Maternal sepsis- an audit in a tertiary care center in South India

Background: Objective of this study was to audit the cases of maternal sepsis and analyze their maternal and fetal outcomes.Methods: A retrospective analysis of cases of maternal sepsis was undertaken for one year. Cases were taken as infection with fever, tachycardia, tachypnea, low oxygen saturation, high or low white blood counts and clinical or laboratory evidence of organ dysfunction and were analyzed. Demographic profile, gestational age at the time of diagnosis, organisms & their sources of infection was noted. Maternal outcomes of abortion, preterm delivery, need for intensive care unit (ICU) / high dependency unit (HDU) stay, blood and blood products, surgical interventions for the control of infection, culture-positive rate, source of organism, antibiotic usage and maternal mortality were analyzed. Fetal outcomes of early fetal demise, preterm birth, intrauterine death, stillbirth and term birth were studied.  Results: There were a total of 2327 deliveries, with 2333 live births during the study period. Twenty-two cases were diagnosed with sepsis, of which 17 survived, and five died. The incidence of maternal sepsis was 9.4/1000 live births & maternal deaths were 22.7%. Ninety percent were in the age group of 21-39 years, 68% were referred, 59% were post-delivery. Fifty nine percent of women who survived, and none of the women who died had medical co-morbidities. Respiratory tract followed by genitourinary tract were the most common source of infection, though culture was negative in 54.5% of the cases. The organisms grown were varied, with Escherichia coli (3/10) contributing to 30% of the culture positive cases. Spontaneous abortion and preterm delivery were 18% each, 36% required surgical intervention, 81% required ICU and 64.7% HDU stay. Seventy-seven had live birth.Conclusions: Maternal sepsis is an evolving preventable health burden. Early recognition requires a high index of clinical suspicion, even in the absence of risk factors. Mortality to morbidity ratio is very high in maternal sepsis. The timing of sepsis determines the fetal outcomes

Development and validation of the RCOS prognostic index: A bedside multivariable logistic regression model to predict hypoxaemia or death in patients with SARS-CoV-2 infection

Introduction: Previous COVID-19 prognostic models have been developed in hospital settings and are not applicable to COVID-19 cases in the general population. There is an urgent need for prognostic scores aimed to identify patients at high risk of complications at the time of COVID-19 diagnosis. Methods: The RDT COVID-19 Observational Study (RCOS) collected clinical data from patients with COVID-19 admitted regardless of the severity of their symptoms in a general hospital in India. We aimed to develop and validate a simple bedside prognostic score to predict the risk of hypoxaemia or death. Results: 4035 patients were included in the development cohort and 2046 in the validation cohort. The primary outcome occurred in 961 (23.8%) and 548 (26.8%) patients in the development and validation cohorts, respectively. The final model included 12 variables: age, systolic blood pressure, heart rate, respiratory rate, aspartate transaminase, lactate dehydrogenase, urea, C-reactive protein, sodium, lymphocyte count, neutrophil count, and neutrophil/lymphocyte ratio. In the validation cohort, the area under the receiver operating characteristic curve (AUROCC) was 0.907 (95% CI, 0.892-0.922), and the Brier Score was 0.098. The decision curve analysis showed good clinical utility in hypothetical scenarios where the admission of patients was decided according to the prognostic index. When the prognostic index was used to predict mortality in the validation cohort, the AUROCC was 0.947 (95% CI, 0.925-0.97) and the Brier score was 0.0188. Conclusions: The RCOS prognostic index could help improve the decision making in the current COVID-19 pandemic, especially in resource-limited settings with poor healthcare infrastructure such as India. However, implementation in other settings is needed to cross-validate and verify our findings

Large-scale Nonlinear Variable Selection via Kernel Random Features

We propose a new method for input variable selection in nonlinear regression. The method is embedded into a kernel regression machine that can model general nonlinear functions, not being a priori limited to additive models. This is the first kernel-based variable selection method applicable to large datasets. It sidesteps the typical poor scaling properties of kernel methods by mapping the inputs into a relatively low-dimensional space of random features. The algorithm discovers the variables relevant for the regression task together with learning the prediction model through learning the appropriate nonlinear random feature maps. We demonstrate the outstanding performance of our method on a set of large-scale synthetic and real datasets.Comment: Final version for proceedings of ECML/PKDD 201

Why the Failure? How Adversarial Examples Can Provide Insights for Interpretable Machine Learning

Recent advances in Machine Learning (ML) have profoundly changed many detection, classification, recognition and inference tasks. Given the complexity of the battlespace, ML has the potential to revolutionise how Coalition Situation Understanding is synthesised and revised. However, many issues must be overcome before its widespread adoption. In this paper we consider two - interpretability and adversarial attacks. Interpretability is needed because military decision-makers must be able to justify their decisions. Adversarial attacks arise because many ML algorithms are very sensitive to certain kinds of input perturbations. In this paper, we argue that these two issues are conceptually linked, and insights in one can provide insights in the other. We illustrate these ideas with relevant examples from the literature and our own experiments

