Patient Vital Signs Research Articles

Design of an Adaptive State Anesthesia Feedback Controller

Anesthesia is critical in medical procedures to ensure the patient's body remains stable and unresponsive during surgery. However, administering the correct dose can be challenging, particularly in prolonged surgeries. An auto-controlled system that incorporates vital sensors and a microprocessor controller has been proposed to address this issue. This system uses an infusion pump to provide the correct amount of anesthetic based on the patient's vital signs. The microprocessor takes control of the system once initiated and signals the motor driver to start injecting the required amount of anesthesia while monitoring vital signs such as temperature, heartbeat, and Spo2. The system alerts the doctor if any abnormality is detected, and the supply of anesthetic is stopped until everything returns to normal. This system ensures accurate anesthetic dosage, minimizing the risk of complications and ensuring a safe surgical procedure.

Smart Chemical Sensor and Biosensor Networks for Healthcare 4.0.

Driven by technological advances from Industry 4.0, Healthcare 4.0 synthesizes medical sensors, artificial intelligence (AI), big data, the Internet of things (IoT), machine learning, and augmented reality (AR) to transform the healthcare sector. Healthcare 4.0 creates a smart health network by connecting patients, medical devices, hospitals, clinics, medical suppliers, and other healthcare-related components. Body chemical sensor and biosensor networks (BSNs) provide the necessary platform for Healthcare 4.0 to collect various medical data from patients. BSN is the foundation of Healthcare 4.0 in raw data detection and information collecting. This paper proposes a BSN architecture with chemical sensors and biosensors to detect and communicate physiological measurements of human bodies. These measurement data help healthcare professionals to monitor patient vital signs and other medical conditions. The collected data facilitates disease diagnosis and injury detection at an early stage. Our work further formulates the problem of sensor deployment in BSNs as a mathematical model. This model includes parameter and constraint sets to describe patient body characteristics, BSN sensor features, as well as biomedical readout requirements. The proposed model's performance is evaluated by multiple sets of simulations on different parts of the human body. Simulations are designed to represent typical BSN applications in Healthcare 4.0. Simulation results demonstrate the impact of various biofactors and measurement time on sensor selections and readout performance.

Performance evaluation of multivariate statistical techniques using edge-enabled optimisation for change detection in activity monitoring

The monitoring of human activities using simple body worn sensors is an important and emerging area of research in machine learning. The sensors capture a large amount of data in a short period of Time in a relatively un-obtrusive manner. The sensor data might have different transitions to be used for deification of different user activities. Therefore, change point detection can be used to classify the transition from one underlying distribution to another. The automatic and accurate change point detection is not only used for different events, however, can also be used for generating real world datasets and responding to changes in patient vital signs in critical situation. Moreover, the huge amount of data can use the current state-of-the-art cloud and edge computing platforms to process the change detection locally and more efficiently. In this paper, we used multivariate exponentially weighted moving Average (MEWMA) for online change point detection. Additionally, genetic algorithm (GA) and particle swarm optimization (PSO) is used to automatically identify an optimal parameter set by maximizing the F-measure. The optimisation approach is implemented over an edge cloud platform so that the data can be processed locally and more accurately. Furthermore, we evaluate our approach against multivariate cumulative sum (MCUSUM) from state-of the-art in terms of different metric measures such as accuracy, precision, sensitivity, G-means and F-measure. Results have been evaluated based on real data set collected using accelerometer for a set of 9 distinct activities performed by 10 users for total period of 35 minutes with achieving high accuracy from 99.3% to 99.9% and F-measure up to 62.94%.

Is VATS suitable for lung diseases with hemoptysis? Experience from a hemoptysis treatment center in China

