Modelling the effect of demographic change and healthcare infrastructure on the patient structure in German hospitals - a longitudinal national study based on official hospital statistics.

BACKGROUND: Effects of demographic change, such as declining birth rates and increasing individual life expectancy, require health system adjustments offering age- and needs-based care. In addition, healthcare factors can also influence health services demand. METHODS: The official German hospital statistics database with odd-numbered years between 1995 and 2011 was analysed. This is a national comprehensive database of all general hospital inpatient services delivered. Official data from hospital statistics were linked at the district level with demographic and socio-economic data as well as population figures from the official regional statistics. Panel data regression, modelling case numbers per hospital, was performed for 13 diagnosis groups that characterised the patient structure. Socio-demographic variables included age, sex, household income, and healthcare factors included bed capacity, personnel and hospital characteristics. RESULTS: The median number of annual treatments per hospital increased from 6 015 (5th and 95th percentile [670; 24 812]) in 1995 to 7 817 in 2011 (5th and 95th percentile [301; 33 651]). We developed models characterising the patient structure of health care in Germany, considering both socio-demographic and hospital factors. Demographic factors influenced case numbers across all major diagnosis groups. For example, the age groups 65-74 and 75 + influenced cerebrovascular disease case numbers (p < 0.001). Other important factors included human and material resources of hospitals or the household income of patients. Distinct differences between the models for the individual diagnosis groups were observed. CONCLUSIONS: Hospital planning should not only consider demographic change but also hospital infrastructure and socio-economic factors.

Continuous therapy response references for BCR::ABL1 monitoring in pediatric chronic myeloid leukemia.

Response to tyrosine kinase inhibitor (TKI) therapy in patients with chronic myeloid leukemia (CML) is monitored by quantification of BCR::ABL1 transcript levels. Milestones for assessing optimal treatment response have been defined in adult CML patients and are applied to children and adolescents although it is questionable whether transferability to pediatric patients is appropriate regarding genetic and clinical differences. Therefore, we analyzed the molecular response kinetics to TKI therapy in 129 pediatric CML patients and investigated whether response assessment based on continuous references can support an early individual therapy adjustment. We applied a moving quantiles approach to establish a high-resolution response target curve and contrasted the median responses in all patients with the median of the ideal target curve obtained from a subgroup of optimal responders. The high-resolution response target curve of the optimal responder group presents a valuable tool for continuous therapy monitoring of individual pediatric CML patients in addition to the fixed milestones. By further comparing BCR::ABL1 transcript levels with BCR::ABL1 fusion gene copy numbers, it is also possible to model the differential dynamics of BCR::ABL1 expression and cell number under therapy. The developed methodology can be transferred to other biomarkers for continuous therapy monitoring.

Data integration between clinical research and patient care: A framework for context-depending data sharing and in silico predictions.

The transfer of new insights from basic or clinical research into clinical routine is usually a lengthy and time-consuming process. Conversely, there are still many barriers to directly provide and use routine data in the context of basic and clinical research. In particular, no coherent software solution is available that allows a convenient and immediate bidirectional transfer of data between concrete treatment contexts and research settings. Here, we present a generic framework that integrates health data (e.g., clinical, molecular) and computational analytics (e.g., model predictions, statistical evaluations, visualizations) into a clinical software solution which simultaneously supports both patient-specific healthcare decisions and research efforts, while also adhering to the requirements for data protection and data quality. Specifically, our work is based on a recently established generic data management concept, for which we designed and implemented a web-based software framework that integrates data analysis, visualization as well as computer simulation and model prediction with audit trail functionality and a regulation-compliant pseudonymization service. Within the front-end application, we established two tailored views: a clinical (i.e., treatment context) perspective focusing on patient-specific data visualization, analysis and outcome prediction and a research perspective focusing on the exploration of pseudonymized data. We illustrate the application of our generic framework by two use-cases from the field of haematology/oncology. Our implementation demonstrates the feasibility of an integrated generation and backward propagation of data analysis results and model predictions at an individual patient level into clinical decision-making processes while enabling seamless integration into a clinical information system or an electronic health record.