Thermoelectric spin voltage in graphene

In recent years, new spin-dependent thermal effects have been discovered in ferromagnets, stimulating a growing interest in spin caloritronics, a field that exploits the interaction between spin and heat currents. Amongst the most intriguing phenomena is the spin Seebeck effect, in which a thermal gradient gives rise to spin currents that are detected through the inverse spin Hall effect. Non-magnetic materials such as graphene are also relevant for spin caloritronics, thanks to efficient spin transport, energy-dependent carrier mobility and unique density of states. Here, we propose and demonstrate that a carrier thermal gradient in a graphene lateral spin valve can lead to a large increase of the spin voltage near to the graphene charge neutrality point. Such an increase results from a thermoelectric spin voltage, which is analogous to the voltage in a thermocouple and that can be enhanced by the presence of hot carriers generated by an applied current. These results could prove crucial to drive graphene spintronic devices and, in particular, to sustain pure spin signals with thermal gradients and to tune the remote spin accumulation by varying the spin-injection bias

Interpretability of deep learning models: A survey of results

Deep neural networks have achieved near-human accuracy levels in various types of classification and prediction tasks including images, text, speech, and video data. However, the networks continue to be treated mostly as black-box function approximators, mapping a given input to a classification output. The next step in this human-machine evolutionary process-incorporating these networks into mission critical processes such as medical diagnosis, planning and control-requires a level of trust association with the machine output. Typically, statistical metrics are used to quantify the uncertainty of an output. However, the notion of trust also depends on the visibility that a human has into the working of the machine. In other words, the neural network should provide human-understandable justifications for its output leading to insights about the inner workings. We call such models as interpretable deep networks. Interpretability is not a monolithic notion. In fact, the subjectivity of an interpretation, due to different levels of human understanding, implies that there must be a multitude of dimensions that together constitute interpretability. In addition, the interpretation itself can be provided either in terms of the low-level network parameters, or in terms of input features used by the model. In this paper, we outline some of the dimensions that are useful for model interpretability, and categorize prior work along those dimensions. In the process, we perform a gap analysis of what needs to be done to improve model interpretability

Incisional hernia following colorectal cancer surgery according to suture technique: Hughes Abdominal Repair Randomized Trial (HART).

BACKGROUND: Incisional hernias cause morbidity and may require further surgery. HART (Hughes Abdominal Repair Trial) assessed the effect of an alternative suture method on the incidence of incisional hernia following colorectal cancer surgery. METHODS: A pragmatic multicentre single-blind RCT allocated patients undergoing midline incision for colorectal cancer to either Hughes closure (double far-near-near-far sutures of 1 nylon suture at 2-cm intervals along the fascia combined with conventional mass closure) or the surgeon's standard closure. The primary outcome was the incidence of incisional hernia at 1 year assessed by clinical examination. An intention-to-treat analysis was performed. RESULTS: Between August 2014 and February 2018, 802 patients were randomized to either Hughes closure (401) or the standard mass closure group (401). At 1 year after surgery, 672 patients (83.7 per cent) were included in the primary outcome analysis; 50 of 339 patients (14.8 per cent) in the Hughes group and 57 of 333 (17.1 per cent) in the standard closure group had incisional hernia (OR 0.84, 95 per cent c.i. 0.55 to 1.27; P = 0.402). At 2 years, 78 patients (28.7 per cent) in the Hughes repair group and 84 (31.8 per cent) in the standard closure group had incisional hernia (OR 0.86, 0.59 to 1.25; P = 0.429). Adverse events were similar in the two groups, apart from the rate of surgical-site infection, which was higher in the Hughes group (13.2 versus 7.7 per cent; OR 1.82, 1.14 to 2.91; P = 0.011). CONCLUSION: The incidence of incisional hernia after colorectal cancer surgery is high. There was no statistical difference in incidence between Hughes closure and mass closure at 1 or 2 years. REGISTRATION NUMBER: ISRCTN25616490 (http://www.controlled-trials.com)

A novel formulation of inhaled sodium cromoglicate (PA101) in idiopathic pulmonary fibrosis and chronic cough: a randomised, double-blind, proof-of-concept, phase 2 trial