BackgroundSurgery is crucial in the treatment of the potentially fatal pulmonary hemoptysis condition. Currently, most patients with hemoptysis are treated by traditional open surgery (OS). To illustrate the effectiveness of video-assisted thoracic surgery (VATS) for hemoptysis, we developed a retrospective study of surgical interventions for lung disease with hemoptysis.MethodsWe collected and then analysed the data, including general information and post-operative outcomes, from 102 patients who underwent surgery for a variety of lung diseases with hemoptysis in our hospital between December 2018 and June 2022.ResultsSixty three cases underwent VATS and 39 cases underwent OS. 76.5% of patients were male (78/102). Comorbidities with diabetes and hypertension were 16.7% (17/102) and 15.7% (16/102) respectively. The diagnoses based on postoperative pathology included aspergilloma in 63 cases (61.8%), tuberculosis in 38 cases (37.4%) and bronchiectasis in 1 case (0.8%). 8 patients underwent wedge resection, 12 patients underwent segmentectomy, 73 patients underwent lobectomy and 9 patients underwent pneumonectomy. There were 23 cases of postoperative complications, of which 7 (30.4%) were in the VATS group, significantly fewer than 16 (69.6%) in the OS group (p = 0.001). The OS procedure was identified as the only independent risk factor for postoperative complications. The median (IQR) of postoperative drainage volume in the first 24 h was 400 (195–665) ml, which was 250 (130–500) ml of the VATS group, significantly less than the 550 (460–820) ml of the OS group (p < 0.05). The median (IQR) of pain scores 24 h after surgery was 5 (4–9). The median (IQR) of postoperative drainage tube removal time was 9.5 (6–17) days for all patients, and it was 7 (5–14) days for the VATS group, which was less than 15 (9–20) days for the OS group.ConclusionVATS for patients with lung disease presenting with hemoptysis is an effective and safe option that may be preferred when the hemoptysis is uncomplicated and the patient's vital signs are stable.

Combined pelvic and acetabular injuries: clinical features and treatment strategies of a unique injury pattern

BackgroundTo explore the clinical characteristics of patients with unstable pelvic fractures combined with acetabular fractures and to discuss the treatment strategies for such patients to help guide treatment.MethodsWe retrospectively assessed 24 patients admitted to our hospital from June 2018 to June 2022 with unstable pelvic fractures combined with acetabular fractures, including 15 male patients and 9 female patients with a mean age of 44.8 years. According to the Tile pelvic fracture classification, 15 cases were type B, and 9 cases were type C. The acetabular fractures were classified using the Letournel–Judet classification. There were 8 transverse fractures, 4 transverse and posterior wall fractures, 3 anterior and posterior hemitransverse fractures, 6 both-column fractures, 2 T-shaped fractures and 1 anterior column fracture. We recorded the cause of the patient's injury and vital signs on admission and assessed the patient's treatment strategy and prognosis.ResultsAll patients completed the surgery successfully, and the follow-up ranged from 6 to 42 months, with a mean of 23 months. The healing time for pelvic fractures ranged from 11 to 21 weeks, with a mean of 14.8 weeks, and the postoperative displacement of the posterior pelvic ring ranged from 1.2 to 9.0 mm, with a mean of 3.5 mm. The final clinical outcome at follow-up was evaluated using the Majeed scale: excellent in 11 cases, good in 10 cases and fair in 3 cases; the excellent rate was 87.5%. The time to healing of the acetabular fracture ranged from 13 to 25 weeks, with a mean of 15.9 weeks, and the postoperative displacement of the acetabular fracture ranged from 0.6 to 5.2 mm, with a mean of 1.9 mm. Hip function was assessed at the final follow-up using a modified Merle D’ Aubigné and Postel scale: there were 9 excellent, 11 good and 4 acceptable scores; an excellent rate of 83.3% was achieved.ConclusionPatients with unstable pelvic fractures combined with acetabular fractures suffer severe trauma and complex mechanisms of injury. Treatment needs to be individualized, taking into account the patient's physiological status, fracture classification and degree of displacement.

Treatment and Prevention of Postoperative Leakage after Gastrectomy for Gastric Cancer.

Anastomotic leakage is one of the common causes of serious morbidity and death after gastrectomy. The use of surgical treatment for leakage decreased due to the development of nonsurgical management. However, if nonsurgical management fails to control the spread of intra-abdominal infection, emergency surgical treatment is required. The authors wished to determine in which cases surgical treatment is needed for postoperative leakage and to identify treatment and prevention strategies. If a patient's vital signs are stable, local abscesses can be cured by conservative treatment after percutaneous drain insertion; if there is no improvement in anastomotic leakage, endoscopic treatment such as clipping, vacuum, and stent placement can be performed. If a patient's vital signs are unstable or patient shows diffuse peritonitis, surgical treatment should be performed. A surgical plan can be established according to leakage location. The duodenal stump may first require conservative treatment. It is recommended that surgical treatment be attempted first for anastomotic leakage of gastrojejunostomy site and gastric stump in remnant stomach. In conclusion, the need for surgical treatment is determined depending on vital signs and presence of diffuse peritonitis. During surgical treatment, a strategic approach is required according to the patient's condition and the anatomical location of leakage.

