Survival in multiple myeloma (MM) has developed favorably over the past decades for reasons that have been ascribed to new medications and treatment. However, development of survival over a long period and comparison to other hematopoietic neoplasms (HN) is less well known. Here we used Swedish cancer data from the Nordcan database, spanning a 50-year period from 1967 to 2016, and analyzed 1- and 5-year survival data. As a novel type of analysis we calculate the difference in survival between year 1 and 5 which indicates how well survival was maintained in the 4-year period following year 1 after diagnosis. The relative 1- and 5- year survival increased constantly; the 5-year survival graph for women was almost linear. The difference between 1- and 5-year survival revealed that the 5-year survival gain was entirely due to the improvement in 1-year survival, except for the last period. Survival improvement in all HNs exceeded that in MM. The linear 5-year survival increase for female MM patients suggests a contribution by many small improvements in the first year care rather than single major events. The future challenges are to push the gains past year 1 and to extend them to old patients.
- MeSH
- Databases, Factual statistics & numerical data MeSH
- Hematologic Neoplasms diagnosis mortality therapy MeSH
- Incidence MeSH
- Middle Aged MeSH
- Humans MeSH
- Survival Rate MeSH
- Multiple Myeloma diagnosis mortality therapy MeSH
- Mortality trends MeSH
- Registries statistics & numerical data MeSH
- Aged MeSH
- Check Tag
- Middle Aged MeSH
- Humans MeSH
- Male MeSH
- Aged MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Comparative Study MeSH
- Geographicals
- Sweden MeSH
Clinical procedure for mild cognitive impairment (MCI) is mainly based on clinical records and short cognitive tests. However, low suspicion and difficulties in understanding test cut-offs make diagnostic accuracy being low, particularly in primary care. Artificial neural networks (ANNs) are suitable to design computed aided diagnostic systems because of their features of generating relationships between variables and their learning capability. The main aim pursued in that work is to explore the ability of a hybrid ANN-based system in order to provide a tool to assist in the clinical decision-making that facilitates a reliable MCI estimate. The model is designed to work with variables usually available in primary care, including Minimental Status Examination (MMSE), Functional Assessment Questionnaire (FAQ), Geriatric Depression Scale (GDS), age, and years of education. It will be useful in any clinical setting. Other important goal of our study is to compare the diagnostic rendering of ANN-based system and clinical physicians. A sample of 128 MCI subjects and 203 controls was selected from the Alzheimer's Disease Neuroimaging Initiative (ADNI). The ANN-based system found the optimal variable combination, being AUC, sensitivity, specificity, and clinical utility index (CUI) calculated. The ANN results were compared with those from medical experts which include two family physicians, a neurologist, and a geriatrician. The optimal ANN model reached an AUC of 95.2%, with a sensitivity of 90.0% and a specificity of 84.78% and was based on MMSE, FAQ, and age inputs. As a whole, physician performance achieved a sensitivity of 46.66% and a specificity of 91.3%. CUIs were also better for the ANN model. The proposed ANN system reaches excellent diagnostic accuracy although it is based only on common clinical tests. These results suggest that the system is especially suitable for primary care implementation, aiding physicians work with cognitive impairment suspicions.
- MeSH
- Databases, Factual statistics & numerical data MeSH
- Diagnosis, Computer-Assisted methods statistics & numerical data MeSH
- Cognitive Dysfunction diagnosis psychology MeSH
- Humans MeSH
- Neural Networks, Computer * MeSH
- Neuropsychological Tests * statistics & numerical data MeSH
- Area Under Curve MeSH
- Aged, 80 and over MeSH
- Aged MeSH
- Sensitivity and Specificity MeSH
- Case-Control Studies MeSH
- Decision Support Systems, Clinical * statistics & numerical data MeSH
- Computational Biology MeSH
- Check Tag
- Humans MeSH
- Aged, 80 and over MeSH
- Aged MeSH
- Publication type
- Journal Article MeSH
BACKGROUND: A minority of European countries have participated in international comparisons with high level data on lung cancer. However, the nature and extent of data collection across the continent is simply unknown, and without accurate data collection it is not possible to compare practice and set benchmarks to which lung cancer services can aspire. METHODS: Using an established network of lung cancer specialists in 37 European countries, a survey was distributed in December 2014. The results relate to current practice in each country at the time, early 2015. The results were compiled and then verified with co-authors over the following months. RESULTS: Thirty-five completed surveys were received which describe a range of current practice for lung cancer data collection. Thirty countries have data collection at the national level, but this is not so in Albania, Bosnia-Herzegovina, Italy, Spain and Switzerland. Data collection varied from paper records with no survival analysis, to well-established electronic databases with links to census data and survival analyses. CONCLUSION: Using a network of committed clinicians, we have gathered validated comparative data reporting an observed difference in data collection mechanisms across Europe. We have identified the need to develop a well-designed dataset, whilst acknowledging what is feasible within each country, and aspiring to collect high quality data for clinical research.
