The age-dependent association of risk factors with pancreatic cancer

BACKGROUND

Pancreatic cancer presents as advanced disease in >80% of patients; yet, appropriate ages to consider prevention and early detection strategies are poorly defined. We investigated age-specific associations and attributable risks of pancreatic cancer for established modifiable and non-modifiable risk factors.

PATIENTS AND METHODS

We included 167 483 participants from two prospective US cohort studies with 1190 incident cases of pancreatic cancer during >30 years of follow-up; 5107 pancreatic cancer cases and 8845 control participants of European ancestry from a completed multicenter genome-wide association study (GWAS); and 248 893 pancreatic cancer cases documented in the US Surveillance, Epidemiology, and End Results (SEER) Program. Across different age categories, we investigated cigarette smoking, obesity, diabetes, height, and non-O blood group in the prospective cohorts; weighted polygenic risk score of 22 previously identified single nucleotide polymorphisms in the GWAS; and male sex and black race in the SEER Program.

RESULTS

In the prospective cohorts, all five risk factors were more strongly associated with pancreatic cancer risk among younger participants, with associations attenuated among those aged >70 years. The hazard ratios comparing participants with three to five risk factors with those with no risk factors were 9.24 [95% confidence interval (CI) 4.11-20.77] among those aged ≤60 years, 3.00 (95% CI 1.85-4.86) among those aged 61-70 years, and 1.46 (95% CI 1.10-1.94) among those aged >70 years (Pheterogeneity = 3×10-5). These factors together were related to 65.6%, 49.7%, and 17.2% of incident pancreatic cancers in these age groups, respectively. In the GWAS and the SEER Program, the associations with the polygenic risk score, male sex, and black race were all stronger among younger individuals (Pheterogeneity ≤0.01).

CONCLUSIONS

Established risk factors are more strongly associated with earlier-onset pancreatic cancer, emphasizing the importance of age at initiation for cancer prevention and control programs targeting this highly lethal malignancy.

Strain wave pathway to semiconductor-to-metal transition revealed by time-resolved X-ray powder diffraction

One of the main challenges in ultrafast material science is to trigger phase transitions with short pulses of light. Here we show how strain waves, launched by electronic and structural precursor phenomena, determine a coherent macroscopic transformation pathway for the semiconducting-to-metal transition in bistable Ti₃O₅ nanocrystals. Employing femtosecond powder X-ray diffraction, we measure the lattice deformation in the phase transition as a function of time. We monitor the early intra-cell distortion around the light absorbing metal dimer and the long range deformations governed by acoustic waves propagating from the laser-exposed Ti₃O₅ surface. We developed a simplified elastic model demonstrating that picosecond switching in nanocrystals happens concomitantly with the propagating acoustic wavefront, several decades faster than thermal processes governed by heat diffusion.

Abstract AS13: Epidemiologic predictors of pre-treatment CA125 in women with ovarian cancer

Background: CA125 is elevated in 80% of ovarian cancer cases and has proven utility in assessing response to therapy and prognosis. Unfortunately, CA125 is elevated in a variety of benign conditions and only in 50% of early stage ovarian cancers, resulting in a sensitivity and specificity unacceptable for population-based screening. Therefore, understanding factors that influence CA125 at presentation could provide important insights for interpreting CA125 values.

Methods: Using pre-treatment CA125 and detailed epidemiologic data from 12 studies participating in the Ovarian Cancer Association Consortium (OCAC), we evaluated factors previously reported to influence CA125 levels, including age, race, oral contraceptive use, parity, tubal ligation, endometriosis, body mass index (BMI), personal history of breast cancer, or a family history of breast or ovarian cancer. We used linear regression to estimate the association between each variable and CA125 probit scores, which we used to standardize values between studies. Secondary analyses included adjustment for histologic subtype. We also estimated the associations within each study using log-transformed CA125 as the outcome and estimated summary measures using random effects meta-analyses.

