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Abstract Accurate assessment of pediatric vital signs is critical for detecting abnormalities and guiding medical interventions, but interpretation is challenging due to age-dependent physiological variations. Therefore, this study aimed to develop age-specific centile curves for blood pressure, heart rate, and respiratory rate in pediatric patients and create a user-friendly web-based application for easy access to these data. We conducted a retrospective cross-sectional observational study analyzing 3,779,482 records from the National Emergency Department Information System of Korea, focusing on patients under 15 years old admitted between January 2016 and December 2017. After applying exclusion criteria to minimize the impact of patients’ symptoms on vital signs, 1,369,608 records were used for final analysis. The box–cox power exponential distribution and Lambda–Mu–Sigma (LMS) method were used to generate blood pressure centile charts, while heart rate and respiratory rate values were drawn from previously collected LMS values. We developed comprehensive age-specific centile curves for systolic, diastolic, and mean blood pressure, heart rate, and respiratory rate. These were integrated into a web-based application ( http://centile.research.or.kr ), allowing users to input patient data and promptly obtain centile and z-score information for vital signs. Our study provides an accessible system for pediatric vital sign evaluation, addressing previous limitations and offering a practical solution for clinical assessment. Future research should validate these centile curves in diverse populations.

OBJECTIVES:. Anemia has been associated with an increased risk of both cardiac arrest and stroke, frequent complications of COVID-19. The effect of hemoglobin level at ICU admission on a composite outcome of cardiac arrest or stroke in an international cohort of COVID-19 patients was investigated. DESIGN:. Retrospective analysis of prospectively collected database. SETTING:. A registry of COVID-19 patients admitted to ICUs at over 370 international sites was reviewed for patients diagnosed with cardiac arrest or stroke up to 30 days after ICU admission. Anemia was defined as: normal (hemoglobin ≥ 12.0 g/dL for women, ≥ 13.5 g/dL for men), mild (hemoglobin 10.0–11.9 g/dL for women, 10.0–13.4 g/dL for men), moderate (hemoglobin ≥ 8.0 and < 10.0 g/dL for women and men), and severe (hemoglobin < 8.0 g/dL for women and men). PATIENTS:. Patients older than 18 years with acute COVID-19 infection in the ICU. INTERVENTIONS:. None. MEASUREMENTS AND MAIN RESULTS:. Of 6926 patients (median age = 59 yr, male = 65%), 760 patients (11.0%) experienced stroke (2.0%) and/or cardiac arrest (9.4%). Cardiac arrest or stroke was more common in patients with low hemoglobin, occurring in 12.8% of patients with normal hemoglobin, 13.3% of patients with mild anemia, and 16.7% of patients with moderate/severe anemia. Time to stroke or cardiac arrest by anemia status was analyzed using Cox proportional hazards regression with death as a competing risk. Covariates selected through clinical knowledge were age, sex, comorbidities (diabetes, hypertension, obesity, and cardiac or neurologic conditions), pandemic era, country income, mechanical ventilation, and extracorporeal membrane oxygenation. Moderate/severe anemia was associated with a higher risk of cardiac arrest or stroke (hazard ratio, 1.32; 95% CI, 1.05–1.67). CONCLUSIONS:. In an international registry of ICU patients with COVID-19, moderate/severe anemia was associated with increased hazard of cardiac arrest or stroke.

Cardiac tamponade (CT) is a rare but potentially lethal complication associated with central venous catheters (CVCs). We present a 6-year-old boy who developed CT after CVC insertion in the right internal jugular vein, with its tip in the inferior vena cava. Initially, he was hospitalized to a tertiary hospital with presumptive diagnoses of pneumonia and left ankle cellulitis. Three days after CVC placement, he developed CT, manifesting as recurrent episodes of hypotension and bradycardia. Once a bedside echocardiography showed CT, pericardiocentesis was performed, resulting in successful resuscitation. Pericardiocentesis revealed a milk-appearing pericardial fluid, indicating chylopericardium. The boy also underwent debridement and joint irrigation of the left ankle, which turned out to be osteomyelitis. Although CVC tips in most reported CVC-related CTs have been commonly located in the right atrium, our case featured a tip located in the inferior vena cava. Additionally, chylopericardium was likely due to the extravasation of total parenteral nutrition fluid, rather than the usual causes, such as recent thoracic surgery. Understanding the mechanisms behind chylopericardium associated with CVCs and timely pericardiocentesis is crucial for improving the outcomes.

