Risk assessment and mortality prediction in patients with venous thromboembolism using big data and machine learning.

Venous thromboembolism (VTE) is the third most common cardiovascular condition that affects mainly hospitalized and cancer patients and it is associated with high morbidity and mortality. Some patients need immediate treatment and monitoring in intensive care units (ICU). Moreover, cancer patients are at increased risk of developing VTE, especially in the immediate period after ICU hospitalization. It is crucial to predict which of the cancer patients will develop VTE, as well as early and late mortality in these high-risk patients and recognize possible treatable factors in order to improve survival. Several scoring and predictive models have been developed for these purposes, but with limited generalizability and they are mostly effective in the prediction of in-hospital mortality. They have several limitations, for example they use data recorded only on the first day of admission. Moreover, no score exists so far to predict late mortality in ICU patients. With the advanced use of electronic health records, open-source big- data medical databases and machine learning, predictive modelling could be utilized and become a powerful tool to guide clinical decision. The aim of the study was to explore the use and performance of various machine learning algorithms (ML) in order to predict two research questions: (i) VTE risk in ICU hospitalized cancer patients after discharge and, (ii) early and late mortality in VTE patients hospitalized in ICU. For that reason, a freely accessible database MIMIC-III has been used that contains a vast amount of various time-series healthcare data from thousands of patients, making it ideal for ML based forecasting. Since it provides information even after discharge from ICU, it gives an opportunity to predict late mortality. Two groups of datasets were extracted from the database: D1, consisted of 4,699 patients with cancer who were admitted to ICU and stratified in two groups based on whether they were readmitted to ICU within 90 days with a diagnosis of VTE or not. The ML classification task was to predict which of the cancer patients originally admitted to ICU will be readmitted with VTE within 90 days. D2, consisted of 2,468 patients who were admitted to ICU with a VTE diagnosis and stratified in three groups, based on their outcome, that is, died during their first ICU admission (early mortality group), died after their discharge from ICU or in a later admission (late mortality group) and remained alive for months after their admission in ICU. In this case, two ML classification tasks were constructed, first to build a model that distinguishes early mortality and second, a model that distinguishes late mortality. A very wide range of features were selected, that includes demographic information, clinical and laboratory data, prescriptions, procedures, well established comorbidity and severity scores as well as information coming from written notes. Clinically relevant entities from free medical notes were extracted using the sequence annotator SABER and then they were fitted into a Latent Dirichlet Allocation (LDA) model of 50 topics. In total, 1,471 features were extracted for each patient, grouped in 8 categories, each representing a different type of medical assessment. Automated ML platform that easily handles with-high dimensional, noisy and missing data, as well as Monte Carlo simulations based on Random Forests with hyperparameter tuning and class- balancing with Synthetic Minority Oversampling Technique (SMOTE) were trained in parallel. Due to the highly imbalanced nature of the first dataset (“cancer patients with thrombosis”), neither of the ML approaches were able to predict DVT in cancer patients even after the use of SMOTE method. As far as it concerns the prediction of early mortality in ICU patients with VTE, the best ML model chosen to predict early mortality was Random Forests (AUC=0,92). Regarding late mortality, the best ML model was again Random Forests. Nevertheless, the task of predicting late mortality was less efficient even with the holistic approach (AUC=0,82). Significant clinically relevant predictive features of early and late mortality were cancer, age, treatment with warfarin, and red cell transfusions, whereas known severity scores performed well only in the prediction of early mortality. The contribution of this study to the current knowledge was multi-leveled, as it explored the performance of various ML approaches in a big-data driven research approach, using multiple formats of data from structured to unstructured medical notes, it examined the effect of balancing techniques in highly imbalanced datasets, such as the case of medical datasets, and finally discovered possibly new biomarkers. Early mortality in critically-ill patients with VTE can be easily predicted by ML techniques, whereas in the case of late mortality, which is a more difficult task, and where medical scores are still lacking, ML could probably outperform classic statistical methods. There is a need for more precise and reliable tools in order to overcome the nature of highly imbalanced medical datasets, such as the case of “cancer patients with thrombosis” dataset. This study showed that automated ML approaches have similar performance with manual selection and parametrization of ML models, which is highly promising in the setting of healthcare “big-data” medical databases

Geriatric Patient Safety Indicators Based on Linked Administrative Health Data to Assess Anticoagulant-Related Thromboembolic and Hemorrhagic Adverse Events in Older Inpatients: A Study Proposal.

