for phenotypic accuracy. IBD significantly increases the risk of SpA. The elevated SpA risk among FDRs and spouses points to shared genetic and environmental factors. Colectomyin patients with ulcerative colitis does not mitigate the risk of EIMs, indicating a need for continued monitoring and management post-surgery. This thesis underscores the importance of comprehensive care approaches that address both gastrointestinal and extraintestinal challenges in IBD.
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| 4. |
- Udumyan, Ruzan, 1971-
(author)
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Stress susceptibility, beta-blocker use and cancer survival
- 2020
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Doctoral thesis (other academic/artistic)abstract
- Accumulating evidence suggests that chronic stress may influence tumour biology through activation of neuroendocrine pathways and thus impair survival. However, measuring stressful exposures and their influence on health is challenging, partly due to substantial inter-individual variation in stress susceptibility. The thesis aimed to explore whether stress resilience and use of β-adrenergic receptor blockers, which are implicated in regulation of neuroendocrine stress response pathways, are linked to survival after a primary cancer diagnosis using data from Swedish national registers. In a cohort of male cancer patients born during 1952-1956 who had their stress resilience assessed during a mandatory conscription examination in late adolescence, low compared with high stress resilience was associated with a higher overall mortality rate. Statistically significant reductions in survival were observed among men with carcinomas of the oropharynx, prostate, upper respiratory tract, and Hodgkin’s lymphoma. In a cohort of patients diagnosed with pancreatic adenocarcinoma during 2006-2009, β-blocker users had a lower pancreatic cancer mortality rate than non-users, particularly among patients without distant metastases at diagnosis. In a cohort of patients diagnosed with non-small cell lung cancer during 2006-2014, there was no clear association between β-blocker use and lung cancer survival, but we cannot exclude the possibility of associations in some sub-groups defined by histology, stage and β-blocker types. In a cohort of patients diagnosed with hepatocellular carcinoma during 2006-2014, β-blocker use was associated with lower liver cancer mortality, particularly among patients with localised disease. A higher-magnitude inverse association was observed for non-selective β-blocker use. In conclusion, greater stress resilience and β-blocker use are associated with improved survival among patients with some cancer types, and this may be explained by a variety of pathways.
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| 5. |
- Brand, Judith, 1984-, et al.
(author)
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Maternal smoking during pregnancy and fractures in offspring : national register based sibling comparison study
- 2020
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In: The BMJ. - : BMJ Publishing Group Ltd. - 1756-1833. ; 368
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Journal article (peer-reviewed)abstract
- OBJECTIVE: To study the impact of maternal smoking during pregnancy on fractures in offspring during different developmental stages of life.DESIGN: National register based birth cohort study with a sibling comparison design.SETTING: Sweden.PARTICIPANTS: 1 680 307 people born in Sweden between 1983 and 2000 to women who smoked (n=377 367, 22.5%) and did not smoke (n=1 302 940) in early pregnancy. Follow-up was until 31 December 2014.MAIN OUTCOME MEASURE: Fractures by attained age up to 32 years.RESULTS: During a median follow-up of 21.1 years, 377 970 fractures were observed (the overall incidence rate for fracture standardised by calendar year of birth was 11.8 per 1000 person years). The association between maternal smoking during pregnancy and risk of fracture in offspring differed by attained age. Maternal smoking was associated with a higher rate of fractures in offspring before 1 year of age in the entire cohort (birth year standardised fracture rates in those exposed and unexposed to maternal smoking were 1.59 and 1.28 per 1000 person years, respectively). After adjustment for potential confounders the hazard ratio for maternal smoking compared with no smoking was 1.27 (95% confidence interval 1.12 to 1.45). This association followed a dose dependent pattern (compared with no smoking, hazard ratios for 1-9 cigarettes/day and >= 10 cigarettes/day were 1.20 (95% confidence interval 1.03 to 1.39) and 1.41 (1.18 to 1.69), respectively) and persisted in within-sibship comparisons although with wider confidence intervals (compared with no smoking, 1.58 (1.01 to 2.46)). Maternal smoking during pregnancy was also associated with an increased fracture incidence in offspring from age 5 to 32 years in whole cohort analyses, but these associations did not follow a dose dependent gradient. In within-sibship analyses, which controls for confounding by measured and unmeasured shared familial factors, corresponding point estimates were all close to null. Maternal smoking was not associated with risk of fracture in offspring between the ages of 1 and 5 years in any of the models.CONCLUSION: Prenatal exposure to maternal smoking is associated with an increased rate of fracture during the first year of life but does not seem to have a long lasting biological influence on fractures later in childhood and up to early adulthood.