Results: Of the 4417 cases included in the analysis, 2918 (66%) were serous, 227 (5%) were mucinous, 484 (11%) were endometrioid, and 258 (6%) were clear cell carcinomas. Median CA125 values varied between studies with a high of 831 U/mL and a low of 271 U/mL. We observed no association between race, oral contraceptive use, tubal ligation, endometriosis, prior breast cancer, and family history of breast cancer and pre-treatment CA125. However, we observed increased pre-treatment CA125 levels with older age (>70 vs. < 50, p=0.03), parity (p=0.01), number of children (p=0.02), obesity (BMI>30 vs. 21-25, p=0.03), and family history of ovarian cancer (p=0.05). Results were similar but attenuated when we calculated summary estimates of the association using meta-analysis of study specific estimates of the association with log-transformed CA125 rather than probit scores. Age and BMI remained predictive of pre-treatment CA125 values after adjustment for histologic subtype. Our data are limited by between site variability in CA125 assays; therefore, validation of these findings is needed with adjustment for type of assay used or in a study population in which all cases had pre-treatment CA125 values measured with the same assay.

Conclusions: Despite these limitations, results from this analysis of over 4000 ovarian cancer cases suggest that age and body size may influence pre-treatment CA125 values.

Citation Format: KL Terry, A Babic, BY Karlan, MT Goodman, D Lambrechts, F Heitz, K Matsuo, I McNeish, T Pejovic, S Kruger Kjaer, PM Webb, E Hogdall, EL Goode, DW Cramer, for the Ovarian Cancer Association Consortium. Epidemiologic predictors of pre-treatment CA125 in women with ovarian cancer [abstract]. In: Proceedings of the 10th Biennial Ovarian Cancer Research Symposium; Sep 8-9, 2014; Seattle, WA. Philadelphia (PA): AACR; Clin Cancer Res 2015;21(16 Suppl):Abstract nr AS13.

Current practices used for the prevention of central venous catheter-associated infection in hematopoietic stem cell transplantation recipients: a survey from the Infectious Diseases Working Party and Nurses' Group of EBMT

BACKGROUND

Central line-associated bloodstream infection (CLABSI) is one of the most common infectious complications after hematopoietic stem cell transplantation. To prevent this complication, international guidelines recommend the implementation of the CLABSI 'prevention bundle' consisting of hand hygiene, full barrier precautions, cleaning the insertion site with chlorhexidine, avoiding femoral sites for insertion, and removing unnecessary catheters. The aim of this survey was to analyze to what extent European Group for Blood and Marrow Transplantation (EBMT) centers have included the CLABSI prevention bundle in practice.

METHODS

A questionnaire used for data collection was sent to the 545 EBMT centers worldwide, 103 of which responded.

RESULTS

All 5 components of the CLABSI prevention bundle were recorded in 28% of the centers' standard operating procedures (SOP), and 21% of the centers answered that they used all of the bundle components in clinical practice. The most common recommendation absent from the SOP was specification of all the components of full barrier precautions (43% of the centers did not include at least 1 component). Skin disinfection with chlorhexidine before catheter insertion was reported by 66% centers. CLABSI rates were monitored in 21% of centers.

CONCLUSIONS

Although most of the centers lacked 1 or more of the CLABSI prevention bundle components in their SOP, improvements could easily be made by updating the centers' SOP. The first important step is introduction of CLABSI rate monitoring in this high-risk patient population.

Filgrastim XM02 (Tevagrastim®) after autologous stem cell transplantation compared to lenograstim: favourable cost-efficacy analysis

PURPOSE

Granulocyte colony-stimulating factors (G-CSFs), filgrastim and lenograstim, are recognised to be useful in accelerating engraftment after autologous stem cell transplantation. Several forms of biosimilar non-glycosylated G-CSF have been approved by the European Medicines Agency, with limited published data supporting the clinical equivalence in peripheral blood stem cell mobilisation and recovery after autologous stem cell transplantation.

METHOD

With the aim of comparing cost-effective strategies in the use of G-CSF after autologous stem cell transplantation, we retrospectively evaluated 32 patients consecutively treated with biosimilar filgrastim XM02 (Tevagrastim) and 26 with lenograstim. All patients received G-CSF (biosimilar or lenograstim) at a dosage of 5 mcg/kg/day subcutaneously from day 5 to absolute neutrophil count of 1500/mmc for three days.

