Vernal Keratoconjunctivitis among Patients Presenting to the Outpatient Department of Ophthalmology of a Tertiary Care Centre: A Descriptive Cross-sectional Study

Introduction: Vernal keratoconjunctivitis is a seasonally recurring, bilateral inflammation of the conjunctiva, that occurs in male children with invariable personal or family history of atopy. It is characterized by interstitial inflammation of the cornea and can have sight-threatening complications if not treated in time. The aim of this study was to find out the prevalence of vernal keratoconjunctivitis among patients presenting to the outpatient department of ophthalmology of a tertiary care centre. Methods: This descriptive cross-sectional study was conducted among patients presenting to the outpatient department of ophthalmology from June 2020 to May 2021. Ethical approval was taken from the Institutional Review Committee (Reference number: IRC-PA-076). The relevant details of the history and clinical examination of the patients were recorded on a specifically designed proforma. A simple random sampling technique was used. Point estimate and 95% Confidence Interval were calculated. Results: Among 2400 patients with conjunctivitis visiting the outpatient department of ophthalmology, vernal keratoconjunctivitis was seen in 80 (3.33%) (2.61- 4.05, 95% Confidence Interval). Conclusions: The prevalence of vernal keratoconjunctivitis in our study was found to be similar to the other studies done in similar settings

Survey and evaluation of hypertension machine learning research

Background: Machine learning (ML) is pervasive in all fields of research, from automating tasks to complex decision‐making. However, applications in different specialities are variable and generally limited. Like other conditions, the number of studies employing ML in hypertension research is growing rapidly. In this study, we aimed to survey hypertension research using ML, evaluate the reporting quality, and identify barriers to ML's potential to transform hypertension care. Methods and Results: The Harmonious Understanding of Machine Learning Analytics Network survey questionnaire was applied to 63 hypertension‐related ML research articles published between January 2019 and September 2021. The most common research topics were blood pressure prediction (38%), hypertension (22%), cardiovascular outcomes (6%), blood pressure variability (5%), treatment response (5%), and real‐time blood pressure estimation (5%). The reporting quality of the articles was variable. Only 46% of articles described the study population or derivation cohort. Most articles (81%) reported at least 1 performance measure, but only 40% presented any measures of calibration. Compliance with ethics, patient privacy, and data security regulations were mentioned in 30 (48%) of the articles. Only 14% used geographically or temporally distinct validation data sets. Algorithmic bias was not addressed in any of the articles, with only 6 of them acknowledging risk of bias. Conclusions: Recent ML research on hypertension is limited to exploratory research and has significant shortcomings in reporting quality, model validation, and algorithmic bias. Our analysis identifies areas for improvement that will help pave the way for the realization of the potential of ML in hypertension and facilitate its adoption

Metabolic Syndrome and Acute Respiratory Distress Syndrome in Hospitalized Patients With COVID-19

Importance: Obesity, diabetes, and hypertension are common comorbidities in patients with severe COVID-19, yet little is known about the risk of acute respiratory distress syndrome (ARDS) or death in patients with COVID-19 and metabolic syndrome. Objective: To determine whether metabolic syndrome is associated with an increased risk of ARDS and death from COVID-19. Design, setting, and participants: This multicenter cohort study used data from the Society of Critical Care Medicine Discovery Viral Respiratory Illness Universal Study collected from 181 hospitals across 26 countries from February 15, 2020, to February 18, 2021. Outcomes were compared between patients with metabolic syndrome (defined as ≥3 of the following criteria: obesity, prediabetes or diabetes, hypertension, and dyslipidemia) and a control population without metabolic syndrome. Participants included adult patients hospitalized for COVID-19 during the study period who had a completed discharge status. Data were analyzed from February 22 to October 5, 2021. Exposures: Exposures were SARS-CoV-2 infection, metabolic syndrome, obesity, prediabetes or diabetes, hypertension, and/or dyslipidemia. Main outcomes and measures: The primary outcome was in-hospital mortality. Secondary outcomes included ARDS, intensive care unit (ICU) admission, need for invasive mechanical ventilation, and length of stay (LOS). Results: Among 46 441 patients hospitalized with COVID-19, 29 040 patients (mean [SD] age, 61.2 [17.8] years; 13 059 [45.0%] women and 15713 [54.1%] men; 6797 Black patients [23.4%], 5325 Hispanic patients [18.3%], and 16 507 White patients [57.8%]) met inclusion criteria. A total of 5069 patients (17.5%) with metabolic syndrome were compared with 23 971 control patients (82.5%) without metabolic syndrome. In adjusted analyses, metabolic syndrome was associated with increased risk of ICU admission (adjusted odds ratio [aOR], 1.32 [95% CI, 1.14-1.53]), invasive mechanical ventilation (aOR, 1.45 [95% CI, 1.28-1.65]), ARDS (aOR, 1.36 [95% CI, 1.12-1.66]), and mortality (aOR, 1.19 [95% CI, 1.08-1.31]) and prolonged hospital LOS (median [IQR], 8.0 [4.2-15.8] days vs 6.8 [3.4-13.0] days; P \u3c .001) and ICU LOS (median [IQR], 7.0 [2.8-15.0] days vs 6.4 [2.7-13.0] days; P \u3c .001). Each additional metabolic syndrome criterion was associated with increased risk of ARDS in an additive fashion (1 criterion: 1147 patients with ARDS [10.4%]; P = .83; 2 criteria: 1191 patients with ARDS [15.3%]; P \u3c .001; 3 criteria: 817 patients with ARDS [19.3%]; P \u3c .001; 4 criteria: 203 patients with ARDS [24.3%]; P \u3c .001). Conclusions and relevance: These findings suggest that metabolic syndrome was associated with increased risks of ARDS and death in patients hospitalized with COVID-19. The association with ARDS was cumulative for each metabolic syndrome criteria present