Association of Chest Anteroposterior Radiography with Computed Tomography in Patients with Blunt Chest Trauma.

In cases of chest trauma, computed tomography (CT) can be used alongside chest anteroposterior (AP) radiography and physical examination during initial evaluation. Performing a CT scan may be difficult if a patient has unstable vital signs. In contrast, radiography may not always reliably diagnose nonmarked pneumothorax or extensive subcutaneous emphysema. This study aimed to determine the agreement between chest radiography and CT findings in patients with blunt chest trauma. The study also aimed to determine the occurrence of occult pneumothorax and clarify the proportion of subcutaneous emphysema and pneumothorax detected through radiography and CT, respectively. We included patients (n = 1284) with chest trauma who were admitted to the emergency room of a tertiary hospital between January 2015 and June 2022. We excluded patients aged <18 years, those with stab injury, those without radiography and CT findings, and patients who required iatrogenic intervention, such as chest tube insertion, before imaging. We recorded age, sex, trauma mechanism, and Abbreviated Injury Scale score for each patient. From radiography and CT scans, we recorded the presence of rib fracture, subcutaneous emphysema, lung contusion, pneumothorax, and pneumomediastinum. The accuracy, sensitivity, specificity, and positive and negative predictive values were calculated to assess the reliability of radiography as a predictor of CT-based diagnosis. Radiography exhibited a specificity of nearly 100% for all items. In most cases, findings that could not be confirmed by CT were not evident on radiographs. The incidence of occult pneumothorax was 87.3%. When subcutaneous emphysema was observed on radiography, CT findings indicated pneumothorax in 96.7% of cases. In situations where the patient's vital signs are unstable and performing a CT scan is not feasible, the presence of subcutaneous emphysema on radiography may indicate the need for chest decompression, even if pneumothorax is not observed.

Effect of Combining Dhikr and Prayer Therapy on Pain and Vital Signs in Appendectomy Patients: A Quasi-Experimental Study.

Purpose: Dhikr and prayer reduce pain and improve a patient's vital signs. However, the interactions among these require further clarification in patients undergoing appendectomy. This study aimed to assess the effects of the combination of dhikr and prayer on pain, pulse rate, respiratory rate, and oxygen saturation. Study design: Quasi-experimental design. Method: Pain, pulse, respiratory rate, and oxygen saturation were measured via clinical examination immediately after leaving the recovery room at 1 and 2 hr after surgery in both the experimental and control groups. In total, 88 eligible participants were allocated to two groups: participants who received both dhikr and prayer (n = 44), and those who received routine care without analgesic therapy (n = 44). The chi-square test, independent t test, and general equation model were employed. Results: Respondents showed a significant interaction between group and time to decrease in pain, pulse, respiratory rate, and improved oxygen saturation, except for pain within 1 hr. The differences in all outcome scores between the groups after 1 and 2 hr were statistically significant, except for oxygen saturation after 1 hr. Conclusion: The combination of dhikr and prayer effectively decreased pain and improved vital signs. This helped nurses implement this procedure by promoting an essential culture of spiritual care for appendectomy patients.

The Application of Artificial Intelligence and Machine Learning to Anesthesiology

This research paper explores the application of artificial intelligence (AI) and machine learning (ML) in anesthesiology. AI and ML have the potential to improve patient outcomes and enhance clinical decision-making by enabling anesthesiologists to monitor patient vital signs in real-time, predict the likelihood of complications, and optimize drug dosages to minimize side effects and enhance efficacy. The Hypotension Prediction Index algorithm is a compelling example of how AI and ML can be utilized to improve intraoperative patient care. However, there is a need for further research and validation to ensure the safety and efficacy of these technologies in clinical practice. Future advancements in AI and ML techniques are likely to result in more sophisticated and accurate predictive models, decision support tools, and monitoring systems that will ultimately benefit patients undergoing anesthesia. Overall, the application of AI and ML in anesthesiology presents a promising avenue for improving patient care and outcomes.