Bronchopulmonary dysplasia (BPD) and pulmonary hypertension (PH) are potentially fatal complications in prematurely born infants. Extracorporeal membrane oxygenation (ECMO) may be a life-saving option for managing infants with BPD and PH. We present 2 patients who were successfully weaned off mechanical ventilators (MVs) through the application of ECMO. The patients were transferred to our institution after receiving MV care for 8 and 10 months, respectively, for BPD and PH. We were able to remove the patients from MVs after a period of ECMO-mediated lung rest. Although more research is required to determine specific criteria for ECMO use in patients with BPD and PH, our clinical experiences may contribute to the early application of ECMO in MV-dependent patients.

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 (0.975–1.000), 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.

Abstract Early detection of deteriorating patients is important to prevent life-threatening events and improve clinical outcomes. Efforts have been made to detect or prevent major events such as cardiopulmonary resuscitation, but previously developed tools are often complicated and time-consuming, rendering them impractical. To overcome this problem, we designed this study to create a deep learning prediction model that predicts critical events with simplified variables. This retrospective observational study included patients under the age of 18 who were admitted to the general ward of a tertiary children’s hospital between 2020 and 2022. A critical event was defined as cardiopulmonary resuscitation, unplanned transfer to the intensive care unit, or mortality. The vital signs measured during hospitalization, their measurement intervals, sex, and age were used to train a critical event prediction model. Age-specific z-scores were used to normalize the variability of the normal range by age. The entire dataset was classified into a training dataset and a test dataset at an 8:2 ratio, and model learning and testing were performed on each dataset. The predictive performance of the developed model showed excellent results, with an area under the receiver operating characteristics curve of 0.986 and an area under the precision-recall curve of 0.896. We developed a deep learning model with outstanding predictive power using simplified variables to effectively predict critical events while reducing the workload of medical staff. Nevertheless, because this was a single-center trial, no external validation was carried out, prompting further investigation.

Abstract Background Measuring arterial partial pressure of carbon dioxide (PaCO2) is crucial for proper mechanical ventilation, but the current sampling method is invasive. End-tidal carbon dioxide (EtCO2) has been used as a surrogate, which can be measured non-invasively, but its limited accuracy is due to ventilation-perfusion mismatch. This study aimed to develop a non-invasive PaCO2 estimation model using machine learning. Methods This retrospective observational study included pediatric patients (

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.

Laundry detergent pod (LDP) exposure has been reported to be fatal in children younger than 2 years, leading to respiratory or central nervous system depression. While gastrointestinal irritation is the most common symptom, there are reported cases of severe acidosis with respiratory depression or pneumonia, resulting in mortality. To our best knowledge, there is no report on a case of LDP exposure presenting with acute respiratory distress syndrome requiring extracorporeal membrane oxygenation support. Here, we present a case of a child with severe acute respiratory distress syndrome following LDP exposure, who was successfully treated with veno-pulmonary extracorporeal membrane oxygenation and steroids.

Objective To analyze the clinical significance of a heart rate (HR) or respiratory rate (RR) higher or lower than the normal in pediatric triage. Methods A retrospective observational study was conducted with data from the Korean National Emergency Department Information System. The subjects were children normal), grade 2 (2 SDs>normal), and grade 3 (3 or more SDs>normal). The main outcomes were hospitalization and intensive care unit (ICU) admission rates. Logistic regression analysis was used to analyze the relationship of the outcomes according to grade in each group. Results Data for 981,297 patients were analyzed. Hospitalization and ICU admission rates increased significantly in the higher HR group (grades 1 to 3; odds ratio [OR], 1.353; P