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| 6. |
- Cao, Yang, Associate Professor, 1972-, et al.
(author)
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COVID-19 case-fatality rate and demographic and socioeconomic influencers : worldwide spatial regression analysis based on country-level data
- 2020
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In: BMJ Open. - : BMJ Publishing Group Ltd. - 2044-6055. ; 10:11
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Journal article (peer-reviewed)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.
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| 7. |
- Cao, Yang, Associate Professor, 1972-, et al.
(author)
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Deep Learning Neural Networks to Predict Serious Complications After Bariatric Surgery : Analysis of Scandinavian Obesity Surgery Registry Data
- 2020
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In: JMIR Medical Informatics. - : JMIR Publications. - 2291-9694. ; 8:5
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Journal article (peer-reviewed)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.
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| 8. |
- Cao, Yang, Associate Professor, 1972-, et al.
(author)
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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
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In: Journal of Personalized Medicine. - : MDPI. - 2075-4426. ; 13:9
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Journal article (peer-reviewed)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.
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| 9. |
- Cao, Yang, Associate Professor, 1972-, et al.
(author)
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Predictive Values of Preoperative Characteristics for 30-Day Mortality in Traumatic Hip Fracture Patients
- 2021
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In: Journal of Personalized Medicine. - : MDPI. - 2075-4426. ; 11:5
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Journal article (peer-reviewed)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.
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| 10. |
- Cao, Yang, Associate Professor, 1972-, et al.
(author)
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The statistical importance of P-POSSUM scores for predicting mortality after emergency laparotomy in geriatric patients
- 2020
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In: BMC Medical Informatics and Decision Making. - : BioMed Central. - 1472-6947. ; 20:1
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Journal article (peer-reviewed)abstract
- BACKGROUND: Geriatric patients frequently undergo emergency general surgery and accrue a greater risk of postoperative complications and fatal outcomes than the general population. It is highly relevant to develop the most appropriate care measures and to guide patient-centered decision-making around end-of-life care. Portsmouth - Physiological and Operative Severity Score for the enumeration of Mortality and morbidity (P-POSSUM) has been used to predict mortality in patients undergoing different types of surgery. In the present study, we aimed to evaluate the relative importance of the P-POSSUM score for predicting 90-day mortality in the elderly subjected to emergency laparotomy from statistical aspects.METHODS: One hundred and fifty-seven geriatric patients aged ≥65 years undergoing emergency laparotomy between January 1st, 2015 and December 31st, 2016 were included in the study. Mortality and 27 other patient characteristics were retrieved from the computerized records of Örebro University Hospital in Örebro, Sweden. Two supervised classification machine methods (logistic regression and random forest) were used to predict the 90-day mortality risk. Three scalers (Standard scaler, Robust scaler and Min-Max scaler) were used for variable engineering. The performance of the models was evaluated using accuracy, sensitivity, specificity and area under the receiver operating characteristic curve (AUC). Importance of the predictors were evaluated using permutation variable importance and Gini importance.RESULTS: The mean age of the included patients was 75.4 years (standard deviation =7.3 years) and the 90-day mortality rate was 29.3%. The most common indication for surgery was bowel obstruction occurring in 92 (58.6%) patients. Types of post-operative complications ranged between 7.0-36.9% with infection being the most common type. Both the logistic regression and random forest models showed satisfactory performance for predicting 90-day mortality risk in geriatric patients after emergency laparotomy, with AUCs of 0.88 and 0.93, respectively. Both models had an accuracy > 0.8 and a specificity ≥0.9. P-POSSUM had the greatest relative importance for predicting 90-day mortality in the logistic regression model and was the fifth important predictor in the random forest model. No notable change was found in sensitivity analysis using different variable engineering methods with P-POSSUM being among the five most accurate variables for mortality prediction.CONCLUSION: P-POSSUM is important for predicting 90-day mortality after emergency laparotomy in geriatric patients. The logistic regression model and random forest model may have an accuracy of > 0.8 and an AUC around 0.9 for predicting 90-day mortality. Further validation of the variables' importance and the models' robustness is needed by use of larger dataset.
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