| 11. |
- Udumyan, Ruzan, 1971-
(författare)
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Stress susceptibility, beta-blocker use and cancer survival
- 2020
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)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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| 12. |
- Arribas, Christina, et al.
(författare)
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Global cross-sectional survey on neonatal pharmacologic sedation and analgesia practices and pain assessment tools : impact of the sociodemographic index (SDI)
- 2024
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Ingår i: Pediatric Research. - : Nature Publishing Group. - 0031-3998 .- 1530-0447.
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Tidskriftsartikel (refereegranskat)abstract
- Background: There is variability in the use of sedatives and analgesics in neonatal intensive care units (NICUs). We aimed to investigate the use of analgesics and sedatives and the management of neonatal pain and distress.Methods: This was a global, prospective, cross-sectional study. A survey was distributed May–November 2022. The primary outcome of this research was to compare results between countries depending on their socio-sanitary level using the sociodemographic index (SDI). We organized results based on geographical location.Results: The survey collected 1304 responses, but we analyzed 924 responses after database cleaning. Responses from 98 different countries were analyzed. More than 60% of NICUs reported having an analgosedation guideline, and one-third of respondents used neonatal pain scales in more than 80% of neonates. We found differences in the management of sedation and analgesia between NICUs on different continents, but especially between countries with different SDIs. Countries with a higher SDI had greater availability of and adherence to analgosedation guidelines, as well as higher rates of analgosedation for painful or distressing procedures. Countries with different SDIs reported differences in analgosedation for neonatal intubation, invasive ventilation, and therapeutic hypothermia, among others.Conclusions: Socio-economic status of countries impacts on neonatal analgosedation management.
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| 13. |
- Bergh, Cecilia, 1972-, et al.
(författare)
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Shared unmeasured characteristics among siblings confound the association of Apgar score with stress resilience in adolescence
- 2019
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Ingår i: Acta Paediatrica. - : Wiley-Blackwell Publishing Inc.. - 0803-5253 .- 1651-2227. ; 108:11, s. 2001-2007
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Tidskriftsartikel (refereegranskat)abstract
- AIM: We investigated the association between low Apgar score, other perinatal characteristics and low stress resilience in adolescence. A within-siblings analysis was used to tackle unmeasured shared familial confounding.METHODS: We used a national cohort of 527,763 males born in Sweden between 1973 and 1992 who undertook military conscription assessments at mean age 18 years (17-20). Conscription examinations included a measure of stress resilience. Information on Apgar score and other perinatal characteristics was obtained through linkage with the Medical Birth Register. Analyses were conducted using ordinary least squares and fixed-effects linear regression models adjusted for potential confounding factors.RESULTS: Infants with a prolonged low Apgar score at five minutes had an increased risk of low stress resilience in adolescence compared to those with highest scores at one minute, with an adjusted coefficient and 95% confidence interval of -0.26 (-0.39, -0.13). The associations were no longer statistically significant when using within-siblings models. However, the associations with stress resilience and birthweight remained statistically significant in all analyses.CONCLUSION: The association with low Apgar score seems to be explained by confounding due to shared childhood circumstances among siblings from the same family, while low birthweight is independently associated with low stress resilience.
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| 14. |
- Brand, Judith, 1984-, et al.
(författare)
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Maternal smoking during pregnancy and fractures in offspring : national register based sibling comparison study
- 2020
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Ingår i: The BMJ. - : BMJ Publishing Group Ltd. - 1756-1833. ; 368
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Tidskriftsartikel (refereegranskat)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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| 15. |
- Cao, Yang, Associate Professor, 1972-, et al.
(författare)
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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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Ingår i: BMJ Open. - : BMJ Publishing Group Ltd. - 2044-6055. ; 10:11
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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.
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| 16. |
- Cao, Yang, Associate Professor, 1972-, et al.
(författare)
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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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Ingår i: JMIR Medical Informatics. - : JMIR Publications. - 2291-9694. ; 8:5
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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.
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| 17. |
- Cao, Yang, Associate Professor, 1972-, et al.
(författare)
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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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Ingår i: Journal of Personalized Medicine. - : MDPI. - 2075-4426. ; 13:9
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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.
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| 18. |
- Cao, Yang, Associate Professor, 1972-, et al.
(författare)
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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
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Ingår i: Journal of Clinical Medicine. - : MDPI. - 2077-0383. ; 8:12
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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.
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| 19. |
- Cao, Yang, Associate Professor, 1972-, et al.
(författare)
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Predictive Values of Preoperative Characteristics for 30-Day Mortality in Traumatic Hip Fracture Patients
- 2021
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Ingår i: Journal of Personalized Medicine. - : MDPI. - 2075-4426. ; 11:5
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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.
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| 20. |
- Cao, Yang, Associate Professor, 1972-, et al.
(författare)
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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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Ingår i: BMC Medical Informatics and Decision Making. - : BioMed Central. - 1472-6947. ; 20:1
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Tidskriftsartikel (refereegranskat)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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