Development of a Beneficiation Flow Sheet for Processing Silica Sand from Chertala Area of kerala

The silica sand from Chertala area of Alappuzha district has been reported to be of good quality. Presently, it is mined and transported to destinations inside and outside the State without any processing. A project on beneficiat-ion/ value addition of this sand was taken up as per the request from Directorate of Industries and Commerce, Govt. of Kerala. Objective of the project is to develop a flow sheet for the total utilization of all fractions of this sand. The aim is to value add the same to produce special grade glass making sand according to BIS specifications (IS:488-1980), a suitable fraction for foundry application as per 1S:3018- 1977 and also to recover the finer fractions of sand and heavy minerals which are below 180,um and consti-tutes about 14% by weight. However, only the first two objectives are covered in the present work

Type 2 diabetes and incidence of cardiovascular diseases: a cohort study in 1·9 million people.

BACKGROUND: The contemporary associations of type 2 diabetes with a wide range of incident cardiovascular diseases have not been compared. We aimed to study associations between type 2 diabetes and 12 initial manifestations of cardiovascular disease. METHODS: We used linked primary care, hospital admission, disease registry, and death certificate records from the CALIBER programme, which links data for people in England recorded in four electronic health data sources. We included people who were (or turned) 30 years or older between Jan 1, 1998, to March 25, 2010, who were free from cardiovascular disease at baseline. The primary endpoint was the first record of one of 12 cardiovascular presentations in any of the data sources. We compared cumulative incidence curves for the initial presentation of cardiovascular disease and used Cox models to estimate cause-specific hazard ratios (HRs). This study is registered at ClinicalTrials.gov (NCT01804439). FINDINGS: Our cohort consisted of 1 921 260 individuals, of whom 1 887 062 (98·2%) did not have diabetes and 34 198 (1·8%) had type 2 diabetes. We observed 113 638 first presentations of cardiovascular disease during a median follow-up of 5·5 years (IQR 2·1-10·1). Of people with type 2 diabetes, 6137 (17·9%) had a first cardiovascular presentation, the most common of which were peripheral arterial disease (reported in 992 [16·2%] of 6137 patients) and heart failure (866 [14·1%] of 6137 patients). Type 2 diabetes was positively associated with peripheral arterial disease (adjusted HR 2·98 [95% CI 2·76-3·22]), ischaemic stroke (1·72 [1·52-1·95]), stable angina (1·62 [1·49-1·77]), heart failure (1·56 [1·45-1·69]), and non-fatal myocardial infarction (1·54 [1·42-1·67]), but was inversely associated with abdominal aortic aneurysm (0·46 [0·35-0·59]) and subarachnoid haemorrhage (0·48 [0·26-0.89]), and not associated with arrhythmia or sudden cardiac death (0·95 [0·76-1·19]). INTERPRETATION: Heart failure and peripheral arterial disease are the most common initial manifestations of cardiovascular disease in type 2 diabetes. The differences between relative risks of different cardiovascular diseases in patients with type 2 diabetes have implications for clinical risk assessment and trial design. FUNDING: Wellcome Trust, National Institute for Health Research, and Medical Research Council

Long term health care use and costs in patients with stable coronary artery disease : a population based cohort using linked electronic health records (CALIBER)

Aims To examine long term health care utilisation and costs of patients with stable coronary artery disease (SCAD). Methods and results Linked cohort study of 94,966 patients with SCAD in England, 1st January 2001 to 31st March 2010, identified from primary care, secondary care, disease and death registries. Resource use and costs, and cost predictors by time and 5-year cardiovascular (CVD) risk profile were estimated using generalised linear models. Coronary heart disease hospitalisations were 20.5% in the first year and 66% in the year following a non-fatal (myocardial infarction, ischaemic or haemorrhagic stroke) event. Mean health care costs were £3,133 per patient in the first year and £10,377 in the year following a non-fatal event. First year predictors of cost included sex (mean cost £549 lower in females); SCAD diagnosis (NSTEMI cost £656 more than stable angina); and co-morbidities (heart failure cost £657 more per patient). Compared with lower risk patients (5-year CVD risk 3.5%), those of higher risk (5-year CVD risk 44.2%) had higher 5-year costs (£23,393 vs. £9,335) and lower lifetime costs (£43,020 vs. £116,888). Conclusion Patients with SCAD incur substantial health care utilisation and costs, which varies and may be predicted by 5-year CVD risk profile. Higher risk patients have higher initial but lower lifetime costs than lower risk patients as a result of shorter life expectancy. Improved cardiovascular survivorship among an ageing CVD population is likely to require stratified care in anticipation of the burgeoning demand

Validation of the myocardial-ischaemic-injury-index machine learning algorithm to guide the diagnosis of myocardial infarction in a heterogenous population: a prespecified exploratory analysis