Background Cough can be a debilitating symptom of idiopathic pulmonary fibrosis (IPF) and is difficult to treat. PA101 is a novel formulation of sodium cromoglicate delivered via a high-efficiency eFlow nebuliser that achieves significantly higher drug deposition in the lung compared with the existing formulations. We aimed to test the efficacy and safety of inhaled PA101 in patients with IPF and chronic cough and, to explore the antitussive mechanism of PA101, patients with chronic idiopathic cough (CIC) were also studied. Methods This pilot, proof-of-concept study consisted of a randomised, double-blind, placebo-controlled trial in patients with IPF and chronic cough and a parallel study of similar design in patients with CIC. Participants with IPF and chronic cough recruited from seven centres in the UK and the Netherlands were randomly assigned (1:1, using a computer-generated randomisation schedule) by site staff to receive PA101 (40 mg) or matching placebo three times a day via oral inhalation for 2 weeks, followed by a 2 week washout, and then crossed over to the other arm. Study participants, investigators, study staff, and the sponsor were masked to group assignment until all participants had completed the study. The primary efficacy endpoint was change from baseline in objective daytime cough frequency (from 24 h acoustic recording, Leicester Cough Monitor). The primary efficacy analysis included all participants who received at least one dose of study drug and had at least one post-baseline efficacy measurement. Safety analysis included all those who took at least one dose of study drug. In the second cohort, participants with CIC were randomly assigned in a study across four centres with similar design and endpoints. The study was registered with ClinicalTrials.gov (NCT02412020) and the EU Clinical Trials Register (EudraCT Number 2014-004025-40) and both cohorts are closed to new participants. Findings Between Feb 13, 2015, and Feb 2, 2016, 24 participants with IPF were randomly assigned to treatment groups. 28 participants with CIC were enrolled during the same period and 27 received study treatment. In patients with IPF, PA101 reduced daytime cough frequency by 31·1% at day 14 compared with placebo; daytime cough frequency decreased from a mean 55 (SD 55) coughs per h at baseline to 39 (29) coughs per h at day 14 following treatment with PA101, versus 51 (37) coughs per h at baseline to 52 (40) cough per h following placebo treatment (ratio of least-squares [LS] means 0·67, 95% CI 0·48–0·94, p=0·0241). By contrast, no treatment benefit for PA101 was observed in the CIC cohort; mean reduction of daytime cough frequency at day 14 for PA101 adjusted for placebo was 6·2% (ratio of LS means 1·27, 0·78–2·06, p=0·31). PA101 was well tolerated in both cohorts. The incidence of adverse events was similar between PA101 and placebo treatments, most adverse events were mild in severity, and no severe adverse events or serious adverse events were reported. Interpretation This study suggests that the mechanism of cough in IPF might be disease specific. Inhaled PA101 could be a treatment option for chronic cough in patients with IPF and warrants further investigation

Determinants of recovery from post-COVID-19 dyspnoea: analysis of UK prospective cohorts of hospitalised COVID-19 patients and community-based controls

Background The risk factors for recovery from COVID-19 dyspnoea are poorly understood. We investigated determinants of recovery from dyspnoea in adults with COVID-19 and compared these to determinants of recovery from non-COVID-19 dyspnoea. Methods We used data from two prospective cohort studies: PHOSP-COVID (patients hospitalised between March 2020 and April 2021 with COVID-19) and COVIDENCE UK (community cohort studied over the same time period). PHOSP-COVID data were collected during hospitalisation and at 5-month and 1-year follow-up visits. COVIDENCE UK data were obtained through baseline and monthly online questionnaires. Dyspnoea was measured in both cohorts with the Medical Research Council Dyspnoea Scale. We used multivariable logistic regression to identify determinants associated with a reduction in dyspnoea between 5-month and 1-year follow-up. Findings We included 990 PHOSP-COVID and 3309 COVIDENCE UK participants. We observed higher odds of improvement between 5-month and 1-year follow-up among PHOSP-COVID participants who were younger (odds ratio 1.02 per year, 95% CI 1.01–1.03), male (1.54, 1.16–2.04), neither obese nor severely obese (1.82, 1.06–3.13 and 4.19, 2.14–8.19, respectively), had no pre-existing anxiety or depression (1.56, 1.09–2.22) or cardiovascular disease (1.33, 1.00–1.79), and shorter hospital admission (1.01 per day, 1.00–1.02). Similar associations were found in those recovering from non-COVID-19 dyspnoea, excluding age (and length of hospital admission). Interpretation Factors associated with dyspnoea recovery at 1-year post-discharge among patients hospitalised with COVID-19 were similar to those among community controls without COVID-19. Funding PHOSP-COVID is supported by a grant from the MRC-UK Research and Innovation and the Department of Health and Social Care through the National Institute for Health Research (NIHR) rapid response panel to tackle COVID-19. The views expressed in the publication are those of the author(s) and not necessarily those of the National Health Service (NHS), the NIHR or the Department of Health and Social Care. COVIDENCE UK is supported by the UK Research and Innovation, the National Institute for Health Research, and Barts Charity. The views expressed are those of the authors and not necessarily those of the funders