- MeSH
- Databases, Factual statistics & numerical data MeSH
- Medical Oncology methods statistics & numerical data MeSH
- Humans MeSH
- Lung Neoplasms diagnosis therapy MeSH
- Data Collection methods statistics & numerical data MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Geographicals
- Europe MeSH
In this article, we provide an overview of the reasons for the introduction of less invasive treatment modalities in the management of intractable mesial temporal lobe epilepsy (mTLE). We summarize our published research on stereotactic amygdalohippocampectomy (SAHE) and recalculate our data for the patients' last visit. In our previous work, we found that patients achieved long-term seizure-free outcomes in 70.5%. Re-analysis of results in a subgroup of patient who were diagnosed and followed-up at Epilepsy Center, Na Homolce Hospital, Prague, indicate that these outcomes are durable. Re-treatment in treatment failures was successful in all cases. The discussion compares novel treatment options and defines the place of SAHE among them.
- MeSH
- Amygdala surgery MeSH
- Databases, Factual statistics & numerical data MeSH
- Electroencephalography MeSH
- Epilepsy, Temporal Lobe diagnostic imaging surgery MeSH
- Hippocampus surgery MeSH
- Humans MeSH
- Magnetic Resonance Imaging MeSH
- Image Processing, Computer-Assisted MeSH
- Radiofrequency Ablation methods MeSH
- Treatment Outcome * MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Review MeSH
Narcolepsy is a rare life-long disease that exists in two forms, narcolepsy type-1 (NT1) or type-2 (NT2), but only NT1 is accepted as clearly defined entity. Both types of narcolepsies belong to the group of central hypersomnias (CH), a spectrum of poorly defined diseases with excessive daytime sleepiness as a core feature. Due to the considerable overlap of symptoms and the rarity of the diseases, it is difficult to identify distinct phenotypes of CH. Machine learning (ML) can help to identify phenotypes as it learns to recognize clinical features invisible for humans. Here we apply ML to data from the huge European Narcolepsy Network (EU-NN) that contains hundreds of mixed features of narcolepsy making it difficult to analyze with classical statistics. Stochastic gradient boosting, a supervised learning model with built-in feature selection, results in high performances in testing set. While cataplexy features are recognized as the most influential predictors, machine find additional features, e.g. mean rapid-eye-movement sleep latency of multiple sleep latency test contributes to classify NT1 and NT2 as confirmed by classical statistical analysis. Our results suggest ML can identify features of CH on machine scale from complex databases, thus providing 'ideas' and promising candidates for future diagnostic classifications.
- MeSH
- Models, Biological * MeSH
- Databases, Factual statistics & numerical data MeSH
- Datasets as Topic MeSH
- Adult MeSH
- Data Interpretation, Statistical MeSH
- Humans MeSH
- Young Adult MeSH
- Narcolepsy classification diagnosis physiopathology MeSH
- Polysomnography statistics & numerical data MeSH
- Supervised Machine Learning * MeSH
- ROC Curve MeSH
- Sleep, REM physiology MeSH
- Sleep Latency physiology MeSH
- Stochastic Processes MeSH
- Rare Diseases classification diagnosis physiopathology MeSH
- Check Tag
- Adult MeSH
- Humans MeSH
- Young Adult MeSH
- Male MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
Background: A percent brain volume change (PBVC) cut-off of -0.4% per year has been proposed to distinguish between pathological and physiological changes in multiple sclerosis (MS). Unfortunately, standardized PBVC measurement is not always feasible on scans acquired outside research studies or academic centers. Percent lateral ventricular volume change (PLVVC) is a strong surrogate measure of PBVC, and may be more feasible for atrophy assessment on real-world scans. However, the PLVVC rate corresponding to the established PBVC cut-off of -0.4% is unknown. Objective: To establish a pathological PLVVC expansion rate cut-off analogous to -0.4% PBVC. Methods: We used three complementary approaches. First, the original follow-up-length-weighted receiver operating characteristic (ROC) analysis method establishing whole brain atrophy rates was adapted to a longitudinal ventricular atrophy dataset of 177 relapsing-remitting MS (RRMS) patients and 48 healthy controls. Second, in the same dataset, SIENA PBVCs were used with non-linear regression to directly predict the PLVVC value corresponding to -0.4% PBVC. Third, in an unstandardized, real world dataset of 590 RRMS patients from 33 centers, the cut-off maximizing correspondence to PBVC was found. Finally, correspondences to clinical outcomes were evaluated in both datasets. Results: ROC analysis suggested a cut-off of 3.09% (AUC = 0.83, p < 0.001). Non-linear regression R2 was 0.71 (p < 0.001) and a - 0.4% PBVC corresponded to a PLVVC of 3.51%. A peak in accuracy in the real-world dataset was found at a 3.51% PLVVC cut-off. Accuracy of a 3.5% cut-off in predicting clinical progression was 0.62 (compared to 0.68 for PBVC). Conclusions: Ventricular expansion of between 3.09% and 3.51% on T2-FLAIR corresponds to the pathological whole brain atrophy rate of 0.4% for RRMS. A conservative cut-off of 3.5% performs comparably to PBVC for clinical outcomes.