BACKGROUND: Diagnostic pathways for myocardial infarction rely on fixed troponin thresholds, which do not recognise that troponin varies by age, sex, and time within individuals. To overcome this limitation, we recently introduced a machine learning algorithm that predicts the likelihood of myocardial infarction. Our aim was to evaluate whether this algorithm performs well in routine clinical practice and predicts subsequent events. METHODS: The myocardial-ischaemic-injury-index (MI3) algorithm was validated in a prespecified exploratory analysis using data from a multi-centre randomised trial done in Scotland, UK that included consecutive patients with suspected acute coronary syndrome undergoing serial high-sensitivity cardiac troponin I measurement. Patients with ST-segment elevation myocardial infarction were excluded. MI3 incorporates age, sex, and two troponin measurements to compute a value (0-100) reflecting an individual's likelihood of myocardial infarction during the index visit and estimates diagnostic performance metrics (including area under the receiver-operating-characteristic curve, and the sensitivity, specificity, negative predictive value, and positive predictive value) at the computed score. Model performance for an index diagnosis of myocardial infarction (type 1 or type 4b), and for subsequent myocardial infarction or cardiovascular death at 1 year was determined using the previously defined low-probability threshold (1·6) and high-probability MI3 threshold (49·7). The trial is registered with ClinicalTrials.gov, NCT01852123. FINDINGS: In total, 20 761 patients (64 years [SD 16], 9597 [46%] women) enrolled between June 10, 2013, and March 3, 2016, were included from the High-STEACS trial cohort, of whom 3272 (15·8%) had myocardial infarction. MI3 had an area under the receiver-operating-characteristic curve of 0·949 (95% CI 0·946-0·952) identifying 12 983 (62·5%) patients as low-probability for myocardial infarction at the pre-specified threshold (MI3 score <1·6; sensitivity 99·3% [95% CI 99·0-99·6], negative predictive value 99·8% [99·8-99·9]), and 2961 (14·3%) as high-probability at the pre-specified threshold (MI3 score ≥49·7; specificity 95·0% [94·6-95·3], positive predictive value 70·4% [68·7-72·0]). At 1 year, subsequent myocardial infarction or cardiovascular death occurred more often in high-probability patients than low-probability patients (520 [17·6%] of 2961 vs 197 [1·5%] of 12 983], p<0·0001). INTERPRETATION: In consecutive patients undergoing serial cardiac troponin measurement for suspected acute coronary syndrome, the MI3 algorithm accurately estimated the likelihood of myocardial infarction and predicted subsequent adverse cardiovascular events. By providing individual probabilities the MI3 algorithm could improve the diagnosis and assessment of risk in patients with suspected acute coronary syndrome. FUNDING: Medical Research Council, British Heart Foundation, National Institute for Health Research, and NHSX

Reproducible disease phenotyping at scale: Example of coronary artery disease in UK Biobank

IMPORTANCE: A lack of internationally agreed standards for combining available data sources at scale risks inconsistent disease phenotyping limiting research reproducibility. OBJECTIVE: To develop and then evaluate if a rules-based algorithm can identify coronary artery disease (CAD) sub-phenotypes using electronic health records (EHR) and questionnaire data from UK Biobank (UKB). DESIGN: Case-control and cohort study. SETTING: Prospective cohort study of 502K individuals aged 40-69 years recruited between 2006-2010 into the UK Biobank with linked hospitalization and mortality data and genotyping. PARTICIPANTS: We included all individuals for phenotyping into 6 predefined CAD phenotypes using hospital admission and procedure codes, mortality records and baseline survey data. Of these, 408,470 unrelated individuals of European descent had a polygenic risk score (PRS) for CAD estimated. EXPOSURE: CAD Phenotypes. MAIN OUTCOMES AND MEASURES: Association with baseline risk factors, mortality (n = 14,419 over 7.8 years median f/u), and a PRS for CAD. RESULTS: The algorithm classified individuals with CAD into prevalent MI (n = 4,900); incident MI (n = 4,621), prevalent CAD without MI (n = 10,910), incident CAD without MI (n = 8,668), prevalent self-reported MI (n = 2,754); prevalent self-reported CAD without MI (n = 5,623), yielding 37,476 individuals with any type of CAD. Risk factors were similar across the six CAD phenotypes, except for fewer men in the self-reported CAD without MI group (46.7% v 70.1% for the overall group). In age- and sex- adjusted survival analyses, mortality was highest following incident MI (HR 6.66, 95% CI 6.07-7.31) and lowest for prevalent self-reported CAD without MI at baseline (HR 1.31, 95% CI 1.15-1.50) compared to disease-free controls. There were similar graded associations across the six phenotypes per SD increase in PRS, with the strongest association for prevalent MI (OR 1.50, 95% CI 1.46-1.55) and the weakest for prevalent self-reported CAD without MI (OR 1.08, 95% CI 1.05-1.12). The algorithm is available in the open phenotype HDR UK phenotype library (https://portal.caliberresearch.org/). CONCLUSIONS: An algorithmic, EHR-based approach distinguished six phenotypes of CAD with distinct survival and PRS associations, supporting adoption of open approaches to help standardize CAD phenotyping and its wider potential value for reproducible research in other conditions