Marco Canepa

Bio: Marco Canepa is an academic researcher from University of Genoa. The author has contributed to research in topics: Heart failure & Medicine. The author has an hindex of 26, co-authored 110 publications receiving 2060 citations. Previous affiliations of Marco Canepa include Johns Hopkins University & National Institutes of Health.

Longitudinal Trajectories of Arterial Stiffness and the Role of Blood Pressure The Baltimore Longitudinal Study of Aging

Abstract: Carotid-femoral pulse wave velocity (PWV), a marker of arterial stiffness, is an established independent cardiovascular risk factor. Little information is available on the pattern and determinants of the longitudinal change in PWV with aging. Such information is crucial to elucidating mechanisms underlying arterial stiffness and the design of interventions to retard it. Between 1988 and 2013, we collected 2 to 9 serial measures of PWV in 354 men and 423 women of the Baltimore Longitudinal Study of Aging, who were 21 to 94 years of age and free of clinically significant cardiovascular disease. Rates of PWV increase accelerated with advancing age in men more than women, leading to sex differences in PWV after the age of 50 years. In both sexes, not only systolic blood pressure (SBP) ≥140 mm Hg but also SBP of 120 to 139 mm Hg was associated with steeper rates of PWV increase compared with SBP<120 mm Hg. Furthermore, there was a dose-dependent effect of SBP in men with marked acceleration in PWV rate of increase with age at SBP ≥140 mm Hg compared with SBP of 120 to 139 mm Hg. Except for waist circumference in women, no other traditional cardiovascular risk factors predicted longitudinal PWV increase. In conclusion, the steeper longitudinal increase of PWV in men than women led to the sex difference that expanded with advancing age. Age and SBP are the main longitudinal determinants of PWV, and the effect of SBP on PWV trajectories exists even in the prehypertensive range.

Natural hexavalent chromium in groundwaters interacting with ophiolitic rocks

Abstract: Thirty of the 58 groundwaters sampled in September-October 2000 in the study area (La Spezia Province, Italy) have Mg-HCO3 to Ca-HCO3 composition, undetectable Cr(III) contents, and virtually equal concentrations of total dissolved Cr and Cr(VI). Therefore, dissolved Cr is present in toto as Cr(VI), with concentrations of 5-73 ppb. These values are above the maximum permissible level for drinking waters (5 ppb). Local ophiolites, especially serpentinites and ultramafites, are Cr-rich and represent a Cr source for groundwaters. How- ever, since Cr is present as Cr(III) in rock-forming minerals, its release to the aqueous solution requires oxidation of Cr(III) to Cr(VI). This can be per- formed by different electron acceptors, including Mn oxides, H2O2, gaseous O2, and perhaps Fe(III) oxyhydroxides. Based on this evidence and due to the absence of anthropogenic Cr sources, the com- paratively high Cr(VI) concentrations measured in the waters of the study area are attributed to natural pollution.

Cancer diagnosis in patients with heart failure : epidemiology, clinical implications and gaps in knowledge

Abstract: Cancer and heart failure (HF) are common medical conditions with a steadily rising prevalence in industrialized countries, particularly in the elderly, and they both potentially carry a poor prognosis. A new diagnosis of malignancy in subjects with pre-existing HF is not infrequent, and challenges HF specialists as well as oncologists with complex questions relating to both HF and cancer management. An increased incidence of cancer in patients with established HF has also been suggested. This review paper summarizes the epidemiology and the prognostic implications of cancer occurrence in HF, the impact of pre-existing HF on cancer treatment decisions and the impact of cancer on HF therapeutic options, while providing some practical suggestions regarding patient care and highlighting gaps in knowledge.

Performance of Prognostic Risk Scores in Chronic Heart Failure Patients Enrolled in the European Society of Cardiology Heart Failure Long-Term Registry

Abstract: Objectives This study compared the performance of major heart failure (HF) risk models in predicting mortality and examined their utilization using data from a contemporary multinational registry. Background Several prognostic risk scores have been developed for ambulatory HF patients, but their precision is still inadequate and their use limited. Methods This registry enrolled patients with HF seen in participating European centers between May 2011 and April 2013. The following scores designed to estimate 1- to 2-year all-cause mortality were calculated in each participant: CHARM (Candesartan in Heart Failure-Assessment of Reduction in Mortality), GISSI-HF (Gruppo Italiano per lo Studio della Streptochinasi nell9Infarto Miocardico-Heart Failure), MAGGIC (Meta-analysis Global Group in Chronic Heart Failure), and SHFM (Seattle Heart Failure Model). Patients with hospitalized HF (n = 6,920) and ambulatory HF patients missing any variable needed to estimate each score (n = 3,267) were excluded, leaving a final sample of 6,161 patients. Results At 1-year follow-up, 5,653 of 6,161 patients (91.8%) were alive. The observed-to-predicted survival ratios (CHARM: 1.10, GISSI-HF: 1.08, MAGGIC: 1.03, and SHFM: 0.98) suggested some overestimation of mortality by all scores except the SHFM. Overprediction occurred steadily across levels of risk using both the CHARM and the GISSI-HF, whereas the SHFM underpredicted mortality in all risk groups except the highest. The MAGGIC showed the best overall accuracy (area under the curve [AUC] = 0.743), similar to the GISSI-HF (AUC = 0.739; p = 0.419) but better than the CHARM (AUC = 0.729; p = 0.068) and particularly better than the SHFM (AUC = 0.714; p = 0.018). Less than 1% of patients received a prognostic estimate from their enrolling physician. Conclusions Performance of prognostic risk scores is still limited and physicians are reluctant to use them in daily practice. The need for contemporary, more precise prognostic tools should be considered.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Standards of Medical Care in Diabetes

Abstract: XI. STRATEGIES FOR IMPROVING DIABETES CARE D iabetes is a chronic illness that requires continuing medical care and patient self-management education to prevent acute complications and to reduce the risk of long-term complications. Diabetes care is complex and requires that many issues, beyond glycemic control, be addressed. A large body of evidence exists that supports a range of interventions to improve diabetes outcomes. These standards of care are intended to provide clinicians, patients, researchers, payors, and other interested individuals with the components of diabetes care, treatment goals, and tools to evaluate the quality of care. While individual preferences, comorbidities, and other patient factors may require modification of goals, targets that are desirable for most patients with diabetes are provided. These standards are not intended to preclude more extensive evaluation and management of the patient by other specialists as needed. For more detailed information, refer to Bode (Ed.): Medical Management of Type 1 Diabetes (1), Burant (Ed): Medical Management of Type 2 Diabetes (2), and Klingensmith (Ed): Intensive Diabetes Management (3). The recommendations included are diagnostic and therapeutic actions that are known or believed to favorably affect health outcomes of patients with diabetes. A grading system (Table 1), developed by the American Diabetes Association (ADA) and modeled after existing methods, was utilized to clarify and codify the evidence that forms the basis for the recommendations. The level of evidence that supports each recommendation is listed after each recommendation using the letters A, B, C, or E.