In search of a standardized noninvasive assessment of intravascular volume status, we prospectively compared the sonographic inferior vena cava collapsibility index (IVC-CI) and central venous pressures (CVPs). Our goals included the determination of CVP behavior across clinically relevant IVC-CI ranges, examination of unitary behavior of IVC-CI with changes in CVP, and estimation of the effect of positive end-expiratory pressure (PEEP) on the IVC-CI/CVP relationship. Prospective, observational study was performed in surgical/medical intensive care unit patients between October 2009 and July 2013. Patients underwent repeated sonographic evaluations of IVC-CI. Demographics, illness severity, ventilatory support, CVP, and patient positioning were recorded. Correlations were made between CVP groupings (<7, 7-12, 12-18, 19+) and IVC-CI ranges (<25, 25-49, 50-74, 75+). Comparison of CVP (2-unit quanta) and IVC-CI (5-unit quanta) was performed, followed by assessment of per-unit ΔIVC-CI/ΔCVP behavior as well as examination of the effect of PEEP on the IVC-CI/CVP relationship. We analyzed 320 IVC-CI/CVP measurement pairs from 79 patients (mean [SD] age, 55.8 [16.8] years; 64.6% male; mean [SD] Acute Physiology and Chronic Health Evaluation II, 11.7 [6.21]). Continuous data for IVC-CI/CVP correlated poorly (R = 0.177, p < 0.01) and were inversely proportional, with CVP less than 7 noted in approximately 10% of the patients for IVC-CIs less than 25% and CVP less than 7 observed in approximately 85% of patients for IVC-CIs greater than or equal to 75%. Median ΔIVC-CI per unit CVP was 3.25%. Most measurements (361 of 320) were collected in mechanically ventilated patients (mean [SD] PEEP, 7.76 [4.11] cm H2O). PEEP-related CVP increase was approximately 2 mm Hg to 2.5 mm Hg for IVC-CIs greater than 60% and approximately 3 mm Hg to 3.5 mm Hg for IVC-CIs less than 30%. PEEP also resulted in lower IVC-CIs at low CVPs, which reversed with increasing CVPs. When IVC-CI was examined across increasing PEEP ranges, we noted an inverse relationship between the two variables, but this failed to reach statistical significance. IVC-CI and CVP correlate inversely, with each 1 mm Hg of CVP corresponding to 3.3% median ΔIVC-CI. Low IVC-CI (<25%) is consistent with euvolemia/hypervolemia, while IVC-CI greater than 75% suggests intravascular volume depletion. The presence of PEEP results in 2 mm Hg to 3.5 mm Hg of CVP increase across the IVC-CI spectrum and lower collapsibility at low CVPs. Although IVC-CI decreased with increasing degrees of PEEP, this failed to reach statistical significance. While this study represents a step forward in the area of intravascular volume estimation using IVC-CI, our findings must be applied with caution owing to some methodologic limitations. Diagnostic study, level III. Prognostic study, level III.

Se skills, and often challenging visualization of the IVC in the postoperative patient. The current study aims to explore the potential for using femoral (FV) or internal jugular (IJV) vein collapsibility as alternative sonographic options in the absence of adequate IVC visualization. METHODS: A prospective, observational study comparing IVC-CI and Fem- and/or IJV-CI was performed in two intensive care units (ICU) between January 2012 and April 2014. Concurrent M-mode measurements of IVC-CI and FV- and/or IJV-CI were collected during each sonographic session. Measurements of IVC were obtained using standard technique. IJV-CI and FV-CI were measured using high-frequency, linear array ultrasound probe placed in the corresponding anatomic areas. Paired data were analyzed using coefficient of correlation/determination and Bland-Altman determination of measurement bias. RESULTS: We performed paired ultrasound examination of IVC-IJV (n = 39) and IVC-FV (n = 22), in 40 patients (mean age 54.1; 40% women). Both FV-CI and IJV-CI scans took less time to complete than IVC-CI scans (both, P < 0.02). Correlations between IVC-CI/FV-CI (R2 = 0.41) and IVC-CI/IJV-CI (R2 = 0.38) were weak. There was a mean -3.5% measurement bias between IVC-CI and IJV-CI, with trend toward overestimation for IJV-CI with increasing collapsibility. In contrast, FV-CI underestimated collapsibility by approximately 3.8% across the measured collapsibility range. CONCLUSION: Despite small measurement biases, correlations between IVC-CI and FV-/IJV-CI are weak. These results indicate that IJ-CI and FV-CI should not be used as a primary intravascular volume assessment tool for clinical decision support in the ICU. The authors propose that IJV-CI and FV-CI be reserved for clinical scenarios where sonographic acquisition of both IVC-CI or subclavian collapsibility are not feasible, especially when trended over time. Sonographers should be aware that IJV-CI tends to overestimate collapsibility when compared to IVC-CI, and FV-CI tends to underestimates collapsibility relative to IVC-CI....

Background and Purpose: The purpose of this study was to assess factors causing delay in treatment of acute stroke in a tertiary care institute in South India. Methods: All clinically suspected cases of acute stroke presenting to the emergency department over a period of 1 year were prospectively followed up and data collected as per a preset pro forma. The various time intervals from stroke onset to definitive management and other pertinent data were collected. The time delays have been evaluated in the decision tree model: Chi-squared Automatic Interaction Detection. Significance was assessed at 5% level of significance (P < 0.05). Results: The mean prehospital time delay for all clinically suspected stroke (n = 361) in our institute was 716 min and the median time 190 min. The mean total in-hospital delay was 94.17 ± 54.5 min and median time being 82 min. The onset of symptoms to first medical contact was the main interval that influenced the prehospital delay. Computed tomographic (CT) diagnosis to stroke unit admission influenced the in-hospital delay the most. Conclusions: Lack of awareness regarding stroke leads to delayed seeking of treatment for the same. The factors that contribute to the in-hospital delay included patient admission procedure delay, lack of staff to transport the patient, and the distance between the stroke unit and CT room. Educating the community with regard to “stroke” and implementation of a better pre- and in-hospital stroke care system is a need of the hour in the country.

Convolutional Neural Networks (CNNs) are kinds of deep learning models that were created primarily for processing and evaluating visual input, which makes them extremely applicable in the field of medical imaging. CNNs are particularly adept in automatically identifying complex patterns and features in pictures like X-rays, CT scans, and MRIs. They accomplish this by capturing hierarchical information utilizing layers of convolutional and pooling processes. By enabling precise disease diagnosis, anatomical structure segmentation, and even patient outcomes’ prediction, CNNs have transformed medical imaging. In this review paper, we examine how crucial CNNs are for improving diagnostic effectiveness and efficiency across a range of medical imaging applications. This review details how Convolutional Neural Networks (CNNs) are used, focusing on the development and use of 3D CNNs for processing and categorizing multidimensional and moving images. The paper discusses how critical 3D CNNs are in areas like analyzing surveillance videos and, especially, in the field of medical imaging to find pathological tissues. With this method, pathologists can segment the layers of the bladder with a lot more accuracy, which cuts down on the time they have to spend looking over them by hand. CNNs use specific filters to find spatial and temporal relationships in images, making understanding and interpreting them easier. CNNs are better at fitting image datasets because they have fewer parameters and weights that can be used more than once. This makes the network better able to understand complex images. This thorough review shows how 3D CNNs could improve the speed and accuracy of processing and analyzing medical images and how far they have already come.