Clinical and Biochemical Characteristics of COVID-19-Associated Acute Kidney Injury (COVAKI): A Proof-of-Concept Case-Control Study.

Introduction Acute kidney injury (AKI) develops in 20-70% of patients with COVID-19. AKI is a syndromic diagnosis with multiple causes and outcomes. This cross-sectional study explored different outcomes of AKI in patients admitted with COVID-19. Material and methods It was a cross-sectional and descriptive study carried out in a tertiary care teaching hospital in Western Maharashtra for two months (May to June 2020). We collected clinical and laboratory data of 456 inpatients admitted with COVID-19 over two consecutive months. We excluded patients already on dialysis upon arrival at the hospital. It predominantly consists of patients who developed AKI during their stay in the hospital. Result C-reactive protein (CRP) was elevated in patients with COVID-19 associated with AKI (COVAKI) (78.87) but was statistically significant (p<0.003). Ferritin was elevated significantly (1619.19) in patients with AKI (p<0.0001). Similarly, higher levels of D-dimer (426.35) and lower serum albumin (1.86) were associated with COVAKI (p<0.0001). The average ICU stay was six days for patients with AKI and 0.37 days for patients without AKI. Days on the ventilator were 3.3 days for patients with AKI and 0.11 days for non-AKI patients. Out of a total 12 deaths of COVID-19 patients over these two months, nine had AKI. This made the association statistically significant (p<0.0001). Conclusion The phenotype COVAKI was associated with higher inflammatory markers, prolonged hospital stay, days spent on a ventilator, and higher oxygen requirement translating into higher mortality compared to those without COVAKI. We found low serum albumin without a corresponding proteinuria or liver dysfunction. The development of COVAKI during the hospital stay was associated with the use of glucocorticoids, hydroxychloroquines (HCQs), and heparin.

Association between culture and the preference for, and perceptions of, 11 routes of medicine administration: A survey in 21 countries and regions.

Medicines can be taken by various routes of administration. These can impact the effects and perceptions of medicines. The literature about individuals' preferences for and perceptions of the different routes of administration is sparse, but indicates a potential influence of culture. Our aim was to determine: (i) any association between one's culture and one's preferred route of medicine administration and (ii) individual perceptions of pain, efficacy, speed of action and acceptability when medicines are swallowed or placed in the mouth, under the tongue, in the nose, eye, ear, lungs, rectum, vagina, on the skin, or areinjected. A cross-sectional, questionnaire-based survey of adults was conducted in 21 countries and regions of the world, namely, Tunisia, Ghana, Nigeria, Turkey, Ethiopia, Lebanon, Malta, Brazil, Great Britain, United States, India, Serbia, Romania, Portugal, France, Netherlands, Japan, South Korea, Hong Kong, mainland China and Estonia, using the Inglehart-Welzel cultural map to ensure coverage across all cultures. Participants scored the pain/discomfort, efficacy, speed of onset and acceptability of the different routes of medicine administration and stated their preferred route. Demographic information was collected. A total of 4435 participants took part in the survey. Overall, the oral route was the most preferred route, followed by injection, while the rectal route was the least preferred. While the oral route was the most preferred in all cultures, the percentage of participants selecting this route varied, from 98% in Protestant Europe to 50% in the African-Islamic culture. A multinomial logistic regression model revealed a number of predictors for the preferred route. Injections were favoured in the Baltic, South Asia, Latin America and African-Islamic cultures while dermal administration was favoured in Catholic Europe, Baltic and Latin America cultures. A marked association was found between culture and the preference for, and perceptions of the different routes by which medicines are taken. This applied to even the least favoured routes (vaginal and rectal). Only women were asked about the vaginal route, and our data shows that the vaginal route was slightly more popular than the rectal one.

Polygenic scores of diabetes-related traits in subgroups of type 2 diabetes in India: a cohort study.