RESULTS

The median time to absolute neutrophil count engraftment was 11 days for the filgrastim XM02 group and 12 days for the lenograstim group. As for platelets recovery, the median time was 12 days in both groups. The median number of G-CSF vials used for patients was 9.5 for Tevagrastim and 10.5 for lenograstim, reflecting a mean estimated cost of about 556.1 euros for Tevagrastim versus 932.2 euros for lenograstim (p< 0.001). The median days of febrile neutropenia were 1.5 and 1 for filgrastim XM02 and lenograstim, respectively. No adverse event related to the use of XM02 filgrastim was recorded.

CONCLUSION

In our experience, filgrastim XM02 and lenograstim showed comparable efficacy in shortening the period of neutropenia after cytoreduction and autologous stem cell transplantation, with a favourable cost effect for filgrastim XM02.

Synergy between medical informatics and bioinformatics: facilitating genomic medicine for future health care

In this paper, we review the results of BIOINFOMED, a study funded by the European Commission (EC) with the purpose to analyse the different issues and challenges in the area where Medical Informatics and Bioinformatics meet. Traditionally, Medical Informatics has been focused on the intersection between computer science and clinical medicine, whereas Bioinformatics have been predominantly centered on the intersection between computer science and biological research. Although researchers from both areas have occasionally collaborated, their training, objectives and interests have been quite different. The results of the Human Genome and related projects have attracted the interest of many professionals, and introduced new challenges that will transform biomedical research and health care. A characteristic of the 'post genomic' era will be to correlate essential genotypic information with expressed phenotypic information. In this context, Biomedical Informatics (BMI) has emerged to describe the technology that brings both disciplines (BI and MI) together to support genomic medicine. In recognition of the dynamic nature of BMI, institutions such as the EC have launched several initiatives in support of a research agenda, including the BIOINFOMED study.

Cytological grading of breast cancer in Giemsa-stained fine needle aspiration smears

A potential cytological nuclear grading based on a semi-quantitative evaluation of three basic nuclear features, size of cell nuclei, anisonucleosis and the proportion of nucleoli-containing-nuclei, was tested on 74 Giemsa-stained fine needle aspiration of breast smears for its reliability in establishing the malignant potential of breast cancer. The prognostic impact of DNA-ploidy and S-phase fraction was also assessed. A good correlation between the three basic nuclear features, DNA-ploidy, S-phase fraction, cytological nuclear grade and histological grade, was shown. Using the cytological nuclear grade proposed, correct classification of cases between low histological grade (HG I) and high histological grade (HG II + HG III) was achieved in 79.73%. A statistically significant difference in 5-year survival rate was also observed between low malignancy grade and high malignancy grade breast cancer patients, regardless of the grading method used. DNA-ploidy and S-phase fraction were not statistically significant in establishing the malignant potential of breast cancer.

Quantitative collagen as a golden standard in differential diagnosing of fibrotic changes in liver tissue

Determining a presence and degree of liver fibrosis provides means for diagnosing disease related processes. We have used two data mining methods, discriminant and regression analyses, to acquire knowledge from the data of 211 patients. We have shown and discussed that quantitative collagen has a distinguished discriminating power and can serve as a golden standard. We have additionally succeeded to obtain a formula consisting of standardised blood tests that can replace quantitative collagen. Practical implications of this is a non-invasive and cost efficient patient examination. All the results are now left for clinical evaluation and so is the current way of histopathological classifications.

Take care: guidelines for patients with chronic hepatitis C

Alcohol consumption has significant impact on the condition of the liver, by itself, and even more in conjunction with other liver diseases such as chronic hepatitis C. Drinking habits might be delicate issues to address and could harm otherwise satisfying communication. Therefore, we intended to outline guidelines for advising hepatitis C patients concerning alcohol consumption. Analysis of a relatively limited knowledge base revealed the complexity of the disease rather than statistically significant findings regarding consumption. Thus, we instead chose to suggest a set of patient educational guidelines, which could be implemented on the Internet, hypothesizing that a better informed patient will be more able to comply with restrictions concerning alcohol consumption. A brief ad hoc evaluation pointed out Internet as a favourable media to present the information. We also suggest a tentative algorithm for further development of clinical decision support systems addressing monitoring of chronic hepatitis C patients.