P43 MUSIC THERAPY IN HAEMODYNAMICS: PRELIMINARY STUDY OF THE CHANGE IN VITAL PARAMETERS IN PATIENTS UNDERGOING INTERVENTIONAL PROCEDURES

Abstract Introduction Patients undergoing invasive procedures may have abnormal emotional reactivity that can lead to impaired vital signs. Music has been shown to modify physiological responses, but there is insufficient data in the literature to demonstrate the relationship between the improvement in vital signs and listening to music during interventional procedures. The aim of this study is to investigate how listening to music modifies vital signs in patients undergoing coronary angiography. Methods Patients admitted to the Campus Bio–Medico University Hospital of Rome for diagnostic coronary angiography were included in the study. During the procedures, patients listened to music selected by an experienced music therapist. The songs belonged to 6 musical genres and were also evaluated for agogic, tempo, and tonality. 7 phases were defined for the collection of vital signs (from the arrival in the waiting room to the stay in the "recovery room"). The results were compared with a homogeneous group of patients undergoing the same procedures. Results During phase 3 (insertion of the introducer), the mean systolic blood pressure was lower in the music therapy group than in the control group (139.9+–23.9 vs 145+–25.5 mmHg; p=0.529). During phase 2 (entering the room), mean oxygen saturation was higher in the music therapy group than in the control group (96 1.5% vs 95.4 1.8%; p=0.212). In all phases, the mean heart rate was lower in the music therapy group than in the control group (70.7+–10.8 vs 75+–18.1 bpm; p=0.33). The lowest mean systolic blood pressure and heart rate were recorded while listening to the Adagio and with 2/4 tempo tracks (mean systolic blood pressure 143,3+–26,9 mmHg; mean heart rate 69,7+–9,3 bpm). Conclusions This study was the first one to focus on changes in vital signs during music therapy in patients undergoing interventional cardiology procedures. Patients receiving music therapy showed a reduction in heart rate in all phases, a reduction in systolic blood pressure in phase 3, and an improvement in oxygen saturation in phase 2 compared with the control group. This approach may reduce the need for drug sedation and its side effects. The results are preliminary and require further analysis with a larger sample size.

Evaluation of electronic measurement of capillary refill for Sepsis screening at ED triage

ObjectiveTo evaluate the association between capillary refill time (CRT) measured by a medical device and sepsis among patients presenting to the Emergency Department (ED). MethodsThis prospective observational study enrolled adult and pediatric patients during ED triage when sepsis was considered a potential diagnosis by the triage nurse. Patients were enrolled at an academic medical center between December 2020 and June 2022. CRT was measured by a research assistant using an investigational medical device. The outcomes included sepsis and septic shock defined using sep-3 criteria, septic shock defined as IV antibiotics and a vasopressor requirement, ICU admission, and hospital mortality. Other measures included patient demographics and vital signs at ED triage. We evaluated univariate associations between CRT and sepsis outcomes. ResultsWe enrolled 563 patients in the study, 48 met Sep-3 criteria, 5 met Sep-3 shock criteria, and 11 met prior septic shock criteria (IV antibiotics and vasopressors to maintain mean arterial pressure of 65). Sixteen patients were admitted to the ICU. The mean age was 49.1 years, and 51% of the cohort was female. The device measured CRT was significantly associated with the diagnosis of sepsis by sep-3 criteria (OR 1.23, 95% CI 1.06–1-43), septic shock by sep-3 criteria (OR 1.57, 95% CI 1.02–2.40), and septic shock defined as receipt of IV antibiotics and a vasopressor requirement (OR 1.37, 95% CI 1.03–1.82). Patients with CRT >3.5 s measured by the DCR device had an odds ratio of 4.67 (95%CI 1.31–16.1) of septic shock (prior definition), and an odds ratio of 3.97 (95% CI 1.99–7.92) of ICU admission, supporting the potential for the 3.5-s cutoff of the DCR measurement. ConclusionsCRT measured by a medical device at ED triage was associated with the diagnosis of sepsis. Objective CRT measurement using a medical device may be a relatively simple way to improve sepsis diagnosis during ED triage.

Prediction of ICU Patients' Deterioration Using Machine Learning Techniques.

