Srql: Sorted relational query language

A relation is an unordered collection of records. Often, however, there is an underlying order (e.g., a sequence of stock prices), and users want to pose queries that reflect this order (e.g., find a weekly moving average). SQL provides no support for posing such queries. In this paper, we show how a rich class of queries reflecting sort order can be naturally expressed and efficiently executed with simple extensions to SQL. 1

Effect of temperature and time delay in centrifugation on stability of select biomarkers of nutrition and non-communicable diseases in blood samples

Introduction: Preanalytical conditions are critical for blood sample integrity and poses challenge in surveys involving biochemical measurements. A cross sectional study was conducted to assess the stability of select biomarkers at conditions that mimic field situations in surveys. Material and methods: Blood from 420 volunteers was exposed to 2 – 8 °C, room temperature (RT), 22 – 30 °C and > 30 °C for 30 min, 6 hours, 12 hours and 24 hours prior to centrifugation. After different exposures, whole blood (N = 35) was used to assess stability of haemoglobin, HbA1c and erythrocyte folate; serum (N = 35) for assessing stability of ferritin, C-reactive protein (CRP), vitamins B12, A and D, zinc, soluble transferrin receptor (sTfR), total cholesterol, high density lipoprotein cholesterol (HDL), low density lipoprotein cholesterol (LDL), tryglicerides, albumin, total protein and creatinine; and plasma (N = 35) was used for glucose. The mean % deviation of the analytes was compared with the total change limit (TCL), computed from analytical and intra-individual imprecision. Values that were within the TCL were deemed to be stable. Result: Creatinine (mean % deviation 14.6, TCL 5.9), haemoglobin (16.4%, TCL 4.4) and folate (33.6%, TCL 22.6) were unstable after 12 hours at 22- 30°C, a temperature at which other analytes were stable. Creatinine was unstable even at RT for 12 hours (mean % deviation: 10.4). Albumin, CRP, glucose, cholesterol, LDL, triglycerides, vitamins B12 and A, sTfR and HbA1c were stable at all studied conditions. Conclusion: All analytes other than creatinine, folate and haemoglobin can be reliably estimated in blood samples exposed to 22-30°C for 12 hours in community-based studies

Hyperoxemia and excess oxygen use in early acute respiratory distress syndrome : Insights from the LUNG SAFE study

Publisher Copyright: © 2020 The Author(s). Copyright: Copyright 2020 Elsevier B.V., All rights reserved.Background: Concerns exist regarding the prevalence and impact of unnecessary oxygen use in patients with acute respiratory distress syndrome (ARDS). We examined this issue in patients with ARDS enrolled in the Large observational study to UNderstand the Global impact of Severe Acute respiratory FailurE (LUNG SAFE) study. Methods: In this secondary analysis of the LUNG SAFE study, we wished to determine the prevalence and the outcomes associated with hyperoxemia on day 1, sustained hyperoxemia, and excessive oxygen use in patients with early ARDS. Patients who fulfilled criteria of ARDS on day 1 and day 2 of acute hypoxemic respiratory failure were categorized based on the presence of hyperoxemia (PaO2 > 100 mmHg) on day 1, sustained (i.e., present on day 1 and day 2) hyperoxemia, or excessive oxygen use (FIO2 ≥ 0.60 during hyperoxemia). Results: Of 2005 patients that met the inclusion criteria, 131 (6.5%) were hypoxemic (PaO2 < 55 mmHg), 607 (30%) had hyperoxemia on day 1, and 250 (12%) had sustained hyperoxemia. Excess FIO2 use occurred in 400 (66%) out of 607 patients with hyperoxemia. Excess FIO2 use decreased from day 1 to day 2 of ARDS, with most hyperoxemic patients on day 2 receiving relatively low FIO2. Multivariate analyses found no independent relationship between day 1 hyperoxemia, sustained hyperoxemia, or excess FIO2 use and adverse clinical outcomes. Mortality was 42% in patients with excess FIO2 use, compared to 39% in a propensity-matched sample of normoxemic (PaO2 55-100 mmHg) patients (P = 0.47). Conclusions: Hyperoxemia and excess oxygen use are both prevalent in early ARDS but are most often non-sustained. No relationship was found between hyperoxemia or excessive oxygen use and patient outcome in this cohort. Trial registration: LUNG-SAFE is registered with ClinicalTrials.gov, NCT02010073publishersversionPeer reviewe

Mobile health for cardiovascular disease risk prediction and management in resource-constrained environments