Frail older people with multiple interacting conditions, polypharmacy, and complex care needs are particularly exposed to health care-related adverse events. Among these, anticoagulant-related thromboembolic and hemorrhagic events are particularly frequent and serious in older inpatients. The growing use of anticoagulants in this population and their substantial risk of toxicity and inefficacy have therefore become an important patient safety and public health concern worldwide. Anticoagulant-related adverse events and the quality of anticoagulation management should thus be routinely assessed to improve patient safety in vulnerable older inpatients. This project aims to develop and validate a set of outcome and process indicators based on linked administrative health data (ie, insurance claims data linked to hospital discharge data) assessing older inpatient safety related to anticoagulation in both Switzerland and France, and enabling comparisons across time and among hospitals, health territories, and countries. Geriatric patient safety indicators (GPSIs) will assess anticoagulant-related adverse events. Geriatric quality indicators (GQIs) will evaluate the management of anticoagulants for the prevention and treatment of arterial or venous thromboembolism in older inpatients. GPSIs will measure cumulative incidences of thromboembolic and bleeding adverse events based on hospital discharge data linked to insurance claims data. Using linked administrative health data will improve GPSI risk adjustment on patients' conditions that are present at admission and will capture in-hospital and postdischarge adverse events. GQIs will estimate the proportion of index hospital stays resulting in recommended anticoagulation at discharge and up to various time frames based on the same electronic health data. The GPSI and GQI development and validation process will comprise 6 stages: (1) selection and specification of candidate indicators, (2) definition of administrative data-based algorithms, (3) empirical measurement of indicators using linked administrative health data, (4) validation of indicators, (5) analyses of geographic and temporal variations for reliable and valid indicators, and (6) data visualization. Study populations will consist of 166,670 Swiss and 5,902,037 French residents aged 65 years and older admitted to an acute care hospital at least once during the 2012-2014 period and insured for at least 1 year before admission and 1 year after discharge. We will extract Swiss data from the Helsana Group data warehouse and French data from the national health insurance information system (SNIIR-AM). The study has been approved by Swiss and French ethics committees and regulatory organizations for data protection. Validated GPSIs and GQIs should help support and drive quality and safety improvement in older inpatients, inform health care stakeholders, and enable international comparisons. We discuss several limitations relating to the representativeness of study populations, accuracy of administrative health data, methods used for GPSI criterion validity assessment, and potential confounding bias in comparisons based on GQIs, and we address these limitations to strengthen study feasibility and validity

Current Perspectives on Viral Disease Outbreaks

The COVID-19 pandemic has reminded the world that infectious diseases are still important. The last 40 years have experienced the emergence of new or resurging viral diseases such as AIDS, ebola, MERS, SARS, Zika, and others. These diseases display diverse epidemiologies ranging from sexual transmission to vector-borne transmission (or both, in the case of Zika). This book provides an overview of recent developments in the detection, monitoring, treatment, and control of several viral diseases that have caused recent epidemics or pandemics

Die Rolle der Hyperkoagulabilität bei neurovaskulären Erkrankungen: Einblicke aus der klinischen Epidemiologie