Assessing vital sign measurements within hospital settings presents a valuable opportunity for data analysis and knowledge extraction. By generating adaptable, personalized prediction models of patient vital signs, these models can yield clinically relevant insights not achievable through population-based models. This study aims to compare several statistical forecasting models to determine their real-life applicability. The primary objectives of this paper are to evaluate whether the following measurements: blood pressure, oxygen saturation, temperature and heart rate can predict deterioration in Intensive Care Unit (ICU) patients. Additionally, we aim to identify which of these measurements contributes most significantly to our prediction. Lastly, we seek to determine the most accurate data mining technique for real-life data applications. This retrospective chart review study utilized data from patients admitted to the ICU at a tertiary hospital between January and December, 2019. Data mining techniques for prediction included logistic regression, support vector machine classifier, k-nearest neighbors (KNN), gradient boosting classifier, and Naive Bayes classifier. A comprehensive comparison of these techniques was performed, focusing on accuracy, precision, recall, and F-measure. To achieve the research objectives, the SelectKBest class was applied to extract the most contributory features for prediction. Blood pressure ranked first with a score of 9.98, followed by respiratory rate, temperature, and heart rate. Analysis of 653 patient records indicated that 129 patients expired, while 542 patients were discharged either to their homes or other facilities. Among the five training models, two demonstrated the highest accuracy in predicting patient deterioration or survival at 88.83% and 84.72%, respectively. The gradient boosting classifier accurately predicted 115 out of 129 expired patients, while the KNN correctly predicted 109 out of 129 expired patients. Machine learning has the potential to enhance clinical deterioration prediction compared to traditional methods. This allows healthcare professionals to implement preventative measures and improve patients' quality of life, ultimately increasing average life expectancy. Although our research focused exclusively on ICU patients, data mining techniques can be applied in various contexts both within and outside the hospital setting.

Private Blockchain-Based AI-Envisioned Home Monitoring Framework in IoMT-Enabled COVID-19 Environment

In current situation, Coronavirus Disease-2019 (COVID-19) becomes a very serious health concern to the human life throughout the world. The Internet of Medical Things(IoMT) allows to deploy several wearable Internet of Things(IoT) enabled smart devices in a patient's body. The deployed smart devices should then securely communicate to nearby mobile devices installed in a smart home, which then securely communicate with the associated Fog server for information processing. The processed information in terms of transactions are formed as blocks and put into a private blockchain consisting of cloud servers. Since the patient's vital signs are very confidential and private, we apply the private blockchain. This article makes utilization of Fog computing and blockchain technology simultaneously to come up with more secure system in an IoMT-enabled COVID-19 situation for patients' home monitoring purpose. We first discuss various phases related to development of a new Fog-based private blockchain-enabled home monitoring framework. Next, we discuss how Artificial Intelligence(AI)-enabled Big data analytics helps in analyzing and tracking the patients' information related to COVID-19 cases. Finally, a blockchain implementation has been performed to exhibit practical demonstration of the proposed blockchain system.

Machine Learning Prediction for Supplemental Oxygen Requirement in Patients with COVID-19

Introduction:The coronavirus disease (COVID-19) poses an urgent threat to global public health and is characterized by rapid disease progression even in mild cases. In this study, we investigated whether machine learning can be used to predict which patients will have a deteriorated condition and require oxygenation in asymptomatic or mild cases of COVID-19.Method:This single-center, retrospective, observational study included COVID-19 patients admitted to the hospital from February 1, 2020, to May 31, 2020, and who were either asymptomatic or presented with mild symptoms and did not require oxygen support on admission. Data on patient characteristics and vital signs were collected upon admission. We used seven machine learning algorithms, assessed their capability to predict exacerbation, and analyzed important influencing features using the best algorithm.Results:In total, 210 patients were included in the study. Among them, 43 (19%) required oxygen therapy. Of all the models, the logistic regression model had the highest accuracy and precision. Logistic regression analysis showed that the model had an accuracy of 0.900, precision of 0.893, and recall of 0.605. The most important parameter for predictive capability was SpO2, followed by age, respiratory rate, and systolic blood pressure.Conclusion:In this study, we developed a machine learning model that can be used as a triage tool by clinicians to detect high-risk patients and disease progression earlier. Prospective validation studies are needed to verify the application of the tool in clinical practice.