It is well established that the leading global cause of mortality and morbidity, cardiovascular disease (CVD), is more severe in resource-constrained environments such as rural India (RI). This thesis explores how best to manage CVD risk in RI by using a mobile-based, point-of- care (POC) Clinical Decision Support System (CDSS), SMARThealth, that is designed to assist Accredited Social Health Activists (ASHAs) or minimally trained health workers. The four major focus areas are: (a) Design, development, and large-scale data collection using SMARThealth - an agile development process and user-centred design approach were followed to pilot test the CDSS with 292 participants. Evaluation metrics included system efficiency, end-user variability, usability, and sub-group analysis to identify better or poorly performing ASHAs. An improved version of SMARThealth was used for baseline data collection across 54 villages (62,194 participants) in Andhra Pradesh, India. 9864 (15.8%) of the participants were at high CVD risk. (b) Improvement of the sole CVD risk prediction algorithm for RI, the WHO/ISH CVD risk prediction charts (WHO-ISHc) - the choice of the low information (LI) model or high information (HI) model of WHO-ISHc was statistically significant for CVD risk prediction in RI (p=0.008;Ï2=7.03) with 155 subjects (or 14.5% of 1066 patients) having different CVD risk scores according to the LI and HI WHO-ISHc. A parsimonious POC test was developed to identify patients for whom risk prediction by the HI and LI WHO/ISHc differ (that is, for whom the assessment of total cholesterol would be beneficial). The POC test showed good discrimination (out-of-sample AUC 0.85 with Random Forests). (c) Assessment of best prediction algorithm for RI - eight highly predictive features of CVD risk were identified based on labelled data, and the resulting model (Model 1) had higher or equal AUCs and log-likelihood scores, and lower Brier scores when compared to a benchmark algorithm. The contribution of age and gender alone offered good discrimination and recalibration of Model 1 for RI was introduced. The lack of recorded end outcomes in RI prompted the use of an unsupervised approach to identify high-risk patients. Clusters of low and high CVD risks were found when ËK =2, but also clusters with intermediate risk when ËK =4 offering an alternative approach to identifying groups of high-risk patients. (d) Analysis from a randomised controlled trial evaluation of SMARThealth - preliminary data analysis of 131 high-risk patients during the first year of the randomised controlled trial showed a statistically significant reduction in median blood pressure between the 1st and 5th assessment (p=0.0097). The proportion of patients under treatment for high blood pressure continued to increase throughout.</p

Mobile health for cardiovascular disease risk prediction and management in resource-constrained environments: Mobile health and machine learning for cardiovascular disease risk management in rural India

It is well established that the leading global cause of mortality and morbidity, cardiovascular disease (CVD), is more severe in resource-constrained environments such as rural India (RI). This thesis explores how best to manage CVD risk in RI by using a mobile-based, point-of- care (POC) Clinical Decision Support System (CDSS), SMARThealth, that is designed to assist Accredited Social Health Activists (ASHAs) or minimally trained health workers. The four major focus areas are: (a) Design, development, and large-scale data collection using SMARThealth - an agile development process and user-centred design approach were followed to pilot test the CDSS with 292 participants. Evaluation metrics included system efficiency, end-user variability, usability, and sub-group analysis to identify better or poorly performing ASHAs. An improved version of SMARThealth was used for baseline data collection across 54 villages (62,194 participants) in Andhra Pradesh, India. 9864 (15.8%) of the participants were at high CVD risk. (b) Improvement of the sole CVD risk prediction algorithm for RI, the WHO/ISH CVD risk prediction charts (WHO-ISHc) - the choice of the low information (LI) model or high information (HI) model of WHO-ISHc was statistically significant for CVD risk prediction in RI (p=0.008;χ2=7.03) with 155 subjects (or 14.5% of 1066 patients) having different CVD risk scores according to the LI and HI WHO-ISHc. A parsimonious POC test was developed to identify patients for whom risk prediction by the HI and LI WHO/ISHc differ (that is, for whom the assessment of total cholesterol would be beneficial). The POC test showed good discrimination (out-of-sample AUC 0.85 with Random Forests). (c) Assessment of best prediction algorithm for RI - eight highly predictive features of CVD risk were identified based on labelled data, and the resulting model (Model 1) had higher or equal AUCs and log-likelihood scores, and lower Brier scores when compared to a benchmark algorithm. The contribution of age and gender alone offered good discrimination and recalibration of Model 1 for RI was introduced. The lack of recorded end outcomes in RI prompted the use of an unsupervised approach to identify high-risk patients. Clusters of low and high CVD risks were found when ˆK =2, but also clusters with intermediate risk when ˆK =4 offering an alternative approach to identifying groups of high-risk patients. (d) Analysis from a randomised controlled trial evaluation of SMARThealth - preliminary data analysis of 131 high-risk patients during the first year of the randomised controlled trial showed a statistically significant reduction in median blood pressure between the 1st and 5th assessment (p=0.0097). The proportion of patients under treatment for high blood pressure continued to increase throughout

Mobile health for cardiovascular disease risk prediction and management in resource-constrained environments