Background Pediatric Index of Mortality 3 (PIM 3) and Pediatric Logistic Organ Dysfunction-2 (PELOD-2) are validated tools for predicting mortality in children. Research suggests that these tools may have different predictive performance depending on patient group characteristics. Therefore, we designed this study to identify the factors that make the mortality rates predicted by the tools different. Methods This retrospective study included patients (

Objective Urinary tract infection (UTI) is a significant issue in young febrile patients due to potential long-term complications. Early detection of UTI is crucial in pediatric emergency departments (PEDs). We developed a tool to predict UTIs in children. Methods Clinical data of patients

Abstract The aim of this study was to develop a predictive model of pediatric mortality in the early stages of intensive care unit (ICU) admission using machine learning. Patients less than 18 years old who were admitted to ICUs at four tertiary referral hospitals were enrolled. Three hospitals were designated as the derivation cohort for machine learning model development and internal validation, and the other hospital was designated as the validation cohort for external validation. We developed a random forest (RF) model that predicts pediatric mortality within 72 h of ICU admission, evaluated its performance, and compared it with the Pediatric Index of Mortality 3 (PIM 3). The area under the receiver operating characteristic curve (AUROC) of RF model was 0.942 (95% confidence interval [CI] = 0.912–0.972) in the derivation cohort and 0.906 (95% CI = 0.900–0.912) in the validation cohort. In contrast, the AUROC of PIM 3 was 0.892 (95% CI = 0.878–0.906) in the derivation cohort and 0.845 (95% CI = 0.817–0.873) in the validation cohort. The RF model in our study showed improved predictive performance in terms of both internal and external validation and was superior even when compared to PIM 3.

Objectives Triage is an essential emergency department (ED) process designed to provide timely management depending on acuity and severity; however, the process may be inconsistent with clinical and hospitalization outcomes. Therefore, studies have attempted to augment this process with machine learning models, showing advantages in predicting critical conditions and hospitalization outcomes. The aim of this study was to utilize nationwide registry data to develop a machine learning-based classification model to predict the clinical course of pediatric ED visits. Methods This cross-sectional observational study used data from the National Emergency Department Information System on emergency visits of children under 15 years of age from January 1, 2016, to December 31, 2017. The primary and secondary outcomes were to identify critically ill children and predict hospitalization from triage data, respectively. We developed and tested a random forest model with the under sampled dataset and validated the model using the entire dataset. We compared the model’s performance with that of the conventional triage system. Results A total of 2,621,710 children were eligible for the analysis and included 12,951 (0.5%) critical outcomes and 303,808 (11.6%) hospitalizations. After validation, the area under the receiver operating characteristic curve was 0.991 (95% confidence interval [CI] 0.991–0.992) for critical outcomes and 0.943 (95% CI 0.943–0.944) for hospitalization, which were higher than those of the conventional triage system. Conclusions The machine learning-based model using structured triage data from a nationwide database can effectively predict critical illness and hospitalizations among children visiting the ED.

Serious bacterial infection (SBI) in children, such as bacterial meningitis or sepsis, is an important condition that can lead to fatal outcomes. Therefore, since it is very important to accurately diagnose SBI, SBI prediction tools such as 'Refined Lab-score' or 'clinical prediction rule' have been developed and used. However, these tools can predict SBI only when there are values of all factors used in the tool, and if even one of them is missing, the tools become useless. Therefore, the purpose of this study was to develop and validate a machine learning-driven model to predict SBIs among febrile children, even with missing values. This was a multicenter retrospective observational study including febrile children

Heart rate and respiratory rate display circadian variation. Pediatric single-parameter rapid response system is activated when heart rate or respiratory rate deviate from age-specific criteria, though activation criteria do not differentiate between daytime and nighttime, and unnecessary activation has been reported due to nighttime bradycardia. We evaluated the relationship between rapid response system activation and the patient’s clinical outcome by separately applying the criteria to daytime and nighttime in patients < 18. The observation period was divided into daytime and nighttime (8:00–20:00, and 20:00 to 8:00), according to which measured heart rate and respiratory rate were divided and rapid response system activation criteria were applied. We classified lower nighttime than daytime values into the ‘decreased group’, and the higher ones into the ‘increased group’, to analyze their effect on cardiopulmonary resuscitation occurrence or intensive care unit transfer. Nighttime heart rate and respiratory rate were lower than the daytime ones in both groups (both p values < 0.001), with no significant association with cardiopulmonary resuscitation occurrence or intensive care unit transfer in either group. Heart rate and respiratory rate tend to be lower at nighttime; however, their effect on the patient’s clinical outcome is not significant.