A Safety Goggles for Dental Treatment That Can Monitor the Pulse

Objective: Using the safety goggles to protect and treat the process of fog, powder or laser injury to the eyes, reduce noise and thus reduce the patient's discomfort. At the same time, pulse frequency and vital signs of patients can be detected during treatment to improve the safety of treatment. Methods: The device is a safety goggles for dental treatment that can monitor the pulse, including a mirror frame, a protective lens mounted on one side of the frame and a first elastic band and a second elastic band located at both ends of the frame. The first elastic band and the second elastic band are connected and fixed together. The first elastic band is provided with a holding box body near the frame, and the top of the holding box body is provided with a power switch, an audio switch and a pulse monitoring switch. The body of the containing box is provided with a control chip, a power supply component, a pulse sensor, a memory and an audio component. The control chip is electrically connected with the memory, a pulse sensor, an audio component, a speaker and a power supply component, and the audio component is electrically connected with the first earplug and the second earplug through the data cable. Results: A kind of safety goggles for dental treatment which can monitor pulse was successfully designed. The device is equipped with safety goggles, so as to prevent the air fog and other dirt damage to the eyes; Pulse sensor was set to obtain pulse signal; Earplugs with extendable length are suitable for individual differences and can improve the sound insulation effect. Conclusion: The safety goggles can solve the problems of harsh light source equipped with dental treatment chair, easy to splash the eyes with aerosol during operation, and loud noise of operating instruments. In addition, it can also monitor patients' vital signs in real time during tooth implantation and extraction and prevent sudden symptoms.

2 A CTS Team Approach to Topological Data Analysis of Electronic Health Records for Subtyping and Clinical Outcomes Prediction in Patients with COVID-19

OBJECTIVES/GOALS: Analysis and modeling of large, complex clinical data remain challenging despite modern advances in biomedical informatics. We aim to explore the potential of topological data analysis (TDA) to address such challenges in the context of COVID-19 outcomes using electronic health records (EHRs). METHODS/STUDY POPULATION: In this work, we develop TDA approaches to characterize subtypes and predict outcomes in patients with COVID-19 infection. First, data for &gt;70,000 COVID-19 patients were extracted from the OneFlorida EHR database. Next, enhancements to the TDA algorithm Mapper were designed and implemented to adapt the technique to this type of data. Clinical variables, including patient demographics, vital signs, and lab values, were then used as input to conduct a population-level exploratory analysis with an emphasis on identifying phenotypic subtypes at increased risk of adverse outcomes such as major adverse cardiovascular events (MACE), mechanical ventilation, and death. RESULTS/ANTICIPATED RESULTS: Preliminary Mapper experiments have produced visual representations of the COVID-19 patient population that are well-suited to exploratory analysis. Such visualizations facilitate easy identification of phenotypic subnetworks that differ from the general population in terms of baseline variables or clinical outcomes. In this and subsequent work, we aim to fully characterize and quantify differences between these subnetworks to identify factors that may confer increased risk (or protection from) adverse outcomes. We also plan to validate and rigorously compare the efficacy of this TDA-based approach to common alternatives such as clustering, principal component analysis, and machine learning. DISCUSSION/SIGNIFICANCE: This work demonstrates the potential utility of TDA for the characterization of complex biomedical data. Mapper provides a novel means of exploring EHR data, which are otherwise difficult to visualize and can aid in identifying or characterizing patient subtypes in diseases such as COVID-19.

The effect of TAP block use in postoperative analgesic in cesarean section.

TAP (transversus abdominis plane) block is an important parameter of multimodal analgesia in the control of postoperative pain in cesarean section cases. In our study, we aimed to compare the analgesic consumption, patient satisfaction rate, vital signs, and visual analog scale (VAS) scores of ASA II patients with and without TAP block in cesarean surgery. This study was designed as a retrospective review of prospectively collected data and an open-label and randomized clinical trial. The files of 180 patients who underwent elementary cesarean section between January 2019 and December 2019 were analyzed. The ASA score, anesthesia method, age, weight, height, parity, TAP block application, VAS score, analgesia duration, the additional analgesic requirement for maintenance, patient satisfaction, postoperative nausea, vomiting, urinary retention, and other complications were recorded. The 180 patients included in the study were divided into 6 groups: Group 1 - General anesthesia, Group 2 - General anesthesia + TAP block, Group 3 - Spinal anesthesia, Group 4 - Spinal anesthesia + TAP block, Group 5 - Epidural anesthesia, and Group 6 - Epidural anesthesia + TAP block. There was no significant difference between the groups in terms of demographic variables. The VAS scores of the first 24 hours were significantly different for Group 1. VAS scores in the 1st and 3rd hours were significantly higher in Group 1 than in the other groups. The groups without TAP block had significantly higher VAS scores at the 12th hour. Furthermore, the VAS score in Group 6 at 24 hours was significantly the lowest, and the earliest analgesic requirement was in Group 1. When the number of analgesic needs of the patients in 24 hours was examined, Group 1 was found to be significantly the highest, and Group 6 was significantly the lowest of all groups. The epidural anesthesia + TAP block Group had the lowest VAS score, the fewest analgesic requirements, the longest analgesia length, and the highest patient satisfaction.