It is well established that the leading global cause of mortality and morbidity, cardiovascular disease (CVD), is more severe in resource-constrained environments such as rural India (RI). This thesis explores how best to manage CVD risk in RI by using a mobile-based, point-of- care (POC) Clinical Decision Support System (CDSS), SMARThealth, that is designed to assist Accredited Social Health Activists (ASHAs) or minimally trained health workers. The four major focus areas are: (a) Design, development, and large-scale data collection using SMARThealth - an agile development process and user-centred design approach were followed to pilot test the CDSS with 292 participants. Evaluation metrics included system efficiency, end-user variability, usability, and sub-group analysis to identify better or poorly performing ASHAs. An improved version of SMARThealth was used for baseline data collection across 54 villages (62,194 participants) in Andhra Pradesh, India. 9864 (15.8%) of the participants were at high CVD risk. (b) Improvement of the sole CVD risk prediction algorithm for RI, the WHO/ISH CVD risk prediction charts (WHO-ISHc) - the choice of the low information (LI) model or high information (HI) model of WHO-ISHc was statistically significant for CVD risk prediction in RI (p=0.008;χ2=7.03) with 155 subjects (or 14.5% of 1066 patients) having different CVD risk scores according to the LI and HI WHO-ISHc. A parsimonious POC test was developed to identify patients for whom risk prediction by the HI and LI WHO/ISHc differ (that is, for whom the assessment of total cholesterol would be beneficial). The POC test showed good discrimination (out-of-sample AUC 0.85 with Random Forests). (c) Assessment of best prediction algorithm for RI - eight highly predictive features of CVD risk were identified based on labelled data, and the resulting model (Model 1) had higher or equal AUCs and log-likelihood scores, and lower Brier scores when compared to a benchmark algorithm. The contribution of age and gender alone offered good discrimination and recalibration of Model 1 for RI was introduced. The lack of recorded end outcomes in RI prompted the use of an unsupervised approach to identify high-risk patients. Clusters of low and high CVD risks were found when ˆK =2, but also clusters with intermediate risk when ˆK =4 offering an alternative approach to identifying groups of high-risk patients. (d) Analysis from a randomised controlled trial evaluation of SMARThealth - preliminary data analysis of 131 high-risk patients during the first year of the randomised controlled trial showed a statistically significant reduction in median blood pressure between the 1st and 5th assessment (p=0.0097). The proportion of patients under treatment for high blood pressure continued to increase throughout.</p

SRQL: Sorted Relational Query Language

A relation is an unordered collection of records. Often, however, there is an underlying order (e.g., a sequence of stock prices), and users want to pose queries that reflect this order (e.g., find a weekly moving average). SQL provides no support for posing such queries. In this paper, we show how a rich class of queries reflecting sort order can be naturally expressed and efficiently executed with simple extensions to SQL. 1. Introduction Ordered data, or sequences, can be found in a wide range of commercial, statistical, and scientific applications. These applications require DBMS support to store, manipulate, and query sequences efficiently, and such support is missing in RDBMSs since the relational model provides sets of tuples as its only data structure. SQL [2], the most widely used query language for relational systems is incapable of answering some common queries posed by commercial and scientific applications, such as moving aggregates. One approach that is being explored in..

SRQL: Sorted Relational Query Language

A relation is an unordered collection of records. Often, however, there is an underlying order (e.g., a sequence of stock prices), and users want to pose queries that reflect this order (e.g., find a weekly moving average). SQL provides no support for posing such queries. In this paper, we show how a rich class of queries reflecting sort order can be naturally expressed and efficiently executed with simple extensions to SQL. 1. Introduction Ordered data, or sequences, can be found in a wide range of commercial, statistical, and scientific applications. These applications require DBMS support to store, manipulate, and query sequences efficiently, and such support is missing in RDBMSs since the relational model provides sets of tuples as its only data structure. SQL [2], the most widely used query language for relational systems is incapable of answering some common queries posed by commercial and scientific applications, such as moving aggregates. One approach that is being explored in..

Healthcare choices in Mumbai slums: A cross-sectional study [version 2; referees: 1 approved, 2 approved with reservations]

Background: Informal urban settlements, known as slums, are the home for a large proportion of the world population. Healthcare in these environments is extremely complex, driven by poverty, environmental challenges, and poor access to formal health infrastructures. This study investigated healthcare challenges faced and choices made by slum dwellers in Mumbai, India. Methods: Structured interviews with 549 slum dwellers from 13 slum areas in Mumbai, India, were conducted in order to obtain a population profile of health-related socio-economic and lifestyle factors, disease history and healthcare access. Statistical tools such as multinomial logistic regression were used to examine the association between such factors and health choices. Results: Private providers (or a mixture of public and private) were seen to be preferred by the study population for most health conditions (62% - 90% health consultations), apart from pregnancy (43% health consultations). Community-based services were also preferred to more remote options. Stark differences in healthcare access were observed between well-known conditions, such as minor injuries, pulmonary conditions, and pregnancy and emerging challenges, such as hypertension and diabetes. A number of socio-economic and lifestyle factors were found to be associated with health-related decisions, including choice of provider and expenditure. Conclusions: Better planning and coordination of health services, across public and private providers, is required to address mortality and morbidity in slum communities in India. This study provides insights into the complex landscape of diseases and health providers that slum dwellers navigate when accessing healthcare. Findings suggest that integrated services and public-private partnerships could help address demand for affordable community-based care and progress towards the target of universal health coverage