IMPORTANCE:. Factors associated with mortality in coronavirus disease 2019 patients on invasive mechanical ventilation are still not fully elucidated. OBJECTIVES:. To identify patient-level parameters, readily available at the bedside, associated with the risk of in-hospital mortality within 28 days from commencement of invasive mechanical ventilation or coronavirus disease 2019. DESIGN, SETTING, AND PARTICIPANTS:. Prospective observational cohort study by the global Coronavirus Disease 2019 Critical Care Consortium. Patients with laboratory-confirmed coronavirus disease 2019 requiring invasive mechanical ventilation from February 2, 2020, to May 15, 2021. MAIN OUTCOMES AND MEASURES:. Patient characteristics and clinical data were assessed upon ICU admission, the commencement of invasive mechanical ventilation and for 28 days thereafter. We primarily aimed to identify time-independent and time-dependent risk factors for 28-day invasive mechanical ventilation mortality. RESULTS:. One-thousand five-hundred eighty-seven patients were included in the survival analysis; 588 patients died in hospital within 28 days of commencing invasive mechanical ventilation (37%). Cox-regression analysis identified associations between the hazard of 28-day invasive mechanical ventilation mortality with age (hazard ratio, 1.26 per 10-yr increase in age; 95% CI, 1.16–1.37; p < 0.001), positive end-expiratory pressure upon commencement of invasive mechanical ventilation (hazard ratio, 0.81 per 5 cm H2O increase; 95% CI, 0.67–0.97; p = 0.02). Time-dependent parameters associated with 28-day invasive mechanical ventilation mortality were serum creatinine (hazard ratio, 1.28 per doubling; 95% CI, 1.15–1.41; p < 0.001), lactate (hazard ratio, 1.22 per doubling; 95% CI, 1.11–1.34; p < 0.001), Paco2 (hazard ratio, 1.63 per doubling; 95% CI, 1.19–2.25; p < 0.001), pH (hazard ratio, 0.89 per 0.1 increase; 95% CI, 0.8–14; p = 0.041), Pao2/Fio2 (hazard ratio, 0.58 per doubling; 95% CI, 0.52–0.66; p < 0.001), and mean arterial pressure (hazard ratio, 0.92 per 10 mm Hg increase; 95% CI, 0.88–0.97; p = 0.003). CONCLUSIONS AND RELEVANCE:. This international study suggests that in patients with coronavirus disease 2019 on invasive mechanical ventilation, older age and clinically relevant variables monitored at baseline or sequentially during the course of invasive mechanical ventilation are associated with 28-day invasive mechanical ventilation mortality hazard. Further investigation is warranted to validate any causative roles these parameters might play in influencing clinical outcomes.

Tracheo-innominate artery fistula (TIF) is a rare complication of tracheostomy and refers to the formation of a fistula between the trachea and innominate artery. Because TIF is fatal, prevention rather than treatment is very important. Here we report the cases of two high-risk patients who underwent tracheostomy, and in whose cases attempts were made to lower the risk of TIF. In the first patient who developed a chest deformity with Duchenne muscular dystrophy, a tracheostomy was performed with a high-level (cricothyroid level) approach compared with the standard tracheostomy. In the second patient, the thoracic cage was relatively small due to a giant omphalocele, and the risk of a fistula forming was decreased by wrapping the innominate artery with an opened polytetrafluoroethylene vascular graft after resolving crowding of the intrathoracic cavity by total thymectomy. There was no TIF occurrence at the outpatient follow-up in either case. We expect that our approaches may be effective intervention measures for preventing TIF.