Background: A machine-learning approach identified five subgroups of diabetes in Europeans which included severe autoimmune diabetes (SAID), severe insulin-deficient diabetes (SIDD), severe insulin-resistant diabetes (SIRD), mild obesity-related diabetes (MOD) and mild age-related diabetes (MARD) with partially distinct genetic aetiologies. We previously validated four of the non-autoimmune subgroups in people with young-onset type 2 diabetes (T2D) from the Indian WellGen study. Here, we aimed to apply European-derived centroids and genetic risk scores (GRSs) to the unselected (for age) WellGen to test their applicability and investigate the genetic aetiology of the Indian T2D subgroups. Methods: We applied European derived centroids and GRSs to T2D participants of Indian ancestry (WellGen, n = 2217, 821 genotyped) and compared them with normal glucose tolerant controls (Pune Maternal Nutrition Study, n = 461). Findings: SIDD was the predominant subgroup followed by MOD, whereas SIRD and MARD were less frequent. Weighted-GRS for T2D, obesity and lipid-related traits associated with T2D. We replicated some of the previous associations of GRS for T2D, insulin secretion, and BMI with SIDD and MOD. Unique to Indian subgroups was the association of GRS for (a) proinsulin with MOD and MARD, (b) liver-lipids with SIDD, SIRD and MOD, and (c) opposite effect of beta-cell GRS with SIDD and MARD, obesity GRS with MARD compared to Europeans. Genetic variants of fucosyltransferases were associated with T2D and MOD in Indians but not Europeans. Interpretation: The similarities emphasise the applicability of some of the European-derived GRSs to T2D and its subgroups in India while the differences highlight the need for large-scale studies to identify aetiologies in diverse ancestries. The data provide robust evidence for genetically distinct aetiologies for the T2D subgroups and at least partly mirror those seen in Europeans. Funding: Vetenskapsrådet, Diabetes Wellness, and Hjärt-Lungfonden (Sweden), DST (India), Wellcome Trust, Crafoord Foundation and Albert Påhlsson Foundation.

Assessment of the predictive capability of modelling and simulation to determine bioequivalence of inhaled drugs: A systematic review.

OBJECTIVES: There are a multitude of different modelling techniques that have been used for inhaled drugs. The main objective of this review was to conduct an exhaustive survey of published mathematical models in the area of asthma and chronic obstructive pulmonary disease (COPD) for inhalation drugs. Additionally, this review will attempt to assess the applicability of these models to assess bioequivalence (BE) of orally inhaled products (OIPs). EVIDENCE ACQUISITION: PubMed, Science Direct, Web of Science, and Scopus databases were searched from 1996 to 2020, to find studies that described mathematical models used for inhaled drugs in asthma/COPD. RESULTS: 50 articles were finally included in this systematic review. This research identified 22 articles on in silico aerosol deposition models, 20 articles related to population pharmacokinetics and 8 articles on physiologically based pharmacokinetic modelling (PBPK) modelling for inhaled drugs in asthma/COPD. Among all the aerosol deposition models, computational fluid dynamics (CFD) simulations are more likely to predict regional aerosol deposition pattern in human respiratory tracts. Across the population PK articles, body weight, gender, age and smoking status were the most common covariates that were found to be significant. Further, limited published PBPK models reported approximately 29 parameters relevant for absorption and distribution of inhaled drugs. The strengths and weaknesses of each modelling technique has also been reviewed. CONCLUSION: Overall, while there are different modelling techniques that have been used for inhaled drugs in asthma and COPD, there is very limited application of these models for assessment of bioequivalence of OIPs. This review also provides a ready reference of various parameters that have been considered in various models which will aid in evaluation if one model or hybrid in silico models need to be considered when assessing bioequivalence of OIPs.

Subgroups of patients with young-onset type 2 diabetes in India reveal insulin deficiency as a major driver.

AIM/HYPOTHESIS: Five subgroups were described in European diabetes patients using a data driven machine learning approach on commonly measured variables. We aimed to test the applicability of this phenotyping in Indian individuals with young-onset type 2 diabetes. METHODS: We applied the European-derived centroids to Indian individuals with type 2 diabetes diagnosed before 45 years of age from the WellGen cohort (n = 1612). We also applied de novo k-means clustering to the WellGen cohort to validate the subgroups. We then compared clinical and metabolic-endocrine characteristics and the complication rates between the subgroups. We also compared characteristics of the WellGen subgroups with those of two young European cohorts, ANDIS (n = 962) and DIREVA (n = 420). Subgroups were also assessed in two other Indian cohorts, Ahmedabad (n = 187) and PHENOEINDY-2 (n = 205). RESULTS: Both Indian and European young-onset type 2 diabetes patients were predominantly classified into severe insulin-deficient (SIDD) and mild obesity-related (MOD) subgroups, while the severe insulin-resistant (SIRD) and mild age-related (MARD) subgroups were rare. In WellGen, SIDD (53%) was more common than MOD (38%), contrary to findings in Europeans (Swedish 26% vs 68%, Finnish 24% vs 71%, respectively). A higher proportion of SIDD compared with MOD was also seen in Ahmedabad (57% vs 33%) and in PHENOEINDY-2 (67% vs 23%). Both in Indians and Europeans, the SIDD subgroup was characterised by insulin deficiency and hyperglycaemia, MOD by obesity, SIRD by severe insulin resistance and MARD by mild metabolic-endocrine disturbances. In WellGen, nephropathy and retinopathy were more prevalent in SIDD compared with MOD while the latter had higher prevalence of neuropathy. CONCLUSIONS /INTERPRETATION: Our data identified insulin deficiency as the major driver of type 2 diabetes in young Indians, unlike in young European individuals in whom obesity and insulin resistance predominate. Our results provide useful clues to pathophysiological mechanisms and susceptibility to complications in type 2 diabetes in the young Indian population and suggest a need to review management strategies.