Knowledge discovery for advanced clinical data management and analysis

Knowledge discovery is a broad research field in which methods are developed to support discovery of novel and potentially useful knowledge from clinical databases and registers in systems for patient care. However, the techniques available are not readily applicable in medical domains, due to, among other reasons, low user friendliness and lack of proper methodological background. Data mining approaches to be explored and improved are predictive modelling, segmentation, dependency modelling, summarization, and change and deviation detection/modelling (in data or knowledge). Another and original contribution of the research is to build up efficient feedback loops. Human experts and available domain expert systems could provide suggestions as how to improve all major steps in the knowledge discovery process such as evaluation of knowledge, choice of data mining methods and data input. A long tradition of collecting and maintaining clinical and administrative data could be found in fields of oncology, cardiology, coronary surgery, social and primary health care medicine. All these areas, that gather data over long periods of time, could benefit from knowledge discovery.

Quantitative data analysis for exploring outcomes in cardiac surgery

The article focuses on possibilities of statistical knowledge exploration to predict outcomes of surgical treatments. The outcomes were defined in relation to the measured peri- and intraoperative data, as well as follow-up patient questionnaire. Clinical consequences are expected in terms of a smaller data set with a better ability to predict the surgery outcomes and a better cost performance. The important questions that could discriminate quality of life (QoL) were: Relief from surgery?, Has cardiac surgery effected earlier symptoms? Work capacity? Consultations after the surgery? The performed data analysis proved to be efficient in the complex data set that was collected. Pain relief was identified to be significant, while relations between measured blood laboratory profile and later QoL were weak.

Knowledge representation forms for data mining methodologies as applied in thoracic surgery

Typical ways of disseminating and using results of clinical research are scientific journals and reports. Presentation forms are condensed and comprehensible mainly to the experts following the specific topics. A vast amount of information remains unutilized due to the complex form of presenting the knowledge. Subject of this research is to explore possibilities of representation and also visualization of the results obtained using data mining methodologies. The intention is to formulate more than scientific ways to communicate facts that are of interest for the clinicians, medical students and even patients. Internet technologies as already widely established media support knowledge representation forms such as hypertext documents and structured knowledge components. The "Assist Me" decision support system for surgical treatment of cardiac patients integrates several forms of data mining and representation methodologies. We are showing a feasibility study in which scientific outcomes were forwarded to a broad group of potential users.

Liver guide for monitoring of chronic hepatitis C

The severity of chronic hepatitis C infection in the individual patient is monitored using blood laboratory findings and liver biopsy. If blood test results could be shown to provide sufficient information concerning the disease, the invasive procedure of liver biopsy could perhaps be avoided in some instances. This study assessed the clinical relevance of blood laboratory tests for detecting disease-related changes in the liver. Histopathological classification was used to assign class membership of the patients and data mining operations were performed in an elaborate way on 19 different data sets. Disease activity could be detected by a small set of blood tests. Extended sets could identify more severe changes, but failed to distinguish them. The extracted rules are implemented as a part of the knowledge base of a corresponding decision support system aimed at specialists and general practitioners.

Aspects of certainty in patient classification using a health-related quality-of-life instrument in inflammatory bowel disease

The study has focused on deriving a certainty measure for the classification of disease activity in patients suffering from inflammatory bowel disease (IBD). The aim is to build an Internet-based health-related quality-of-life (HRQoL) questionnaire to continuously monitor a patient's condition. Data from 109 patients was collected four times in intervals of three months, using a standardized disease-specific quality-of-life questionnaire, the Rating Form of IBD Patient Concern (RFIPC), extended with 11 additional questions. Correlation analysis showed that the RFIPC items along with "general wellbeing" were highly correlated (significance < 0.001). Factor analysis confirmed this high correlation and only one factor was identified among those variables. Multivariate discriminant analysis was successful to 78.1% in classifying between cases of remission and relapse. Implementation of a smooth threshold function decreased the classification error. However, discrimination regarding change in disease activity over time has to be further improved.

