Background A non-invasive method of measuring cardiac output (CO) can be beneficial in the care of critically ill patients. HemoVista (BiLab Co., Ltd.) is a medical device that measures CO non-invasively using multi-channel impedance plethysmography technology. The purpose of this study was to exploratively evaluate the performance of HemoVista in critically ill patients undergoing CO monitoring with the FloTrac (Edwards Lifesciences).
Methods After non-invasively installing the HemoVista sensor in critically ill patients whose CO was monitored with the FloTrac, CO values measured by both devices were collected for 30 minutes. Cardiac output measured by both devices was selected every 10 seconds, creating approximately 360 data pairs per patient. Linear correlation analysis with Pearson correlation coefficients, Bland-Altman analysis, and four-quadrant plot analysis were performed to evaluate the performance of HemoVista.
Results A total of 7,138 pairs of CO data from the 20 patients were included in the analysis. A significant correlation was observed between the two methods of measuring CO (Pearson's r=0.489, P<0.001). The mean bias was 1.03 L/min, the 95% CI for the limit of agreement was –1.83 L/min to 3.93 L/min and the percentage error was 55.8%. The concordance rate of time-dependent CO between the two devices was 14.6%.
Conclusions It was observed that the current version of HemoVista has unsuitable performance for use in intensive care units. To be used for critically ill patients, the algorithm must be improved and reevaluated with an enhanced version.
Background Identifying critically ill patients at risk of cardiac arrest is important because it offers the opportunity for early intervention and increased survival. The aim of this study was to develop a deep learning model to predict critical events, such as cardiopulmonary resuscitation or mortality.
Methods This retrospective observational study was conducted at a tertiary university hospital. All patients younger than 18 years who were admitted to the pediatric intensive care unit from January 2010 to May 2023 were included. The main outcome was prediction performance of the deep learning model at forecasting critical events. Long short-term memory was used as a deep learning algorithm. The five-fold cross validation method was employed for model learning and testing.
Results Among the vital sign measurements collected during the study period, 11,660 measurements were used to develop the model after preprocessing; 1,060 of these data points were measurements that corresponded to critical events. The prediction performance of the model was the area under the receiver operating characteristic curve (95% confidence interval) of 0.988 (0.9751.000), and the area under the precision-recall curve was 0.862 (0.700–1.000).
Conclusions The performance of the developed model at predicting critical events was excellent. However, follow-up research is needed for external validation.
Background Various rapid response systems have been developed to detect clinical deterioration in patients. Few studies have evaluated single-parameter systems in children compared to scoring systems. Therefore, in this study we evaluated a single-parameter system called the acute response system (ARS).
Methods This retrospective study was performed at a tertiary children’s hospital. Patients under 18 years old admitted from January 2012 to August 2023 were enrolled. ARS parameters such as systolic blood pressure, heart rate, respiratory rate, oxygen saturation, and whether the ARS was activated were collected. We divided patients into two groups according to activation status and then compared the occurrence of critical events (cardiopulmonary resuscitation or unexpected intensive care unit admission). We evaluated the ability of ARS to predict critical events and calculated compliance. We also analyzed the correlation between each parameter that activates ARS and critical events.
Results The critical events prediction performance of ARS has a specificity of 98.5%, a sensitivity of 24.0%, a negative predictive value of 99.6%, and a positive predictive value of 8.1%. The compliance rate was 15.6%. Statistically significant increases in the risk of critical events were observed for all abnormal criteria except low heart rate. There was no significant difference in the incidence of critical events.
Conclusions ARS, a single parameter system, had good specificity and negative predictive value for predicting critical events; however, sensitivity and positive predictive value were not good, and medical staff compliance was poor.
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Background
Supraventricular tachycardia (SVT) is a common arrhythmia with associated symptoms such as palpitation, dizziness, and fatigue. It significantly affects patients’ quality of life (QoL). Radiofrequency cardiac ablation (RFCA) is a highly effective treatment to eliminate arrhythmia and improve patients’ QoL. The purpose of this study was to assess the level of QoL among patients with SVT and examine the difference in QoL before and after RFCA.
Methods One group pre-posttest design with a convenience sample of 112 patients was used. QoL was assessed by 36-Item Short Form (SF-36). Data were collected at admission through face-to-face interviews and 1-month post-discharge through phone interviews.
Results There was a significant difference between QoL before (33.7±17.0) and 1 month after (62.5±18.5) the RFCA. Post-RFCA patients diagnosed with atrioventricular nodal reentrant tachycardia had higher QoL than other types of SVT. Moreover, there were significant negative relationships between QoL and the number and duration of episodes pre- and post-RFCA. There were no significant differences in QoL based on: age, sex, working status, marital status, smoking, coronary artery disease, diabetes mellitus, and hypertension.