Pandemic Analytics: How Countries are Leveraging Big Data Analytics and Artificial Intelligence to Fight COVID-19?

Emergence of coronavirus in December 2019 and its spread across the world in the following months has made it a global health concern. The uncertainty about its evolution, transmission and effect of SARS-CoV-2, has left the countries and their governments in a worrisome state. Ambiguity about the strategies that would work towards mitigating the impact of virus has prompted them to use data-driven methods. Several countries started applying big data and advanced analytics technology for management of the crisis. This study aims to understand how different nations have employed analytics to deal with COVID-19. This paper reviews various strategies employed by different governments and organizations across nations that use advanced analytics to tackle pandemic. In the current emergency of corona virus, there have been several measures that organizations have taken to mitigate its impact, thanks to the evolution of computing technology. Big data and analytical tools provide various solutions like detection of existing COVID-19 cases, prediction of future outbreak, anticipation of potential preventive and therapeutic agents, and assistance in informed decision-making. This review discusses the big data analytics and artificial intelligence approaches that policy makers, researchers, epidemiologists and private organizations have adopted. By examining the different ways and areas where data analytics has been utilized, this study provides the other nations with the progressive scheme to address the pandemic.

Community acceptance of environmental larviciding against malaria with Bacillus thuringiensis israelensis in rural Burkina Faso - A knowledge, attitudes and practices study.

BACKGROUND: Malaria control is based on early treatment of cases and on vector control. The current measures for malaria vector control in Africa are mainly based on long-lasting insecticidal nets (LLINs) and to a much smaller extent on indoor residual spraying (IRS). While bed net use is widely distributed and its role is intensively researched, Bti-based larviciding is a relatively novel tool in Africa. In this study, we analyze the perception and acceptability of Bti-based larval source management under different larviciding scenarios that were performed in a health district in Burkina Faso. OBJECTIVE: To research people's perception and acceptance regarding biological larviciding interventions against malaria in their communities. METHODS: A cross-sectional study was undertaken using a total of 634 administered questionnaires. Data were collected in a total of 36 rural villages and in seven town quarters of the semi-urban town of Nouna. RESULTS: Respondents had basic to good knowledge regarding malaria transmission and how to protect oneself against it. More than 90% reported sleeping under a bed net, while other measures such as mosquito coils and insecticides were only used by a minority. The majority of community members reported high perceived reductions in mosquito abundance and the number of malaria episodes. There was a high willingness to contribute financially to larviciding interventions among interviewees. CONCLUSIONS: This study showed that biological larviciding interventions are welcomed by the population that they are regarded as an effective and safe means to reduce mosquito abundance and malaria transmission. A routine implementation would, despite low intervention costs, require community ownership and contribution.

Characteristics of COVID-19 Patients Admitted to a Tertiary Care Hospital in Pune, India and Predictors of Requirement for Intensive Care Treatment.

OBJECTIVES: 1. To study associations of severity of COVID-19 disease with clinical features and laboratory markers. 2. To develop a model to predict the need for ICU treatment. METHODS: This is an analysis of clinical course in 800 consecutive patients from a dedicated COVID-19 tertiary care hospital in Pune, India (8th April to 15th June 2020). We obtained clinical and laboratory information, severity grading and progress from hospital records. We studied associations of these characteristics with need for ICU management. We developed a predictive model of need for ICU treatment among first 500 patients and tested its sensitivity and specificity in the following 300 patients. RESULTS: Average age was 41 years, 16% were 20 years of age, 55% were male, 50% were asymptomatic and 16% had at least one comorbidity. Using MoHFW India severity guidelines, 73% patients had mild, 6% moderate and 20% severe disease. Severity was associated with higher age, symptomatic presentation, elevated neutrophil and reduced lymphocyte counts and elevated inflammatory markers. Seventy-seven patients needed ICU treatment: they were older (56 years), more symptomatic and had lower SpO2 and abnormal chest X-ray and deranged hematology and biochemistry at admission. A model trained on the first 500 patients, using above variables predicted need for ICU treatment with sensitivity 80%, specificity 88% in subsequent 300 patients; exclusion of expensive laboratory tests (Ferritin, C- Reactive Protein) did not affect accuracy. CONCLUSION: In the early phase of COVID- 19 pendemic, a significant proportion of hospitalized patients were young and asymptomatic. Need for ICU treatment was predicted by simple measures including higher age, symptomatic onset, low SpO2 and abnormal chest X-ray. We propose a simple model for referring patients for treatment at specialized COVID-19 hospitals.

