A Prospective study on central venous catheter related blood stream infections in surgical patients

Introduction:Vascular catheter related infections are the leading cause of nosocomial blood stream infections and associated with significant mortality and morbidity. This study is carried out to know the central venous related blood stream infections in surgical patients. Materials & Methods: The present study was carried out in the Department of Surgery, Sir Sayajirao General Hospital and Medical College, Baroda. It was a prospective study of total of 72 patients who had undergone Central Venous Catheterisation. The study was carried out from October 2017 to November 2018.Results:Most of the patients were in the age group of 30-50 years and males outnumbered females in all age group. There was no statistically significant difference between emergency and elective procedure of the CVC insertion. Number of attempts in CVC insertion was found to be statistically significant with. Tip colonization (p value - 0.0465) and BSI (p value-0.031). Number of lumens in CVC was found to be statistically significant with regards to tip colonization (p value-0.0449) and BSI (p value-0.0243). Highest mortality occurred within 1-10 days of hospital stay. There has been statistically significant difference between the number of days of CVC in situ and catheter colonization and BSI.Conclusion: we can conclude that our findings helps to implement Educational, training of health care workers, and adherence to standardized protocols for insertion and maintenance of CRBSI catheters significantly reduced the incidence of catheter-related infections and represent the most important preventive measures

Effect of diuretics on sodium, potassium and chloride levels- a cross sectional study

Background: Diuretics are one of the widely used class of drugs used in various cardiovascular and other disorders. However, they can cause various metabolic adverse effects, electrolyte imbalance being among important changes.Methods: A cross sectional study was conducted in which patients diagnosed with hypertension for at least one month were included. Over a period of 6 months patients were enrolled irrespective of whether they were taking diuretics or not. Demographic details, drug therapy and electrolyte levels were recorded in a proforma. Data was analyzed for difference in serum electrolyte levels between diuretic and nondiuretic groups as well as between different diuretic groups.Results: Out of total 177 participants, 71 were on diuretic therapy. There was significant difference in mean serum sodium (S. Na), potassium (S. K) and chlorine (S. Cl) levels between diuretic and nondiuretic groups (P<0.05). Hyponatremia, hypokalemia and hyperkalemia were observed. Thiazide diuretic group showed significantly greater hyponatremia compared to other diuretics (P=0.028). Hyperkalemia was observed in participants receiving K sparing diuretic or combination of loop and K sparing diuretics. Old age and number of comorbidities showed negative association with S. Na. Females had significantly more hyponatremia than male participants.Conclusions: The study confirms that diuretics cause various abnormalities in electrolytes namely Na and K levels. Old age, comorbidities and female sex are risk factors for hyponatremia

Using Multilevel Outcomes to Construct and Select Biomarker Combinations for Single-level Prediction

Biomarker studies may involve a multilevel outcome, such as no, mild, or severe disease. There is often interest in predicting one particular level of the outcome due to its clinical significance. The standard approach to constructing biomarker combinations in this context involves dichotomizing the outcome and using a binary logistic regression model. We assessed whether information can be usefully gained from instead using more sophisticated regression methods. Furthermore, it is often necessary to select among several candidate biomarker combinations. One strategy involves selecting a combination on the basis of its ability to predict the outcome level of interest. We propose an algorithm that leverages the multilevel outcome to inform combination selection. We apply this algorithm to data from a study of acute kidney injury after cardiac surgery, where the kidney injury may be absent, mild, or severe. Using more sophisticated modeling approaches to construct combinations provided gains over the binary logistic regression approach in specific settings. In the examples considered, the proposed algorithm for combination selection tended to reduce the impact of bias due to selection and to provide combinations with improved performance. Methods that utilize the multilevel nature of the outcome in the construction and/or selection of biomarker combinations have the potential to yield better combinations

Developing Biomarker Combinations in Multicenter Studies via Direct Maximization and Penalization