- MeSH
- Databases, Factual statistics & numerical data MeSH
- Adult MeSH
- Middle Aged MeSH
- Humans MeSH
- Magnetic Resonance Imaging MeSH
- Nonlinear Dynamics MeSH
- Image Processing, Computer-Assisted MeSH
- Disability Evaluation MeSH
- Disease Progression MeSH
- Reference Values MeSH
- Multiple Sclerosis, Relapsing-Remitting diagnostic imaging pathology MeSH
- ROC Curve MeSH
- Severity of Illness Index MeSH
- Lateral Ventricles diagnostic imaging pathology MeSH
- Check Tag
- Adult MeSH
- Middle Aged MeSH
- Humans MeSH
- Male MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
PURPOSE: The purpose of this study was to analyze pressure injury (PI) occurrence upon admission and at any time during the hospital course inpatients care facilities in the Czech Republic. Secondary aims were to evaluate demographic and clinical data of patients with PI and the impact of a PI on length of stay (LOS) in the hospital. DESIGN: Retrospective, cross-sectional analysis. SETTING AND SUBJECTS: The sample comprised data of hospitalized patients entered into the National Register of Hospitalized Patients (NRHOSP) database of the Czech Republic between 2007 and 2014 with a diagnosis L89 (pressure ulcer of unspecified site based on the International Classification of Diseases, Tenth Revision, ICD-10). Electronic records of 17,762,854 hospitalizations were reviewed. METHOD: Data from the NRHOSP from all acute and non-acute care hospitals in the Czech Republic were analyzed. Specifically, we analyzed patients admitted to acute and non-acute care facilities with a primary or secondary diagnosis of PI. RESULTS: The NRHOSP database included 17,762,854 cases, of which 46,224 cases (33,342 cases in acute care hospitals; 12,882 in non-acute care hospitals) had the L89 diagnosis (0.3%). The mean age of patients admitted with a PI was 73.8 ± 15.3 years (mean ± SD), and their average LOS was 33.2 ± 76.9 days. The mean LOS of patients hospitalized with L89 code as a primary diagnosis (n = 6877) was significantly longer compared to those patients for whom L89 code was a secondary diagnosis (25.8 vs 20.2 days, P < .001) in acute care facilities. In contrast, we found no difference in the mean LOS for patients hospitalized in non-acute care facility (58.7 days vs 65.1 days; P = .146) with ICD code L89. CONCLUSION: Pressure injuries were associated with significant LOS in both acute and non-acute care settings in the Czech Republic. Despite the valuable insights we obtained from the analysis of NRHOSP data, we advocate creation of a more valid and reliable electronic reporting system that enables policy makers to evaluate the quality and safety concerning PI and its impact on patients and the healthcare system.
- MeSH
- Databases, Factual standards statistics & numerical data MeSH
- Pressure Ulcer classification epidemiology nursing MeSH
- Inpatients statistics & numerical data MeSH
- Middle Aged MeSH
- Humans MeSH
- Hospitals standards statistics & numerical data MeSH
- Statistics, Nonparametric MeSH
- Cross-Sectional Studies MeSH
- Retrospective Studies MeSH
- Aged, 80 and over MeSH
- Aged MeSH
- Check Tag
- Middle Aged MeSH
- Humans MeSH
- Male MeSH
- Aged, 80 and over MeSH
- Aged MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Geographicals
- Czech Republic MeSH
Autoři předkládají výsledky analýzy dat o kvalitě péče o pacienty s onemocněním diabetes mellitus v ČR. Data byla získána z databází Všeobecné zdravotní pojišťovny za léta 2003-2013. Zaměřili jsme se zejména na celkovou mortalitu, preskripci metforminu a sulfonylurey, incidenci amputací u pacientů s onemocněním diabetes mellitus a monitorování kompenzace diabetu u pacientů léčených inzulinovou pumpou. Mortalita pacientů s diabetes mellitus léčených perorálními antidiabetiky v České republice během let 2003 – 2013 poklesla a přiblížila se populačnímu průměru.