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Operationalizing algorithmic explainability in the context of risk profiling done by robo financial advisory apps

Robo Advisors are financial advisory apps that profile users into risk classes before providing financial advice. This risk profiling of users is of functional importance and is legally mandatory. Irregularities at this primary step will lead to incorrect recommenda- tions for the users. Further, lack of transparency and explanations for these automated decisions makes it tougher for users and regulators to understand the rationale behind the advice given by these apps, leading to a trust deficit. Regulators monitor this pro- filing but possess no independent toolkit to “demystify” the black box or adequately explain the decision-making process of the robo financial advisor. Our paper proposes an approach towards developing a ‘RegTech tool’ that can explain the robo advisors decision making. We use machine learning models to reverse engi- neer the importance of features in the black-box algorithm used by the robo advisor for risk profiling and provide three levels of explanation. First, we find the importance of inputs used in the risk profiling algorithm. Second, we infer relationships between inputs and with the assigned risk classes. Third, we allow regulators to explain decisions for any given user profile, in order to ‘spot check’ a random data point. With these three explanation methods, we provide regulators, who lack the technical knowledge to understand algorithmic decisions, a method to understand it and ensure that the risk-profiling done by robo advisory applications comply with the regulations they are subjected to

Do index futures cause spot market volatility? : an investigation of the Australian Resources Index

This paper applies GARCH models to ascertain the impact of index futures trading on the volatility of the spot market. Specifically, the research aims to determine whether the introduction of index futures trading increases or decreases the level of volatility within the underlying spot market. In addition, the research verifies the sensitivity of price to information as well as the impact the leverage effect may have on the degree and structure of volatility. As Australia is a commodity driven economy, resources constitute one of the largest economic sectors. Following from this, the daily closing price of the ASX 200 Resources Index for the period 2010 to 2016 was therefore used in the analysis. Given that 14 October 2013 was when the Australian Securities Exchange launched the ASX 200 Resources Index futures, investigating the volatility prior to and after this date is also a focus of the paper. The results of the study suggest that the introduction of index futures did not substantially increase the level of volatility in the spot market but found that there is an increase in sensitivity to historical information; and that a negative leverage effect exists within the Resources Index. Since the Australian share market operates within a dynamic financial landscape, the study adopts a framework that seeks to provide behavioural and macroeconomic explanations for the findings, where appropriate

Testing market efficiency across the GFC : a sectorial approach to the case of Australia

This paper applies a range of linear and non-linear tests to investigate weak form market efficiency within the Australian Stock Exchange across the period of the GFC. In particular, we aim to answer the following question: can we detect changes in the efficiency of the market during the period of volatility and disruption associated with the GFC, and observe any divergence in market efficiency across sectors that demonstrate differing market performance? Spanning a time period of 2000 to 2015, the data is cleaved into three periods of distinct economic conditions: a pre-crisis period of relatively high growth, the GFC period of disruption and contraction, and a post-GFC period of relatively low growth. Furthermore, market returns are split into five industry indices to search for evidence of market inefficiency in those sectors (real estate, consumer discretionary, financials, materials and metals and mining). A range of tests are applied in order to systematically investigate the structure of the market in each sector, including both linear and non-linear tests. The results of the study confirm the lack of weak form market efficiency of the selected ASX indices, with these sectors demonstrating little switching to an efficient market within the post-GFC environment

The impact of the GFC on sectoral market efficiency : non-linear testing for the case of Australia