When biomarker studies involve patients at multiple centers and the goal is to develop biomarker combinations for diagnosis, prognosis, or screening, we consider evaluating the predictive capacity of a given combination with the center-adjusted AUC (aAUC), a summary of conditional performance. Rather than using a general method to construct the biomarker combination, such as logistic regression, we propose estimating the combination by directly maximizing the aAUC. Furthermore, it may be desirable to have a biomarker combination with similar predictive capacity across centers. To that end, we allow for penalization of the variability in center-specific performance. We demonstrate good asymptotic properties of the resulting combinations. Simulations provide small-sample evidence that maximizing the aAUC can lead to combinations with greater predictive capacity than combinations constructed via logistic regression. We further illustrate the utility of constructing combinations by maximizing the aAUC while penalizing variability. We apply these methods to data from a study of acute kidney injury after cardiac surgery

Biomarker Combinations for Diagnosis and Prognosis in Multicenter Studies: Principles and Methods

Many investigators are interested in combining biomarkers to predict an outcome of interest or detect underlying disease. This endeavor is complicated by the fact that many biomarker studies involve data from multiple centers. Depending upon the relationship between center, the biomarkers, and the target of prediction, care must be taken when constructing and evaluating combinations of biomarkers. We introduce a taxonomy to describe the role of center and consider how a biomarker combination should be constructed and evaluated. We show that ignoring center, which is frequently done by clinical researchers, is often not appropriate. The limited statistical literature proposes using random intercept logistic regression models, an approach that we demonstrate is generally inadequate and may be misleading. We instead propose using fixed intercept logistic regression, which appropriately accounts for center without relying on untenable assumptions. After constructing the biomarker combination, we recommend using performance measures that account for the multicenter nature of the data, namely the center-adjusted area under the receiver operating characteristic curve. We apply these methods to data from a multicenter study of acute kidney injury after cardiac surgery. Appropriately accounting for center, both in construction and evaluation, may increase the likelihood of identifying clinically useful biomarker combinations

Development of a hardware-In-the-Loop (HIL) testbed for cyber-physical security in smart buildings

As smart buildings move towards open communication technologies, providing access to the Building Automation System (BAS) through the intranet, or even remotely through the Internet, has become a common practice. However, BAS was historically developed as a closed environment and designed with limited cyber-security considerations. Thus, smart buildings are vulnerable to cyber-attacks with the increased accessibility. This study introduces the development and capability of a Hardware-in-the-Loop (HIL) testbed for testing and evaluating the cyber-physical security of typical BASs in smart buildings. The testbed consists of three subsystems: (1) a real-time HIL emulator simulating the behavior of a virtual building as well as the Heating, Ventilation, and Air Conditioning (HVAC) equipment via a dynamic simulation in Modelica; (2) a set of real HVAC controllers monitoring the virtual building operation and providing local control signals to control HVAC equipment in the HIL emulator; and (3) a BAS server along with a web-based service for users to fully access the schedule, setpoints, trends, alarms, and other control functions of the HVAC controllers remotely through the BACnet network. The server generates rule-based setpoints to local HVAC controllers. Based on these three subsystems, the HIL testbed supports attack/fault-free and attack/fault-injection experiments at various levels of the building system. The resulting test data can be used to inform the building community and support the cyber-physical security technology transfer to the building industry.Comment: Presented at the 2023 ASHRAE Winter Conferenc

Long-term clinical consequences of acute kidney injury in the HIV-infected

To evaluate the long-term consequences of acute kidney injury (AKI) in human immunodeficiency virus (HIV)-infected persons, we studied 17,325 patients in a national HIV registry during their first hospitalization. We determined the association of AKI with risk for heart failure, cardiovascular events, end-stage renal disease (ESRD), and mortality beginning 90 days after discharge. Based on AKI Network criteria, 2453 had stage 1; 273 had stage 2 or 3; and 334 had dialysis-requiring AKI. Over a mean follow-up period of 5.7 years, 333 had heart failure, 673 had cardiovascular diseases (CVDs), 348 developed ESRD, and 8405 deaths occurred. In multivariable-adjusted analyses, AKI stage 1 was associated with death and ESRD, but not heart failure or other CVD. Dialysis-requiring AKI had much stronger and significant associations with increased risk for long-term ESRD, and death in addition to heart failure and cardiovascular events. When AKI was reclassified to account for recovery, stage 1 with recovery was still associated with death, but not ESRD. Thus, in this national sample of HIV-infected persons, we found the clinical repercussions of AKI appear to extend beyond the hospital setting contributing to excess cardiovascular risks, ESRD, and mortality. Additionally, AKI affected almost one of six patients with HIV who survived at least 90 days following discharge