Given their crucial role in preserving the hemostatic balance, coagulation factors are of great interest in the context of both venous and arterial thrombosis. Recent laboratory work has indicated that a hypercoagulable state increases risk for ischemic events. Furthermore, first clinical trials have found that temporarily lowering Factor XI levels prevents clots without increasing bleeds in knee arthroplasty patients, which may make these factors attractive therapeutic targets in secondary prevention of ischemic stroke. My dissertation subprojects aimed to study the role of coagulation factors in neurovascular disease using existing observational datasets. As such, these clinical epidemiologic investigations provided important first insights in a cost-effective way. My work describes diverse aspects of the processes by which coagulation factors are implicated in neurovascular diseases, with each subproject characterized by a different study design and population. We identified genetic determinants of contact factor levels (High Molecular Weight Kininogen (HMWK), Prekallikrein (PK), FXI and FXII) and probed their associations with vascular disease phenotypes in a case-control study. In addition to replicating known associations between single genetic variants and contact system factor levels, we identified two novel loci; one for PK antigen levels (KLKB1 rs4253243; βconditional=-12.38; 95% confidence interval (CI), -20.07 to -4.69) and one for HMWK antigen levels (KNG1 rs5029980; βconditional=5.86; 95%CI: 2.40 to 9.32). We estimated the effects of hypercoagulability on post-stroke outcomes in a cohort of 576 ischemic stroke patients. After controlling for confounding, compared with having low or normal levels, having high (>75th-percentile) FXI activity levels increased the hazard for the combined endpoint (recurrent stroke, myocardial infarction, or all-cause mortality) within three years of first ischemic stroke (Hazard Ratio (HR)=1.80, 95%CI: 1.09–2.98). High FVIII activity was also linked to worse outcomes (HR=2.05, 95%CI: 1.28–3.29), whereas high FXII activity was not (HR=0.86, 95%CI: 0.49–1.51). Finally, in a general population cohort of older persons, we found no evidence of a relevant contribution of factor VIII activity to the presence or worsening of white matter hyperintensities or cognitive performance over time. Each of these subprojects provided a substantial contribution to filling knowledge gaps in the field of hypercoagulability research. Taken together, this dissertation work contributes to a better understanding of genetic influences on coagulation factor levels as well as the longer-term effects of expressed hypercoagulability on outcomes among stroke patients and in a general population sample of older persons. My work concludes with a set of suggestions for future coagulation factor research, especially in the context of stroke, based on lessons learned during the process of synthesizing and critically evaluating my results.Aufgrund ihrer entscheidenden Rolle bei der Aufrechterhaltung der hämostatischen Balance, sind Gerinnungsfaktoren im Zusammenhang mit venösen sowie arteriellen Thrombosen von großem Interesse. Neuere Laborergebnisse zeigten ein erhöhtes Risiko für ischämische Ereignisse durch einen hyperkoagulablen Zustand. Erste klinische Studien zeigten eine vielversprechende Wirkung gegen Thrombosen durch die temporäre Senkung des Gerinnungsfaktor-XI-Spiegels – ohne Erhöhung des Blutungsrisikos bei Knieendoprothese-Patient*innen. Diese Erkenntnis ist auch im Kontext der Sekundärprävention von Schlaganfällen von hoher Relevanz. Das Ziel meiner Dissertation war, in drei Teilprojekten die Rolle bestimmter Gerinnungsfaktoren zu untersuchen, die zur Hyperkoagulabilität bei neurovaskulären Erkrankungen beitragen. Die auf klinisch-epidemiologischen Fragestellungen basierenden Sekundäranalysen von Beobachtungsdatensätzen liefern wichtige Erkenntnisse für die Zielpopulationen in kosten-effektiver Weise. Im Rahmen meiner Dissertation wurden genetische Determinanten von Gerinnungsfaktoren des Contact-Activation-Systems (hochmolekulares Kininogen (HMWK), Präkallikrein (PK), Faktor XI und Faktor XII) identifiziert. Außerdem untersuchten wir ihre Assoziationen mit Phänotypen von vaskulären Erkrankungen in einer Fall-Kontroll-Studie. Zusätzlich zur Replikation bekannter Assoziationen zwischen einzelnen genetischen Varianten und den Spiegeln der Gerinnungsfaktoren identifizierten wir zwei neue Genloci: einen für den PK-Antigenspiegel (KLKB1 rs4253243; βadjustiert=-12,38; 95% Konfidenzintervall (KI): -20,07 bis -4,69) und einen für den HMWK-Antigenspiegel (KNG1 rs5029980; βadjustiert=5,86; 95%KI, 2,40 bis 9,32). Des Weiteren schätzten wir die Effekte von hohen Faktor-XI-, Faktor-XII- und Faktor-VIII-Aktivitätsspiegeln auf Langzeit-Outcomes in einer Kohorte von 576 Patient*innen mit ischämischem Schlaganfall. Im Vergleich zu niedrigen oder normalen Werten hatten Schlaganfallpatient*innen mit einer hohen FXI-Aktivität (>75. Perzentil) ein höheres Risiko für den kombinierten Endpunkt (rezidivierender Schlaganfall, Myokardinfarkt oder Gesamtmortalität) innerhalb von drei Jahren (HR=1,80; 95%KI: 1,09 bis 2,98) nach Confounding-Adjustierung. Eine hohe FVIII-Aktivität war ebenfalls mit einem schlechteren Outcome verbunden (HR=2,05; 95%KI: 1,28 bis 3,29), eine hohe FXII-Aktivität hingegen nicht (HR=0,86; 95%KI: 0,49 bis 1,51). Schließlich fanden wir in einer Allgemeinbevölkerungskohorte älterer Personen keine Hinweise auf einen relevanten Beitrag des Faktor-VIII-Aktivitätsspiegels zur Präsenz von White Matter Hyperintensities oder verminderten kognitiven Funktionen und deren Verschlechterung im Zeitverlauf. Meine Arbeit schließt mit praktischen und methodischen Vorschlägen für zukünftige Forschung zu Gerinnungsfaktoren, insbesondere im Kontext des ischämischen Schlaganfalls, basierend auf den Erkenntnissen aus der Synthese und kritischen Bewertung meiner Ergebnisse

