Platelets are crucial for maintaining physiological equilibrium, thrombosis formation, inflammation, bacterial defense, wound repair, angiogenesis, and tumorigenesis. In the Pediatric Intensive Care Unit (PICU), children frequently exhibit platelet reductions or functional alterations due to diverse pathological conditions, which significantly influence disease progression and therapeutic approaches. We analyzed the association between platelets count and its derived parameters and all-cause mortality. Adjusted smoothing spline plots, subgroup analysis and segmented multivariate logistic regression analysis were conducted to estimate the relative risk between proportional risk between platelets and all-cause mortality. Of the 11625 children, 677 (5.82%) died. After adjusting for confounders, there was a negative association between platelets and the risk of all-cause mortality in PICU. For every 100 × 10^9/L increase in platelets, the risk of death was reduced by 17% (adjusted OR = 0.83, 95% CI: 0.78, 0.89). The results of sensitivity analysis showed that in different stratified analyses (age, ICU category,WBC Count), the effect of platelets count on all-cause mortality remained stable. After adjusting for inflammation, nutrition, and liver function factors, platelets reduction is still an independent risk factor for PICU all-cause mortality.

The growing concern of pediatric mortality demands heightened preparedness in clinical settings, especially within intensive care units (ICUs). As respiratory-related admissions account for a substantial portion of pediatric illnesses, there is a pressing need to predict ICU mortality in these cases. This study based on data from 1188 patients, addresses this imperative using machine learning techniques and investigating different class balancing methods for pediatric ICU mortality prediction. This study employs the publicly accessible \"Paediatric Intensive Care database\" to train, validate, and test a machine learning model for predicting pediatric patient mortality. Features were ranked using three machine learning feature selection techniques, namely Random Forest, Extra Trees, and XGBoost, resulting in the selection of 16 critical features from a total of 105 features. Ten machine learning models and ensemble techniques are used to make accurate mortality predictions. To tackle the inherent class imbalance in the dataset, we applied a unique data partitioning technique to enhance the model\'s alignment with the data distribution. The CatBoost machine learning model achieved an area under the curve (AUC) of 72.22%, while the stacking ensemble model yielded an AUC of 60.59% for mortality prediction. The proposed subdivision technique, on the other hand, provides a significant improvement in performance metrics, with an AUC of 85.2% and an accuracy of 89.32%. These findings emphasize the potential of machine learning in enhancing pediatric mortality prediction and inform strategies for improved ICU readiness.

BACKGROUND: Postextubation respiratory support in pediatric ARDS may be used to support the recovering respiratory system and promote timely, successful liberation from mechanical ventilation. This study\'s aims were to (1) describe the use of postextubation respiratory support in pediatric ARDS from the time of extubation to hospital discharge, (2) identify potential risk factors for postextubation respiratory support, and (3) provide preliminary data for future larger studies.
METHODS: This pilot single-center prospective cohort study recruited subjects with pediatric ARDS. Subjects\' respiratory status up to hospital discharge, the use of postextubation respiratory support, and how it changed over time were recorded. Analysis was performed comparing subjects who received postextubation respiratory support versus those who did not and compared its use among pediatric ARDS severity categories. Multivariable logistic regression was used to determine variables associated with the use of postextubation respiratory support and included oxygenation index (OI), ventilator duration, and weight.
RESULTS: Seventy-three subjects with pediatric ARDS, with median age and OI of 4 (0.6-10.5) y and 7.3 (4.9-12.7), respectively, were analyzed. Postextubation respiratory support was provided to 54/73 (74%) subjects: 28/45 (62.2%), 19/21 (90.5%), and 7/7 (100%) for mild, moderate, and severe pediatric ARDS, respectively, (P = .01). OI and mechanical ventilation duration were higher in subjects who received postextubation respiratory support (8.7 [5.4-14] vs 4.6 [3.7-7], P < .001 and 10 [7-17] d vs 4 [2-7] d, P < .001) compared to those who did not. At hospital discharge, 12/67 (18.2%) survivors received home respiratory support (6 subjects died prior to hospital discharge). In the multivariable model, ventilator duration (adjusted odds ratio 1.3 [95% CI 1.0-1.7], P = .050) and weight (adjusted odds ratio 0.95 [95% CI 0.91-0.99], P = .02) were associated with the use of postextubation respiratory support.
CONCLUSIONS: The majority of intubated subjects with pediatric ARDS received respiratory support postextubation, and a substantial proportion continued to require it up to hospital discharge.