Disease progression of cancer patients during COVID-19 pandemic: a comprehensive analytical strategy by time-dependent modelling.

BACKGROUND: As the whole world is experiencing the cascading effect of a new pandemic, almost every aspect of modern life has been disrupted. Because of health emergencies during this period, widespread fear has resulted in compromised patient safety, especially for patients with cancer. It is very challenging to treat such cancer patients because of the complexity of providing care and treatment, along with COVID-19. Hence, an effective treatment comparison strategy is needed. We need to have a handy tool to understand cancer progression in this unprecedented scenario. Linking different events of cancer progression is the need of the hour. It is a huge challenge for the development of new methodology. METHODS: This article explores the time lag effect and makes a statistical inference about the best experimental arm using Accelerated Failure Time (AFT) model and regression methods. The work is presented as the occurrence of other events as a hazard rate after the first event (relapse). The time lag effect between the events is linked and analysed. RESULTS: The results were presented as a comprehensive analytical strategy by joining all disease progression. An AFT model applied with the transition states, and the dependency structure between the gap times was used by the auto-regression model. The effects of arms were compared using the coefficient of auto-regression and accelerated failure time (AFT) models. CONCLUSIONS: We provide the solutions to overcome the issue with intervals between two consecutive events in motivating head and neck cancer (HNC) data. COVID-19 is not going to leave us soon. We have to conduct several cancer clinical trials in the presence of COVID-19. A comprehensive analytical strategy to analyse cancer clinical trial data during COVID-19 pandemic is presented.

Transforming healthcare with big data analytics and artificial intelligence: A systematic mapping study.

The domain of healthcare has always been flooded with a huge amount of complex data, coming in at a very fast-pace. A vast amount of data is generated in different sectors of healthcare industry: data from hospitals and healthcare providers, medical insurance, medical equipment, life sciences and medical research. With the advancement in technology, there is a huge potential for utilization of this data for transforming healthcare. The application of analytics, machine learning and artificial intelligence over big data enables identification of patterns and correlations and hence provides actionable insights for improving the delivery of healthcare. There have been many contributions to the literature in this topic, but we lack a comprehensive view of the current state of research and application. This paper focuses on assessing the available literature in order to provide the researchers with evidence that enable fostering further development in this area. A systematic mapping study was conducted to identify and analyze research on big data analytics and artificial intelligence in healthcare, in which 2421 articles between 2013 and February 2019 were evaluated. The results of this study will help understand the needs in application of these technologies in healthcare by identifying the areas that require additional research. It will hence provide the researchers and industry experts with a base for future work.

Involving private healthcare practitioners in an urban NCD sentinel surveillance system: lessons learned from Pune, India.

BACKGROUND: Despite the rising impact of non-communicable diseases (NCDs) on public health in India, lack of quality data and routine surveillance hampers the planning process for NCD prevention and control. Current surveillance programs focus largely on communicable diseases and do not adequately include the private healthcare sector as a major source of care in cities. OBJECTIVE: The objective of the study was to conceptualize, implement, and evaluate a prototype for an urban NCD sentinel surveillance system among private healthcare practitioners providing primary care in Pune, India. DESIGN: We mapped all private healthcare providers in three selected areas of the city, conducted a knowledge, attitude, and practice survey with regard to surveillance among 258 consenting practitioners, and assessed their willingness to participate in a routine NCD surveillance system. In total, 127 practitioners agreed and were included in a 6-month surveillance study. Data on first-time diagnoses of 10 selected NCDs alongside basic demographic and socioeconomic patient information were collected onsite on a monthly basis using a paper-based register. Descriptive and regression analyses were performed. RESULTS: In total, 1,532 incident cases were recorded that mainly included hypertension (n=622, 41%) and diabetes (n=460, 30%). Dropout rate was 10% (n=13). The monthly reporting consistency was quite constant, with the majority (n=63, 50%) submitting 1-10 cases in 6 months. Average number of submitted cases was highest among allopathic practitioners (17.4). A majority of the participants (n=104, 91%) agreed that the surveillance design could be scaled up to cover the entire city. CONCLUSIONS: The study indicates that private primary healthcare providers (allopathic and alternate medicine practitioners) play an important role in the diagnosis and treatment of NCDs and can be involved in NCD surveillance, if certain barriers are addressed. Main barriers observed were lack of regulation of the private sector, cross-practices among different systems of medicine, limited clinic infrastructure, and knowledge gaps about disease surveillance. We suggest a voluntary augmented sentinel NCD surveillance system including public and private healthcare facilities at all levels of care.

