10 research outputs found

    Covert6: A Tool to Corroborate the Existence of IPv6 Covert Channels

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    Covert channels are any communication channel that can be exploited to transfer information in a manner that violates the system’s security policy. Research in the field has shown that, like many communication channels, IPv4 and the TCP/IP protocol suite have been susceptible to covert channels, which could be exploited to leak data or be used for anonymous communications. With the introduction of IPv6, researchers are acutely aware that many vulnerabilities of IPv4 have been remediated in IPv6. However, a proof of concept covert channel system was demonstrated in 2006. A decade later, IPv6 and its related protocols have undergone major changes, which has introduced a need to reevaluate the current state of covert channels within IPv6. The current research demonstrates the corroboration of covert channels in IPv6 by building a tool that establishes a covert channel against a simulated enterprise network. This is further validated against multiple channel criteria

    Evaluation of efficiency of various treatment modalities and outcome in patients with hyponatremia admitted to ICU

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    Background:Hyponatremia is the most common dyselectrolytemia in ICU patients and is an independent predictor of mortality. Its etiology, presentation and response to therapy and outcomes are highly unpredictable and rapid correction can lead to myelinolysis.Objectives: To Observe outcome of therapy in patients with hyponatremia and to compare efficacy of various treatment modalities.Methods:Observational study of patients admitted and managed by primary consultants. Sample of 50 consecutive adult patients admitted to Medical ICU from 01/10/2013 to 01/04/2014 with serum Sodium < 130 meq/L were prospectively studied after informed consent. Measures of Efficacy were taken as improvement in symptoms, adequate correction and length of ICU stay.Measures of safety were taken as mortality, rapid correction and over correction.Result:Hypertension was the most common comorbidity (48%). The commonest symptom was altered sensorium (52%). 14 % patients were receiving hydrochlorothiazide for HT. The mean sodium level was 116.96 ± 10.6 and the lowest value was 94 meq/l. 16% patients developed hyponatremia (Sodium level <130 meq/l) during ICU stay.Overcorrection of sodium level appears to be associated with increased mortality [3/5 vs 6/45] (Fishers exact test, p = 0.035). Conclusion:The volume of hypertonic saline calculated based on bodyweight and serum sodium levels consistently produces overcorrection of sodium levels in South Indian patients. The usage of hypertonic saline was restricted to the more severely hyponatremic patients and in general, less than 50% of the daily calculated volume of hypertonic saline only was administered. Keywords: Hyponatremia,serum,sodium,nursin

    Impact of COVID-19 on non-COVID intensive care unit service utilization, case mix and outcomes: A registry-based analysis from India

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    Background: Coronavirus disease 2019 (COVID-19) has been responsible for over 3.4 million deaths globally and over 25 million cases in India. As part of the response, India imposed a nation-wide lockdown and prioritized COVID-19 care in hospitals and intensive care units (ICUs). Leveraging data from the Indian Registry of IntenSive care, we sought to understand the impact of the COVID-19 pandemic on critical care service utilization, case-mix, and clinical outcomes in non-COVID ICUs. Methods: We included all consecutive patients admitted between 1st October 2019 and 27th September 2020. Data were extracted from the registry database and included patients admitted to the non-COVID or general ICUs at each of the sites. Outcomes included measures of resource-availability, utilisation, case-mix, acuity, and demand for ICU beds. We used a Mann-Whitney test to compare the pre-pandemic period (October 2019 - February 2020) to the pandemic period (March-September 2020). In addition, we also compared the period of intense lockdown (March-May 31st 2020) with the pre-pandemic period. Results: There were 3424 patient encounters in the pre-pandemic period and 3524 encounters in the pandemic period. Comparing these periods, weekly admissions declined (median [Q1 Q3] 160 [145,168] to 113 [98.5,134]; p<0.001); unit turnover declined (median [Q1 Q3] 12.1 [11.32,13] to 8.58 [7.24,10], p<0.001), and APACHE II score increased (median [Q1 Q3] 19 [19,20] to 21 [20,22] ; p<0.001). Unadjusted ICU mortality increased (9.3% to 11.7%, p=0.015) and the length of ICU stay was similar (median [Q1 Q3] 2.11 [2, 2] vs. 2.24 [2, 3] days; p=0.151). Conclusion: Our registry-based analysis of the impact of COVID-19 on non-COVID critical care demonstrates significant disruptions to healthcare utilization during the pandemic and an increase in the severity of illness