This paper investigates the efficiency of the Australian stock market during the period of volatility and disruption associated with the Global Financial Crises (GFC). Furthermore, the investigation seeks to observe any divergence in market efficiency between industry sectors that demonstrate differing economic performance across the period. Spanning a time period of 2000-2015, the data are split into three periods of distinct economic conditions: a pre-crisis period of relatively high growth, the GFC period of disruption and contraction, and a post-GFC period of relatively low growth. Five sector indices listed on the Australian Securities Exchange are analysed to search for evidence of market efficiency (Real Estate, Consumer Discretionary, Financials, Materials, and Metals and Mining). A range of non-linear tests are applied in order to systematically investigate the structure of the market in each sector. The results highlight the cointegrated nature of non-linearity across related sectors, and also demonstrate that different industries within the same economy can reveal highly diverse patterns of non-linearity and market efficiency in response to financial crisis

Clinicoepidemiological Observational Study of Acquired Alopecias in Females Correlating with Anemia and Thyroid Function

Alopecia can either be inherited or acquired; the latter, more common, can be diffuse, patterned, and focal, each having cicatricial and noncicatricial forms. This observational study of 135 cases in a semiurban Indian population aimed to detect the prevalence of various forms of acquired alopecia in females and correlate the same with levels of hemoglobin, serum ferritin, triiodothyronine, thyroxin, and thyroid stimulating hormone. The majority (84, 62.2%) of our cases of alopecia had telogen effluvium followed by female pattern alopecia (32, 23.7%). Stress (86, 63.7%), topical application of chemicals (72, 53.3%), systemic medications for concurrent illnesses (62, 5%), and pregnancy (14, 10.3%) were the common exacerbating factors. Neither low hemoglobin (<12 gm%, 73.4%) nor low serum ferritin (<12 μg/L, 6.7%) was found to be statistically significant. A majority (90, 90.9%) of 99 cases with anemia (hemoglobin levels of <12 gm%) had serum ferritin levels >12 μg/L. Though lack of vitamin B12 testing was a limitation of our study, its deficiency could be the probable cause of iron deficiency as the majority (58, 64.4%) of these cases, as indeed majority (89, 65.4%) of our study population, were vegetarians. Thyroid disorders (23, 17%, including 9 newly diagnosed) were not of significance statistically

Predicting the impact of patient and private provider behavior on diagnostic delay for pulmonary tuberculosis patients in India: A simulation modeling study.

BackgroundTuberculosis (TB) incidence in India continues to be high due, in large part, to long delays experienced by patients before successful diagnosis and treatment initiation, especially in the private sector. This diagnostic delay is driven by patients' inclination to switch between different types of providers and providers' inclination to delay ordering of accurate diagnostic tests relevant to TB. Our objective is to quantify the impact of changes in these behavioral characteristics of providers and patients on diagnostic delay experienced by pulmonary TB patients.Methods and findingsWe developed a discrete event simulation model of patients' diagnostic pathways that captures key behavioral characteristics of providers (time to order a test) and patients (time to switch to another provider). We used an expectation-maximization algorithm to estimate the parameters underlying these behavioral characteristics, with quantitative data encoded from detailed interviews of 76 and 64 pulmonary TB patients in the 2 Indian cities of Mumbai and Patna, respectively, which were conducted between April and August 2014. We employed the estimated model to simulate different counterfactual scenarios of diagnostic pathways under altered behavioral characteristics of providers and patients to predict their potential impact on the diagnostic delay. Private healthcare providers including chemists were the first point of contact for the majority of TB patients in Mumbai (70%) and Patna (94%). In Mumbai, 45% of TB patients first approached less-than-fully-qualified providers (LTFQs), who take 28.71 days on average for diagnosis. About 61% of these patients switched to other providers without a diagnosis. Our model estimates that immediate testing for TB by LTFQs at the first visit (at the current level of diagnostic accuracy) could reduce the average diagnostic delay from 35.53 days (95% CI: 34.60, 36.46) to 18.72 days (95% CI: 18.01, 19.43). In Patna, 61% of TB patients first approached fully qualified providers (FQs), who take 9.74 days on average for diagnosis. Similarly, immediate testing by FQs at the first visit (at the current level of diagnostic accuracy) could reduce the average diagnostic delay from 23.39 days (95% CI: 22.77, 24.02) to 11.16 days (95% CI: 10.52, 11.81). Improving the diagnostic accuracy of providers per se, without reducing the time to testing, was not predicted to lead to any reduction in diagnostic delay. Our study was limited because of its restricted geographic scope, small sample size, and possible recall bias, which are typically associated with studies of patient pathways using patient interviews.ConclusionsIn this study, we found that encouraging private providers to order definitive TB diagnostic tests earlier during patient consultation may have substantial impact on reducing diagnostic delay in these urban Indian settings. These results should be combined with disease transmission models to predict the impact of changes in provider behavior on TB incidence