Knowledge, attitude, and practices with respect to disease surveillance among urban private practitioners in Pune, India.

BACKGROUND: Participation of private practitioners in routine disease surveillance in India is minimal despite the fact that they account for over 70% of the primary healthcare provision. We aimed to investigate the knowledge, attitudes, and practices of private practitioners in the city of Pune toward disease surveillance. Our goal was to identify what barriers and facilitators determine their participation in current and future surveillance efforts. DESIGN: A questionnaire-based survey was conducted among 258 practitioners (response rate 86%). Data were processed using SPSS™ Inc., Chicago, IL, USA, version 17.0.1. RESULTS: Knowledge regarding surveillance, although limited, was better among allopathy practitioners. Surveillance practices did not differ significantly between allopathy and alternate medicine practitioners. Multivariable logistic regression suggested practicing allopathy [odds ratio (OR) 3.125, 95% confidence interval (CI) 1.234-7.915, p=0.016] and availability of a computer (OR 3.670, 95% CI 1.237-10.889, p=0.019) as significant determinants and the presence of a laboratory (OR 3.792, 95% CI 0.998-14.557, p=0.052) as a marginal determinant of the practitioner's willingness to participate in routine disease surveillance systems. Lack of time (137, 55%) was identified as the main barrier at the individual level alongside inadequately trained subordinate staff (14, 6%). Main extrinsic barriers included lack of cooperation between government and the private sector (27, 11%) and legal issues involved in reporting data (15, 6%). There was a general agreement among respondents (239, 94%) that current surveillance efforts need strengthening. Over a third suggested that availability of detailed information and training about surveillance processes (70, 33%) would facilitate reporting. CONCLUSIONS: The high response rate and the practitioners' willingness to participate in a proposed pilot non-communicable disease surveillance system indicate that there is a general interest from the private sector in cooperating. Keeping reporting systems simple, preferably in electronic formats that minimize infrastructure and time requirements on behalf of the private practitioners, will go a long way in consolidating disease surveillance efforts in the state. Organizing training sessions, providing timely feedback, and awarding continuing medical education points for routine data reporting seem feasible options and should be piloted.

Assessment of the core and support functions of the Integrated Disease Surveillance system in Maharashtra, India.

BACKGROUND: Monitoring the progress of the Integrated Disease Surveillance (IDS) strategy is an important component to ensure its sustainability in the state of Maharashtra in India. The purpose of the study was to document the baseline performance of the system on its core and support functions and to understand the challenges for its transition from an externally funded "project" to a state owned surveillance "program". METHODS: Multi-centre, retrospective cross-sectional evaluation study to assess the structure, core and support surveillance functions using modified WHO generic questionnaires. All 34 districts in the state and randomly identified 46 facilities and 25 labs were included in the study. RESULTS: Case definitions were rarely used at the periphery. Limited laboratory capacity at all levels compromised case and outbreak confirmation. Only 53% districts could confirm all priority diseases. Stool sample processing was the weakest at the periphery. Availability of transport media, trained staff, and rapid diagnostic tests were main challenges at the periphery. Data analysis was weak at both district and facility levels. Outbreak thresholds were better understood at facility level (59%) than at the district (18%). None of the outbreak indicator targets were met and submission of final outbreak report was the weakest. Feedback and training was significantly better (p < 0.0001) at district level (65%; 76%) than at facility level (15%; 37%). Supervision was better at the facility level (37%) than at district (18%) and so were coordination, communication and logistic resources. Contractual part time positions, administrative delays in recruitment, and vacancies (30%) were main human resource issues that hampered system performance. CONCLUSIONS: Significant progress has been made in the core and support surveillance functions in Maharashtra, however some challenges exist. Support functions (laboratory, transport and communication equipment, training, supervision, human and other resources) are particularly weak at the district level. Structural integration and establishing permanent state and district surveillance officer positions will ensure leadership; improve performance; support continuity; and offer sustainability to the program. Institutionalizing the integrated disease surveillance strategy through skills based personnel development and infrastructure strengthening at district levels is the only way to avoid it from ending up isolated! Improving surveillance quality should be the next on agenda for the state.