Assessing an AI knowledge-base for asymptomatic liver diseases

Discovering not yet seen knowledge from clinical data is of importance in the field of asymptomatic liver diseases. Avoidance of liver biopsy which is used as the ultimate confirmation of diagnosis by making the decision based on relevant laboratory findings only, would be considered an essential support. The system based on Quinlan's ID3 algorithm was simple and efficient in extracting the sought knowledge. Basic principles of applying the AI systems are therefore described and complemented with medical evaluation. Some of the diagnostic rules were found to be useful as decision algorithms i.e. they could be directly applied in clinical work and made a part of the knowledge-base of the Liver Guide, an automated decision support system.

Diagnostic systems for assessing alveolar bone loss

Radiological diagnostics serves as a basic monitoring technique for alveolar bone loss which is a severe consequence of periodontal disease. To evaluate efficacy of Conventional Visual Radiography (CVR), and to assess a complete clinical status, we had used two more diagnostic systems. These are Digital Subtraction Radiography (DSR) and Probing Pocket Depth (PPD). Experimental Periodontitis was studied in 20 beagle dogs based on the measurements taken in the beginning (baseline), and before (11th month) and after the medical treatment (12th month). Data analyses pointed out the same clinical trend, i.e. a significant bone loss prior to medical treatment and its recovery to the initial state. Differences in metrics and measurement errors could be identified as causes for discrepancies between the systems, but a relationship between the CVR and PPD is worth of further research, as these systems do not appear to be entirely compatible, but rather complementary to each other.

Machine learning for the quality of life in inflammatory bowel disease

Presence of a chronic disease influences patients' lives and reinforces demands to accept and then cope with the illness. In the case of inflammatory bowel disease, quality of life greatly differs through phases of remissions and relapses. Could the quality of life questionnaire tell the difference? In this study we are disclosing possibilities of assessing patients' perspectives by analysing analogue scale statements regarding concerns and worries related to ulcerative colitis. Some two hundred Swedish patients, 3/4 in remission and 1/4 in relapse, filled out a booklet containing 36 statements. To characterise the disease activity, we have used multivariate discrimination. To structure and describe in details paths distinguishing the remission from relapse, we have used an artificial intelligence procedure. Applications of the CART (Classification And Regression Trees) algorithm resulted in a set of classifiers which are, based on the similar subsets of significant variables, i.e. statements. Best reached classification accuracy did not exceed 80% in any case. Other classifiers namely, K-nearest-neighbour (KNN), Learning Vector Quantization (LVQ) and Back Propagation Neural Network (BPNN) confirmed that outcome. An expectation that the disease activity should clearly speak throughout the questionnaire held for a certain number of the observations such as pain and suffering, loss of bowel control, dying early, feeling alone, ability to have children, being treated as different and concerns regarding the medication. To highlight the difference of incorrect 20%, K-means clustering was performed. The results settled a basis for a hypothesis that the studied quality of life instrument captures more than the disease activity.

Hemopump treatment in patients with postcardiotomy heart failure

BACKGROUND

This study examined the use of the Hemopump to treat low cardiac output syndrome after cardiopulmonary bypass.

METHODS

We used the Hemopump temporary cardiac assist system in 29 patients with severe left ventricular dysfunction after open heart operations from September 1991 to November 1994.

RESULTS

Five patients were excluded from the study due to initial patient/device-related problems. Ten patients died in the operating room or early during the stay in the intensive care unit due to progressive biventricular failure. Fourteen patients (58.3%) were weaned from the device, and all of them were later discharged. In a subgroup of patients (54%) in whom we had a more aggressive approach for early insertion of the pump, the survival rate was 85%. Preoperative Higging risk score was significantly related to survival.

CONCLUSIONS

The Hemopump can effectively unload a failing left ventricle with preservation of multiorgan perfusion. A minor decrease in kidney function was observed in most patients, but none of the surviving patients needed hemodialysis. One patient required a short period of peritoneal dialysis to get rid of fluid overload. Hemolysis or platelet dysfunction was not a clinical problem.