Impact of BCR::ABL1 transcript type on RT-qPCR amplification performance and molecular response to therapy.

Several studies have reported that chronic myeloid leukaemia (CML) patients expressing e14a2 BCR::ABL1 have a faster molecular response to therapy compared to patients expressing e13a2. To explore the reason for this difference we undertook a detailed technical comparison of the commonly used Europe Against Cancer (EAC) BCR::ABL1 reverse transcriptase quantitative polymerase chain reaction (RT-qPCR) assay in European Treatment and Outcome Study (EUTOS) reference laboratories (n = 10). We found the amplification ratio of the e13a2 amplicon was 38% greater than e14a2 (p = 0.015), and the amplification efficiency was 2% greater (P = 0.17). This subtle difference led to measurable transcript-type dependent variation in estimates of residual disease which could be corrected by (i) taking the qPCR amplification efficiency into account, (ii) using alternative RT-qPCR approaches or (iii) droplet digital PCR (ddPCR), a technique which is relatively insensitive to differences in amplification kinetics. In CML patients, higher levels of BCR::ABL1/GUSB were identified at diagnosis for patients expressing e13a2 (n = 67) compared to e14a2 (n = 78) when analysed by RT-qPCR (P = 0.0005) but not ddPCR (P = 0.5). These data indicate that widely used RT-qPCR assays result in subtly different estimates of disease depending on BCR::ABL1 transcript type; these differences are small but may need to be considered for optimal patient management.

How to predict relapse in leukemia using time series data: A comparative in silico study.

Risk stratification and treatment decisions for leukemia patients are regularly based on clinical markers determined at diagnosis, while measurements on system dynamics are often neglected. However, there is increasing evidence that linking quantitative time-course information to disease outcomes can improve the predictions for patient-specific treatment responses. We designed a synthetic experiment simulating response kinetics of 5,000 patients to compare different computational methods with respect to their ability to accurately predict relapse for chronic and acute myeloid leukemia treatment. Technically, we used clinical reference data to first fit a model and then generate de novo model simulations of individual patients' time courses for which we can systematically tune data quality (i.e. measurement error) and quantity (i.e. number of measurements). Based hereon, we compared the prediction accuracy of three different computational methods, namely mechanistic models, generalized linear models, and deep neural networks that have been fitted to the reference data. Reaching prediction accuracies between 60 and close to 100%, our results indicate that data quality has a higher impact on prediction accuracy than the specific choice of the particular method. We further show that adapted treatment and measurement schemes can considerably improve the prediction accuracy by 10 to 20%. Our proof-of-principle study highlights how computational methods and optimized data acquisition strategies can improve risk assessment and treatment of leukemia patients.

Analysis of chronic myeloid leukaemia during deep molecular response by genomic PCR: a traffic light stratification model with impact on treatment-free remission.

This work investigated patient-specific genomic BCR-ABL1 fusions as markers of measurable residual disease (MRD) in chronic myeloid leukaemia, with a focus on relevance to treatment-free remission (TFR) after achievement of deep molecular response (DMR) on tyrosine kinase inhibitor (TKI) therapy. DNA and mRNA BCR-ABL1 measurements by qPCR were compared in 2189 samples (129 patients) and by digital PCR in 1279 sample (62 patients). A high correlation was found at levels of disease above MR4, but there was a poor correlation for samples during DMR. A combination of DNA and RNA MRD measurements resulted in a better prediction of molecular relapse-free survival (MRFS) after TKI stop (n = 17) or scheduled interruption (n = 25). At 18 months after treatment cessation, patients with stopped or interrupted TKI therapy who were DNA negative/RNA negative during DMR maintenance (green group) had an MRFS of 80% and 100%, respectively, compared with those who were DNA positive/RNA negative (MRFS = 57% and 67%, respectively; yellow group) or DNA positive/RNA positive (MRFS = 20% for both cohorts; red group). Thus, we propose a "traffic light" stratification as a TFR predictor based on DNA and mRNA BCR-ABL1 measurements during DMR maintenance before TKI cessation.