The European Hematology Association Roadmap for European Hematology Research: a consensus document

The European Hematology Association (EHA) Roadmap for European Hematology Research highlights major achievements in diagnosis and treatment of blood disorders and identifies the greatest unmet clinical and scientific needs in those areas to enable better funded, more focused European hematology research. Initiated by the EHA, around 300 experts contributed to the consensus document, which will help European policy makers, research funders, research organizations, researchers, and patient groups make better informed decisions on hematology research. It also aims to raise public awareness of the burden of blood disorders on European society, which purely in economic terms is estimated at €23 billion per year, a level of cost that is not matched in current European hematology research funding. In recent decades, hematology research has improved our fundamental understanding of the biology of blood disorders, and has improved diagnostics and treatments, sometimes in revolutionary ways. This progress highlights the potential of focused basic research programs such as this EHA Roadmap. The EHA Roadmap identifies nine ‘sections’ in hematology: normal hematopoiesis, malignant lymphoid and myeloid diseases, anemias and related diseases, platelet disorders, blood coagulation and hemostatic disorders, transfusion medicine, infections in hematology, and hematopoietic stem cell transplantation. These sections span 60 smaller groups of diseases or disorders. The EHA Roadmap identifies priorities and needs across the field of hematology, including those to develop targeted therapies based on genomic profiling and chemical biology, to eradicate minimal residual malignant disease, and to develop cellular immunotherapies, combination treatments, gene therapies, hematopoietic stem cell treatments, and treatments that are better tolerated by elderly patients

Malattia trombotica: condizioni cliniche ad aumentato rischio e terapia anticoagulante

missin

The European Hematology Association Roadmap for European Hematology Research. A Consensus Document

Abstract The European Hematology Association (EHA) Roadmap for European Hematology Research highlights major achievements in diagnosis and treatment of blood disorders and identifies the greatest unmet clinical and scientific needs in those areas to enable better funded, more focused European hematology research. Initiated by the EHA, around 300 experts contributed to the consensus document, which will help European policy makers, research funders, research organizations, researchers, and patient groups make better informed decisions on hematology research. It also aims to raise public awareness of the burden of blood disorders on European society, which purely in economic terms is estimated at Euro 23 billion per year, a level of cost that is not matched in current European hematology research funding. In recent decades, hematology research has improved our fundamental understanding of the biology of blood disorders, and has improved diagnostics and treatments, sometimes in revolutionary ways. This progress highlights the potential of focused basic research programs such as this EHA Roadmap. The EHA Roadmap identifies nine sections in hematology: normal hematopoiesis, malignant lymphoid and myeloid diseases, anemias and related diseases, platelet disorders, blood coagulation and hemostatic disorders, transfusion medicine, infections in hematology, and hematopoietic stem cell transplantation. These sections span 60 smaller groups of diseases or disorders. The EHA Roadmap identifies priorities and needs across the field of hematology, including those to develop targeted therapies based on genomic profiling and chemical biology, to eradicate minimal residual malignant disease, and to develop cellular immunotherapies, combination treatments, gene therapies, hematopoietic stem cell treatments, and treatments that are better tolerated by elderly patients. Received December 15, 2015. Accepted January 27, 2016. Copyright © 2016, Ferrata Storti Foundatio