    Impact of COVID-19 on non-COVID intensive care unit service utilization, case mix and outcomes: A registry-based analysis from India

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    Background: Coronavirus disease 2019 (COVID-19) has been responsible for over 3.4 million deaths globally and over 25 million cases in India. As part of the response, India imposed a nation-wide lockdown and prioritized COVID-19 care in hospitals and intensive care units (ICUs). Leveraging data from the Indian Registry of IntenSive care, we sought to understand the impact of the COVID-19 pandemic on critical care service utilization, case-mix, and clinical outcomes in non-COVID ICUs. Methods: We included all consecutive patients admitted between 1st October 2019 and 27th September 2020. Data were extracted from the registry database and included patients admitted to the non-COVID or general ICUs at each of the sites. Outcomes included measures of resource-availability, utilisation, case-mix, acuity, and demand for ICU beds. We used a Mann-Whitney test to compare the pre-pandemic period (October 2019 - February 2020) to the pandemic period (March-September 2020). In addition, we also compared the period of intense lockdown (March-May 31st 2020) with the pre-pandemic period. Results: There were 3424 patient encounters in the pre-pandemic period and 3524 encounters in the pandemic period. Comparing these periods, weekly admissions declined (median [Q1 Q3] 160 [145,168] to 113 [98.5,134]; p<0.001); unit turnover declined (median [Q1 Q3] 12.1 [11.32,13] to 8.58 [7.24,10], p<0.001), and APACHE II score increased (median [Q1 Q3] 19 [19,20] to 21 [20,22] ; p<0.001). Unadjusted ICU mortality increased (9.3% to 11.7%, p=0.015) and the length of ICU stay was similar (median [Q1 Q3] 2.11 [2, 2] vs. 2.24 [2, 3] days; p=0.151). Conclusion: Our registry-based analysis of the impact of COVID-19 on non-COVID critical care demonstrates significant disruptions to healthcare utilization during the pandemic and an increase in the severity of illness

    Implementing an intensive care registry in India: Preliminary results of the case-mix program and an opportunity for quality improvement and research

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    Background: The epidemiology of critical illness in India is distinct from high-income countries. However, limited data exist on resource availability, staffing patterns, case-mix and outcomes from critical illness. Critical care registries, by enabling a continual evaluation of service provision, epidemiology, resource availability and quality, can bridge these gaps in information. In January 2019, we established the Indian Registry of IntenSive care to map capacity and describe case-mix and outcomes. In this report, we describe the implementation process, preliminary results, opportunities for improvement, challenges and future directions. Methods: All adult and paediatric ICUs in India were eligible to join if they committed to entering data for ICU admissions. Data are collected by a designated representative through the electronic data collection platform of the registry. IRIS hosts data on a secure cloud-based server and access to the data is restricted to designated personnel and is protected with standard firewall and a valid secure socket layer (SSL) certificate. Each participating ICU owns and has access to its own data. All participating units have access to de-identified network-wide aggregate data which enables benchmarking and comparison. Results: The registry currently includes 14 adult and 1 paediatric ICU in the network (232 adult ICU beds and 9 paediatric ICU beds). There have been 8721 patient encounters with a mean age of 56.9 (SD 18.9); 61.4% of patients were male and admissions to participating ICUs were predominantly unplanned (87.5%). At admission, most patients (61.5%) received antibiotics, 17.3% needed vasopressors, and 23.7% were mechanically ventilated. Mortality for the entire cohort was 9%. Data availability for demographics, clinical parameters, and indicators of admission severity was greater than 95%. Conclusions: IRIS represents a successful model for the continual evaluation of critical illness epidemiology in India and provides a framework for the deployment of multi-centre quality improvement and context-relevant clinical research

    The value of open-source clinical science in pandemic response: lessons from ISARIC

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    The value of open-source clinical science in pandemic response: lessons from ISARIC

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