Invitation to cervical cancer screening does increase participation in Germany: Results from the MARZY study.

The effect of different invitation models on participation in cervical cancer screening (CCS) was investigated in a randomized population-based cohort study in Germany. Participants were randomly selected via population registries and randomized into intervention Arm A (invitation letter) and Arm B (invitation letter and information brochure) or control Arm C (no invitation). The intervention and control arms were compared with regard to 3-year participation and the two invitation models were compared between intervention arms. Of the 7,758 eligible women aged 30-65 years, living in the city of Mainz and in the rural region of Mainz-Bingen, 5,265 were included in the analysis. Differences in proportions of women attending CCS were investigated and logistic regression was performed to analyze various factors influencing participation. In the intervention group, 91.8% participated in CCS compared to 85.3% in the control group (p < 0.0001), with a 6.6 percentage point increase in participation [95% confidence interval (CI) 4.6-8.6] and an adjusted odds ratio (OR) of 2.69 (95% CI 2.15-3.37). Effect estimators increased to 21.9 percentage points (95% CI 16.7-27.1) and an OR of 3.64 (95% CI 2.74-4.82), respectively, when women who participated in screening annually were excluded from the analysis. The invitation letter was particularly effective among women with lower school education, migrant women and older women. No difference in participation was found between the intervention Arm A and Arm B. An accompanying information brochure did not motivate more women to undergo CCS. However, a written invitation statistically significantly increased participation in CCS in Germany.

Evaluation of polymorphisms in angiogenesis-related genes as predictive and prognostic markers for sunitinib-treated metastatic renal cell carcinoma patients.

PURPOSE: Single nucleotide polymorphisms (SNPs) in angiogenesis-associated genes might play an important role in activity of the tyrosine kinase inhibitor sunitinib and could affect survival of cancer patients treated with this drug. The aim of this retrospective study was to elucidate the role of 10 known SNPs in VEGFA, VEGFR1, VEGFR2 and VEGFR3 as potential prognostic and predictive markers in an independent cohort of patients with metastatic renal cell carcinoma (mRCC). METHODS: DNA from 121 mRCC patients treated with sunitinib was used to analyze SNPs by TaqMan genotyping assays. Disease control rate was evaluated according to RECIST. Adverse effects of sunitinib were registered from medical records. The results of Cox and logistic regression were verified by correction for multiple testing. RESULTS: Kaplan-Meier analysis revealed a reduced progression-free survival in patients with the wild-type (WT) allele of the VEGFA SNP rs699947 compared to variant alleles. Patients with the AA/AC-alleles of the VEGFR1 SNP rs9582036 had an improved median overall survival compared to those with the CC-WT allele what could be confirmed by multivariable Cox proportional hazard regression analyses. No statistically significant associations between the analyzed SNPs and higher risk for adverse effects were observed. CONCLUSIONS: The results of this study suggest that most of the selected SNPs in angiogenesis-related genes are not associated with survival of mRCC patients after sunitinib therapy or with adverse effects. Only the VEGFR1 SNP rs9582036 showed a statistically significant association with overall survival. The potential of SNPs as prognostic and predictive markers for sunitinib-treated mRCC patients should be finally assessed by prospective studies.

Recessão, pobreza e família: a década pior do que perdida / Economic recession, poverty and family: a lost decade

Estuda as formas como os vários tipos de família da região metropolitana de São Paulo foram afetados pela recessão de 1981-83, pela relativa recuperação econômica de 1983-85, pelo boom de 1986 ocasionado pelo Plano Cruzado e pela deteriorização posterior