Nursing Assessment of Cardiovascular System: Importance of History Taking and Physical Examination

Assessment of a patient's cardiovascular system is crucial in identifying potential problems, determining a diagnosis, and creating a care plan. The nursing assessment process involves both history taking and physical examination. History taking is important in obtaining information about the patient's past medical history, risk factors for cardiovascular disease, symptoms, and medication use. This information can help to identify potential problems, such as high blood pressure or heart disease, and provide a baseline for comparison during subsequent assessments. Physical examination includes evaluating the patient's vital signs, such as blood pressure, heart rate, and rhythm, and listening to the heart and lungs for sounds that may indicate problems, such as murmurs or crackles. The examination may also involve palpating for peripheral pulses, evaluating for edema, and assessing skin color and temperature. Together, the information gathered during history taking and physical examination forms a comprehensive picture of the patient's cardiovascular system and provides the foundation for an effective nursing care plan.

The Impact of Virtual Reality on the Patients and Providers Experience in Wide-Awake, Local-Only Hand Surgery

Virtual reality (VR) is an emerging technology with the potential to enhance patient care by reducing pain and anxiety for a variety of medical procedures. The aim of this study was to evaluate an immersive VR program as a nonpharmacologic intervention to reduce anxiety and increase satisfaction in patients undergoing wide-awake, local-only hand surgery. The secondary aim was to assess providers' experience with the program. An implementation evaluation was employed to assess the experience of 22 patients who used VR during outpatient, wide-awake hand surgery at a veterans affairs hospital. We assessed the patients' anxiety scores and vital signs before and after the procedure as well as postprocedural satisfaction measures. The providers' experience was also assessed. Patients who used VR exhibited lower anxiety scores after the procedure compared with what they exhibited before the procedure and had high satisfaction levels with their VR experience. Surgeons who used the system reported that VR improved their ability to teach learners and better focus on the procedure. Virtual reality, as a nonpharmacologic intervention, reduced anxiety and contributed to the patients' perioperative satisfaction with wide-awake, local-only hand surgery. As a secondary finding, VR positively impacted the providers' experience by increasing their ability to concentrate on tasks during the surgery. Virtual reality represents a novel technology that can reduce anxiety and contribute to a positive experience for both patients and providers during wide-awake, local-only hand procedures.

The association of clinically relevant variables with chest radiograph lung disease burden quantified in real-time by radiologists upon initial presentation in individuals hospitalized with COVID-19

ObjectivesWe aimed to correlate lung disease burden on presentation chest radiographs (CXR), quantified at the time of study interpretation, with clinical presentation in patients hospitalized with coronavirus disease 2019 (COVID-19). Material and methodsThis retrospective cross-sectional study included 5833 consecutive adult patients, aged 18 and older, hospitalized with a diagnosis of COVID-19 with a CXR quantified in real-time while hospitalized in 1 of 12 acute care hospitals across a multihospital integrated healthcare network between March 24, 2020, and May 22, 2020. Lung disease burden was quantified in real-time by 118 radiologists on 5833 CXR at the time of exam interpretation with each lung annotated by the degree of lung opacity as clear (0%), mild (1–33%), moderate (34–66%), or severe (67–100%). CXR findings were classified as (1) clear versus disease, (2) unilateral versus bilateral, (3) symmetric versus asymmetric, or (4) not severe versus severe. Lung disease burden was characterized on initial presentation by patient demographics, co-morbidities, vital signs, and lab results with chi-square used for univariate analysis and logistic regression for multivariable analysis. ResultsPatients with severe lung disease were more likely to have oxygen impairment, an elevated respiratory rate, low albumin, high lactate dehydrogenase, and high ferritin compared to non-severe lung disease. A lack of opacities in COVID-19 was associated with a low estimated glomerular filtration rate, hypernatremia, and hypoglycemia. ConclusionsCOVID-19 lung disease burden quantified in real-time on presentation CXR was characterized by demographics, comorbidities, emergency severity index, Charlson Comorbidity Index, vital signs, and lab results on 5833 patients. This novel approach to real-time quantified chest radiograph lung disease burden by radiologists needs further research to understand how this information can be incorporated to improve clinical care for pulmonary-related diseases.. An absence of opacities in COVID-19 may be associated with poor oral intake and a prerenal state as evidenced by the association of clear CXRs with a low eGFR, hypernatremia, and hypoglycemia.