Acute cerebral infarctions are rare in children, however they can occur as a complication of a Mycoplasma pneumoniae (MP) infection due to direct invasion, vasculitis, or a hypercoagulable state. We report on the case of a 5-year-old boy who had an extensive stroke in multiple cerebrovascular territories 10 days after the diagnosis of MP infection. Based on the suspicion that the cerebral infarction was associated with a macrolide-resistant MP infection, the patient was treated with levofloxacin, methyl-prednisolone, intravenous immunoglobulin, and enoxaparin. Despite this medical management, cerebral vascular narrowing progressed and a decompressive craniectomy became necessary for the patient’s survival. According to laboratory tests, brain magnetic resonance imaging, and clinical manifestations, the cerebral infarction in this case appeared to be due to the combined effects of hypercoagulability and cytokine-induced vascular inflammation.

Phacomatosis pigmentovascularis (PPV) is a rare syndrome characterized by concurrent nevus flammeus (capillary malformation) and pigmentary nevus. According to current research, the major pathophysiologic mechanism in PPV is venous dysplasia with resultant compensatory collateral channels and venous hypertension. Arterial involvement is rare. We herein report our experience on renovascular hypertension, intermittent claudication, and severe rhabdomyolysis due to diffuse stenosis of multiple arteries in a patient with PPV type IIb associated with SWS.

To effectively use vital signs as indicators in children, the magnitude of deviation from expected vital sign distribution should be determined. The purpose of this study is to derive age-specific centile charts for the heart rate and respiratory rate of the children who visited the emergency department. This study used the Korea’s National Emergency Department Information System dataset. Patients aged

Vancomycin is known to be unintentionally eliminated by continuous renal replacement therapy, and the protein bound fraction of vancomycin is also known to be different in adults and children. However, there are only a few studies investigating the relationship between the dose of continuous venovenous hemodiafiltration (CVVHDF) parameters and serum concentration of vancomycin in pediatric patients. The aim of this study was to determine clinical and demographic parameters that significantly affect serum vancomycin concentrations. This retrospective cohort study was conducted at a pediatric intensive care unit in a tertiary university children's hospital. Data from oliguric patients who underwent CVVHDF and vancomycin therapeutic drug monitoring were collected. The correlation between factors affecting serum concentration of vancomycin was analyzed using mixed effect model. A total of 177 serum samples undergoing vancomycin therapeutic drug monitoring were analyzed. The median age of study participants was 2.23 (interquartile range, 0.3-11.84) years, and 126 (71.19%) were male patients. Serum concentration of vancomycin decreased significantly as the effluent flow rate (EFR; P < 0.001), dialysate flow rate (DFR; P = 0.009), replacement fluid flow rate (RFFR; P = 0.008), the proportion of RFFR in the sum of DFR and RFFR (P = 0.025), and residual urine output increased. The adjusted R2 of the multivariate regression model was 0.874 (P < 0.001) and the equation was as follows: Vancomycin trough level (mg/L) = (0.283 × daily dose of vancomycin [mg/kg/d]) + (365.139 / EFR [mL/h/kg])-(15.842 × residual urine output [mL/h/kg]). This study demonstrated that the serum concentration of vancomycin was associated with EFR, DFR, RFFR, the proportion of RFFR, and residual urine output in oliguric pediatric patients receiving CVVHDF.

Children with hereditary neuromuscular diseases (NMDs) are at a high risk of morbidity and mortality related to respiratory failure. The use of home mechanical ventilation (HMV) has saved the lives of many children with NMD but, due to a lack of studies, dependable guidelines are not available. We drew upon our experience to compare the various underlying NMDs and to evaluate HMV with regard to respiratory morbidity, the proper indications and timing for its use, and to develop a policy to improve the quality of home noninvasive ventilation (NIV).We retrospectively analyzed the medical records of 57 children with childhood-onset hereditary NMDs in whom HMV was initiated between January 2000 and May 2013 at Seoul National University Children's Hospital. The degree of respiratory morbidity was estimated by the frequency and duration of hospitalizations caused by respiratory distress.The most common NMD was spinal muscular atrophy (SMA, n = 33). Emergent mechanical ventilation was initiated in 44% of the patients before the confirmed diagnosis, and the indicators of pre-HMV respiratory morbidity (e.g., extubation trials, hypoxia, hospitalizations, and intensive care unit stay) were greater in these patients than in others. The proportion of post-HMV hospitalizations (range, 0.00-0.52; median, 0.01) was lower than that of pre-HMV hospitalizations (0.02-1.00; 0.99) (P < 0.001). Eight patients were able to maintain home NIV. The main causes of NIV failure were air leakage and a large amount of airway secretions.The application of HMV helped reduce respiratory morbidity in children with childhood-onset hereditary NMD. Patients with SMA type I can benefit from an early diagnosis and the timely application of HMV. The choice between invasive and noninvasive HMV should be based on the patient's age and NIV trial tolerance. Systematic follow-up guidelines provided by a multidisciplinary team are needed.