The authors present results of the analysis of the data on the quality of the care for patients with diabetes mellitus in the Czech Republic. The data was obtained from the General Health Insurance Company of the Czech Republic (VZP) databases for the period of 2003-2013. We focused in particular on overall mortality, prescriptions for metformin and sulfonylurea, incidence of amputations in patients with diabetes mellitus and monitoring of diabetes compensation in patients treated with the insulin pump. The mortality of patients with diabetes mellitus treated with oral antidiabetic drugs in the Czech Republic decreased over the decade of 2003 – 2013 and came close to the population average.
- MeSH
- Amputation, Surgical statistics & numerical data MeSH
- Databases, Factual statistics & numerical data MeSH
- Diabetes Mellitus * drug therapy mortality therapy MeSH
- Diabetic Foot surgery MeSH
- Adult MeSH
- Hypoglycemic Agents therapeutic use MeSH
- Insulin Infusion Systems statistics & numerical data MeSH
- Diabetes Complications surgery MeSH
- Quality of Health Care * statistics & numerical data trends MeSH
- Drug Prescriptions statistics & numerical data MeSH
- Middle Aged MeSH
- Humans MeSH
- Metformin therapeutic use MeSH
- Pilot Projects MeSH
- Blood Glucose Self-Monitoring statistics & numerical data MeSH
- Aged, 80 and over MeSH
- Aged MeSH
- Sulfonylurea Compounds therapeutic use MeSH
- Age Distribution MeSH
- Check Tag
- Adult MeSH
- Middle Aged MeSH
- Humans MeSH
- Male MeSH
- Aged, 80 and over MeSH
- Aged MeSH
- Female MeSH
- Geographicals
- Czech Republic MeSH
Although transcranial sonography is not yet an established diagnostic modality for dementia screening or differential diagnosis of Alzheimer's disease (AD) from vascular dementia (VaD), intracranial hemodynamic assessment may provide crucial information about the association between cognitive deterioration and vascular risk factors. We conducted a systematic narrative review of available literature through MEDLINE and EMBASE search to identify all available data about the evaluation of VaD patients with transcranial Doppler, and to discuss further the vascular disorders of the cerebral circulation in patients with vascular cognitive impairment. According to the available literature data to date, VaD patients were found to have lower mean flow velocity values in four studies (indicating cerebral hypoperfusion), higher pulsatility indices in three studies (indicating increased downstream vascular resistance), and more severe impairment of cerebrovascular reactivity in five studies (indicating exhausted vasodilatory reserve) compared to AD patients and controls. Microembolic signals were also found to be significantly more common in patients with VaD or AD compared to their age- and gender-matched controls, suggesting that asymptomatic microembolism, apart for being only marker of VaD, could presumably be involved in the genesis of dementia, and in the overlap between VaD and AD. Further studies with larger and carefully selected groups are required to eliminate potential confounders and to set specific cut-off values for the aforementioned hemodynamic parameters in demented patients and dementia subtypes.
- MeSH
- Databases, Factual statistics & numerical data MeSH
- Cognition Disorders diagnosis etiology MeSH
- Humans MeSH
- Brain pathology MeSH
- Cerebrovascular Circulation MeSH
- Risk Factors MeSH
- Ultrasonography, Doppler, Transcranial * MeSH
- Dementia, Vascular ultrasonography MeSH
- Check Tag
- Humans MeSH
- Male MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Review MeSH
Over the past few years, new biomarkers have allowed a deeper insight into gliomagenesis and facilitated the identification of possible targets for glioma therapy. Isocitrate dehydrogenase (IDH) 1 and IDH2 mutations have been shown to be promising biomarkers for monitoring disease prognosis and predicting the response to treatment. This review summarizes recent findings in this field. Web of Science, Science Direct, and PubMed online databases were used to search for publications investigating the role of IDH in glioma. References were identified by searching for the keywords "IDH1 or IDH2 and glioma and diagnostic or predictive or prognostic" in papers published from January, 2008, to April, 2014. Only papers in English were reviewed. Publications available only as an abstract were not included. IDH1/2 mutations are tightly associated with grade II and III gliomas and secondary glioblastomas, with better prognosis and production of a recently described oncometabolite, 2-hydroxyglutarate (2HG). Although the contradictory positive effect of IDH mutation on prognosis and negative role of 2HG in tumor transformation remain unresolved, the future direction of personalized treatment strategies targeted to glioma development is likely to focus on IDH1/2 mutations.