Machine learning to support diagnostics in the domain of asymptomatic liver disease

Machine learning procedures, in unsupervised and supervised manner, can enable their users to achieve knowledge hardly comprehensible by even the best experts. This is true also if the clinical knowledge has been carefully assembled in a prospective way. A data set including 165 patients with elevated routine laboratory tests was extensively studied according to clinical history, laboratory profile and liver biopsy. Unsupervised learning by Kohonen feature map disclosed 4 groups of patients: the largest one with no or slight histopathological changes (116) and three smaller, more homogenous, with more diseased patients. Standardized histopathological scorings of the liver specimens defined patients into two groups. Fifty-eight of them were, according to the analysis, recommended for a liver biopsy, due to more severe degrees of inflammation and fibrosis. One-hundred and seven of the patients, in whom liver biopsy was retrospectively considered unnecessary, had only minor degrees of inflammation, fibrosis and/or steatosis. Supervised learning, using the inductive systems based on Quinlan's ID3 and CART algorithms, extracted knowledge in the form of decision trees. This approach could define a need for biopsy either with a very few significant findings or by pathways containing quotients and multiplications of the different basic items. These procedures were analyzed and compared for their theoretical and applicative performances. The cluster and Fischerian discriminant analyses were performed in order to compare the classification performance. The medical appropriateness of the obtained results is satisfying, therefore decision support systems, outlined in this study, should be evaluated in wider clinical practice. To achieve this goal, an example of a Medical Logical Module (MLM), based on the Arden Syntax, is given.

A statistically rule-based decision support system for the management of patients with suspected liver disease

The paper describes how a decision support system in liver diseases, mostly oriented to prediction of the necessity for liver biopsy, has been developed. The system designed is a hybrid one and consists of two parts: logical and statistical. The logical part contains rules, formulated on the basis of current medical knowledge, which enables recognition of clear cases; diseased or non-diseased. The unclear cases are classified on the basis of rules statistically extracted from databases. These rules have been reached after a comprehensive exploratory analysis of the sample of 165 patients with slightly to moderately raised levels of routine liver tests but without signs or symptoms of liver diseases. The extracted decision diagrams which simulate traditional medical diagnosis conduct have been found to be superior to discriminant analysis and probabilistic inductive learning. They use only a limited number of laboratory tests to detect the necessity for biopsy.

Methods for knowledge extraction from a clinical database on liver diseases

We performed exploratory data analysis (EDA) to examine the hidden structure in liver disease data. The purpose was to demonstrate the potential of statistical techniques for extracting knowledge from an active HIS (hospital information system) database with decision support. The goal is to give strong support to the creation of new rules or "tuning" of old rules in the knowledge base. This would facilitate utilization of large patient databases, now commonly available, to help build/update decision support systems for improved patient care. Several statistical techniques were investigated. Stepwise discriminant analysis was found to be a good method in discriminating among different disease classes. Results showed that classification strength of a few (3) variables was similar to all the available (19) variables. Other important issues in the work are treatment of missing values as well as atypical values in medical databases. In estimating missing values we utilized both statistical methods and artificial intelligence approaches. Both these approaches were promising in the estimation of missing values. The study showed that several statistical approaches are possible for knowledge extraction from clinical data collected retrospectively.

Integrated approach for designing medical decision support systems with knowledge extracted from clinical databases by statistical methods

In clinical research data is often studied by a particular method without previous analysis of quality or semantic contents which could link clinical database and data analytical (e.g. statistical) procedures. In order to avoid bias caused by this situation, we propose that the analysis of medical data should be divided into two main steps. In the first one we concentrate on conducting the quality, semantic and structure analyses. In the second step our aim is to build an appropriate dictionary of data analysis methods for further knowledge extraction. Methods like robust statistical techniques, procedures for mixed continuous and discrete data, fuzzy linguistic approach, machine learning and neural networks can be included. The results may be evaluated both using test samples and applying other relevant data-analytical techniques to the particular problem under the study.