Background: Diagnosing pediatric septic shock is difficult due to the complex and often impractical traditional criteria, such as systemic inflammatory response syndrome (SIRS), which result in delays and higher risks. This study aims to develop a deep learning-based model using SIRS data for early diagnosis in pediatric septic shock cases. Methods: The study analyzed data from pediatric patients (<18 years old) admitted to a tertiary hospital from January 2010 to July 2023. Vital signs, lab tests, and clinical information were collected. Septic shock cases were identified using SIRS criteria and inotrope use. A deep learning model was trained and evaluated using the area under the receiver operating characteristics curve (AUROC) and area under the precision-recall curve (AUPRC). Variable contributions were analyzed using the Shapley additive explanation value. Results: The analysis, involving 9,616,115 measurements, identified 34,696 septic shock cases (0.4%). Oxygen supply was crucial for 41.5% of the control group and 20.8% of the septic shock group. The final model showed strong performance, with an AUROC of 0.927 and AUPRC of 0.879. Key influencers were age, oxygen supply, sex, and partial pressure of carbon dioxide, while body temperature had minimal impact on estimation. Conclusions: The proposed deep learning model simplifies early septic shock diagnosis in pediatric patients, reducing the diagnostic workload. Its high accuracy allows timely treatment, but external validation through prospective studies is needed. [ABSTRACT FROM AUTHOR]

Vancomycin is a frequently used antibiotic in intensive care units, and the patient's renal clearance affects the pharmacokinetic characteristics of vancomycin. Several advantages have been reported for vancomycin continuous intravenous infusion, but studies on continuous dosing regimens based on patients' renal clearance are insufficient. The aim of this study was to develop a vancomycin serum concentration prediction model by factoring in a patient's renal clearance. Children admitted to our institution between July 1, 2021, and July 31, 2022 with records of continuous infusion of vancomycin were included in the study. Sex, age, height, weight, vancomycin dose by weight, interval from the start of vancomycin administration to the time of therapeutic drug monitoring sampling, and vancomycin serum concentrations were analyzed with the linear regression analysis of the mixed effect model. Univariable regression analysis was performed using the vancomycin serum concentration as a dependent variable. It showed that vancomycin dose (p < 0.001) and serum creatinine (p = 0.007) were factors that had the most impact on vancomycin serum concentration. Vancomycin serum concentration was affected by vancomycin dose (p < 0.001) and serum creatinine (p = 0.001) with statistical significance, and a multivariable regression model was obtained as follows: Vancomycin serum concentration (mg/l) = -1.296 + 0.281 × vancomycin dose (mg/kg) + 20.458 × serum creatinine (mg/dl) (adjusted coefficient of determination, R2 = 0.66). This prediction model is expected to contribute to establishing an optimal continuous infusion regimen for vancomycin. [ABSTRACT FROM AUTHOR]

This case study explores the use of CD45RA+ depleted lymphocyte infusion as a potential treatment for refractory cytomegalovirus (CMV) disease in an infant with complete DiGeorge syndrome (cDGS). The patient, who had a severe T cell deficiency, initially received ganciclovir but developed resistance. Infusing CD45RA+ depleted CMV-specific lymphocytes from the patient's father resulted in a decrease in CMV viral load. However, the patient ultimately passed away from pneumonia and sepsis. The study suggests that CD45RA+ depleted lymphocyte infusion may be a viable option for treating refractory CMV disease in cDGS patients, but further research is needed. [Extracted from the article]