Conclusions After RFCA, the QoL of patients with ST improved for both physical and mental component subscales.
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Background There are conflicting results regarding the association between body mass index and the prognosis of cardiac arrest patients. We investigated the association of the composition and distribution of muscle and fat with neurologic outcomes at hospital discharge in successfully resuscitated out-of-hospital cardiac arrest (OHCA) patients.
Methods This prospective, single-centre, observational study involved adult OHCA patients, conducted between April 2019 and June 2021. The ratio of total skeletal muscle, upper limb muscle, lower limb muscle, and total fat to body weight was measured using InBody S10, a bioimpedance analyser, after achieving the return of spontaneous circulation. Restricted cubic spline curves with four knots were used to examine the relationship between total skeletal muscle, upper limb muscle, and lower limb muscle relative to total body weight and neurologic outcome at discharge. Multivariable logistic regression analysis was performed to assess an independent association.
Results A total of 66 patients were enrolled in the study. The proportion of total muscle and lower limb muscle positively correlated with the possibility of having a good neurologic outcome. The proportion of lower limb muscle showed an independent association in the multivariable analysis (adjusted odds ratio, 2.29; 95% confidence interval, 1.06–13.98), and its optimal cut-off value calculated through receiver operating characteristic curve analysis was 23.1%, which can predict a good neurological outcome.
Conclusions A higher proportion of lower limb muscle to body weight was independently associated with the probability of having a good neurologic outcome in OHCA patients.
Yunseob Shin, Kyung-Jae Cho, Yeha Lee, Yu Hyeon Choi, Jae Hwa Jung, Soo Yeon Kim, Yeo Hyang Kim, Young A Kim, Joongbum Cho, Seong Jong Park, Won Kyoung Jhang
Acute Crit Care. 2022;37(4):654-666. Published online October 26, 2022
Background Early recognition of deterioration events is crucial to improve clinical outcomes. For this purpose, we developed a deep-learning-based pediatric early-warning system (pDEWS) and aimed to validate its clinical performance.
Methods This is a retrospective multicenter cohort study including five tertiary-care academic children’s hospitals. All pediatric patients younger than 19 years admitted to the general ward from January 2019 to December 2019 were included. Using patient electronic medical records, we evaluated the clinical performance of the pDEWS for identifying deterioration events defined as in-hospital cardiac arrest (IHCA) and unexpected general ward-to-pediatric intensive care unit transfer (UIT) within 24 hours before event occurrence. We also compared pDEWS performance to those of the modified pediatric early-warning score (PEWS) and prediction models using logistic regression (LR) and random forest (RF).
Results The study population consisted of 28,758 patients with 34 cases of IHCA and 291 cases of UIT. pDEWS showed better performance for predicting deterioration events with a larger area under the receiver operating characteristic curve, fewer false alarms, a lower mean alarm count per day, and a smaller number of cases needed to examine than the modified PEWS, LR, or RF models regardless of site, event occurrence time, age group, or sex.
Conclusions The pDEWS outperformed modified PEWS, LR, and RF models for early and accurate prediction of deterioration events regardless of clinical situation. This study demonstrated the potential of pDEWS as an efficient screening tool for efferent operation of rapid response teams.
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Background The aim of this study was to evaluate the hemodynamic protective effects of perioperative ventilation in pressure-controlled ventilation (PCV) and adaptive support ventilation (ASV) modes based on non-invasive hemodynamic monitoring indicators.
Methods The study included 32 patients who were scheduled for planned open abdominal surgery. Depending on the chosen ventilation strategy, patients were included in two groups of PCV mode ventilation (n=14) and ASV mode ventilation (n=18). The hemodynamic effects of the ventilation strategies were assessed by estimated continuous cardiac output (esCCO) and cardiac index (esCCI).
Results Preoperative cardiac output (CO) was 6.1±1.3 L/min in group 1 patients and 6.3±0.8 L/min in group 2 patients, and preoperative cardiac index (CI) was 3.9±0.4 L/min/m2 in group 1 patients and 3.8±0.8 L/min/m2 in group 2 patients. The ejection fraction (EF) in group 1 subjects was 55.4%±0.3%; this rate was 56.5%±0.5% in group 2 subjects. Group 1 patients experienced a 14.7% CO decrease to 5.2±0.7 L/min, a 17.9% CI decrease to 3.2±0.6 L/min/m2 , and a 12.8% mean arterial pressure decrease to 82.3±9.4 mm Hg 30 minutes after the start of surgery. One hour after the start of surgery, the CO mean values of group 2 patients were lower than baseline by 7.9% and differed from the dynamics of patients in group 1, in whom CO was lower than baseline by 13.1%. At the end of the operation, the CO values were lower than baseline by 11.5% and 6.3% in patients of groups 1 and 2, respectively. Our data showed that the changes in EF during and after surgery correlated with CO indicators determined by the esCCO.
Conclusions In our study, perioperative ventilation in ASV mode was more protective than PCV mode and was characterized by lower tidal volume (16.2%) and driving pressure (12.1%). Hemodynamically-controlled mechanical ventilation reduces the negative impact of cardiopulmonary interactions,
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Background The inflammatory response that occurs following cardiac arrest can determine the long-term prognosis of patients who survive out-of-hospital cardiac arrest. We evaluated the correlation between C-reactive protein-to-albumin ratio (CAR) following cardiac arrest and long-term mortality.
Methods The current retrospective observational study examined patients with post-cardiac arrest syndrome (PCAS) treated with targeted temperature management at a single tertiary care hospital. We measured CAR at four time points (at admission and then 24 hours, 48 hours, and 72 hours after) following cardiac arrest. The primary outcome was the patients’ 6-month mortality. We performed multivariable and area under the receiver operating characteristic curve (AUC) analyses to investigate the relationship between CAR and 6-month mortality.
Results Among the 115 patients, 52 (44.1%) died within 6 months. In the multivariable analysis, CAR at 48 hours (odds ratio [OR], 1.130; 95% confidence interval [CI], 1.027–1.244) and 72 hours (OR, 1.241; 95% CI, 1.059–1.455) after cardiac arrest was independently associated with 6-month mortality. The AUCs of CAR at admission and 24, 48, and 72 hours after cardiac arrest for predicting 6-month mortality were 0.583 (95% CI, 0.489–0.673), 0.622 (95% CI, 0.528–0.710), 0.706 (95% CI, 0.615–0.786), and 0.762 (95% CI, 0.675–0.835), respectively.
Conclusions CAR at 72 hours after cardiac arrest was an independent predictor for long-term mortality in patients with PCAS.
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Methods A retrospective study was conducted at a tertiary care hospital in Pakistan between January 2014 and December 2018. All pediatric (<18 years old) patients admitted to the intensive care unit and undergoing continuous electrocardiographic monitoring after surgery for CHD were included in this study. Data pertaining to the incidence, diagnosis, and management of postoperative arrhythmias were collected.
Results Amongst 812 children who underwent surgery for CHD, 185 (22.8%) developed arrhythmias. Junctional ectopic tachycardia (JET) was the most common arrhythmia, observed in 120 patients (64.9%), followed by complete heart block (CHB) in 33 patients (17.8%). The highest incidence of early postoperative arrhythmia was seen in patients with atrioventricular septal defects (64.3%) and transposition of the great arteries (36.4%). Patients were managed according to the Pediatric Advanced Life Support guidelines. JET resolved successfully within 24 hours in 92% of patients, while 16 (48%) patients with CHB required a permanent pacemaker.
Conclusions More than one in five pediatric patients suffered from early postoperative arrhythmias in our setting. Further research exploring predictive factors and the development of better management protocols of patients with CHB are essential for reducing the morbidity and mortality associated with postoperative arrhythmia.
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Post-cardiac arrest syndrome is a complex and critical issue in resuscitated patients undergone cardiac arrest. Ischemic-reperfusion injury occurs in multiple organs due to the return of spontaneous circulation. Bundle of management practicies are required for post-cardiac arrest care. Early invasive coronary angiography should be considered to identify and treat coronary artery obstructive disease. Vasopressors such as norepinephrine and dobutamine are the first-line treatment for shock. Maintainance of oxyhemoglobin saturation greater than 94% but less than 100% is recommended to avoid fatality. Target temperature therapeutic hypothermia helps to resuscitated patients. Strict temperature control is required and is maintained with the help of cooling devices and monitoring the core temperature. Montorings include electrocardiogram, oxymetry, capnography, and electroencephalography (EEG) along with blood pressue, temprature, and vital signs. Seizure should be treated if EEG shows evidence of seizure or epileptiform activity. Clinical neurologic examination and magnetic resonance imaging are considered to predict neurological outcome. Glycemic control and metabolic management are favorable for a good neurological outcome. Recovery from acute kidney injury is essential for survival and a good neurological outcome.
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