Objectives In the setting of the recent global pandemic, children infected with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus causing the coronavirus disease 2019 (COVID-19) presented to our hospital with a variety of symptoms ranging from mild to severe disease including multiorgan dysfunction. Our objective was to study the clinical profile, risk factors, complications, and outcomes in pediatric patients admitted to our center with SARS-CoV-2 infection. Methods This retrospective observational study was conducted at a large quaternary center in Riyadh between May 2020 and September 2021. The study population was comprised of children between 0 and ≤14 years with SARS-CoV-2 suspicion or positivity. Results One hundred and fifty-six children were included in the study, the majority of whom were 1-10 years old. One hundred and twenty of them (76.93%) were SARS-CoV-2 positive. Fifty-nine patients (37.18%) were labelled as multisystem inflammatory syndrome in children (MIS-C) based on clinical and lab criteria, of whom 35 (22.44%) tested SARS-CoV-2 positive. Hematological disease was found to be the most common comorbidity, followed by neurological and chronic lung diseases. The most common symptoms encountered were fever, cough, vomiting, fatigue, and diarrhea. Eighty patients (51%) required pediatric intensive care unit (PICU) admission (length of stay: 5-12 days), among whom 32 (40%) required ventilation, 26 (32.5%) needed hemodynamic support, and three patients (3.75%) underwent continuous renal replacement therapy (CRRT). The overall mortality rate was 4.5% (seven patients) among the studied population. The most frequent lab abnormalities were found to be elevated serum ferritin, erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), and lactate dehydrogenase (LDH) levels. Ninety-one percent received antibiotics, and prophylactic anticoagulant was used in 32%. In the MIS-C subset, 80.5% received steroids, 71.43% intravenous immunoglobulin (IVIG), and 5.17% (three patients) tocilizumab. Conclusion The SARS-CoV-2 infection presented with a range of severity among our cohort of children; however, most of the patients responded well to appropriate supportive treatment. A slight male preponderance was noted. The most common symptoms encountered were fever, cough, vomiting, fatigue, and diarrhea. Inflammatory markers such as ESR, CRP, serum ferritin, and LDH levels were found to be elevated in nearly all patients. Raised serum lactate and serum creatinine and lymphopenia were of significant note in patients with MIS-C. Higher mortality rates were observed in patients with MIS-C and those requiring respiratory support. In addition to these two factors, the presence of comorbidities and the need for CRRT were associated with prolonged PICU length of stay.

BACKGROUND: Unplanned extubation (UE) is defined as unintentional dislodgement of an endotracheal tube (ETT) from the trachea. UEs can lead to instability, cardiac arrest, and may require emergent tracheal re-intubation. As part of our hospital-wide quality improvement (QI) work, a multidisciplinary committee reviewed all UEs to determine contributing factors and evaluation of clinical outcomes to develop QI interventions aimed to minimize UEs. The objective was to investigate occurrence, contributing factors, and clinical outcomes of UEs in the pediatric ICU (PICU), cardiac ICU (CICU), and neonatal ICU (NICU) in a large academic children\'s hospital. We hypothesized that these would be substantially different across 3 ICUs.
METHODS: A single-center retrospective review of UEs in the PICU, CICU, and NICU was recorded in a prospective database for the last 5 y. Consensus-based standardized operational definitions were developed to capture contributing factors and adverse events associated with UEs. Data were extracted through electronic medical records by 3 respiratory therapists and local Virtual Pediatric Systems (VPS) database. Consistency of data extraction and classification were evaluated.
RESULTS: From January 2016-December 2021, 408 UEs in 339 subjects were reported: PICU 52 (13%), CICU 31 (7%), and NICU 325 (80%). The median (interquartile range) of age and weight was 2.0 (0-4.0) months and 5.3 (3.0-8.0) kg. Many UE events were not witnessed (54%). Common contributing factors were routine nursing care (no. = 70, 18%), ETT retaping (no. = 62, 16%), and being held (no. = 15, 3.9%). The most common adverse events with UE were desaturation < 80% (33%) and bradycardia (22.8%). Cardiac arrest occurred in 12%. Sixty-seven percent of UEs resulted in re-intubation within 72 h. The proportion of re-intubation across 3 units was significantly different: PICU 62%, CICU 35%, NICU 71%, P < .001.
CONCLUSIONS: UEs occurred commonly in a large academic children\'s hospital. Whereas UE was associated with adverse events, re-intubation rates within 72 h were < 70% and variable across the units.

UNASSIGNED: Pediatric intensive care unit (PICU)-associated delirium contributes to a decline in postdischarge quality of life, with worse outcomes for individuals with delayed identification. As delirium screening rates remain low within PICUs, caregivers may be able to assist with early detection, for which they need more education, as awareness of pediatric delirium among caregivers remains limited.
UNASSIGNED: This study aimed to develop an educational tool for caregivers to identify potential delirium symptoms during their child\'s PICU stay, educate them on how to best support their child if they experience delirium, and guide them to relevant family resources.
UNASSIGNED: Web-based focus groups were conducted at a tertiary pediatric hospital with expected end users of the tool (ie, PICU health care professionals and caregivers of children with an expected PICU length of stay of over 48 h) to identify potential educational information for inclusion in a family resource guide and to identify strategies for effective implementation. Data were analyzed thematically to generate requirements to inform prototype development. Participants then provided critical feedback on the initial prototype, which guided the final design.
UNASSIGNED: In all, 24 participants (18 health care professionals and 6 caregivers) attended 7 focus groups. Participants identified five informational sections for inclusion: (1) delirium definition, (2) key features of delirium (signs and symptoms), (3) postdischarge outcomes associated with delirium, (4) tips to inform family-centered care, and (5) education or supportive resources. Participants identified seven design requirements: information should (1) be presented in an order that resembles the structure of the clinical discussion around delirium; (2) increase accessibility, recall, and preparedness by providing multiple formats; (3) aim to reduce stress by implementing positive framing; (4) minimize cognitive load to ensure adequate information processing; (5) provide supplemental electronic resources via QR codes; (6) emphasize collaboration between caregivers and the health care team; and (7) use prompting questions to act as a call to action for caregivers.
UNASSIGNED: Key design requirements derived from end-user feedback were established and guided the development of a novel pediatric delirium education tool. Implementing this tool into regular practice has the potential to reduce distress and assist in the early recognition and treatment of delirium in the PICU domain. Future evaluation of its clinical utility is necessary.

Communication is a central aspect of nursing care and is especially important when pertaining to progressive illnesses and end of life. This article reviews basic palliative care terminology and outlines a variety of communication frameworks from the \"dos\" to the \"don\'ts.\" These communication strategies are meant to be added to the nurse\'s \"toolbox\" so that nurses may use them in various scenarios. These communication tools are meant to help mitigate the stress and discomfort nurses often feel when using palliative communication or delivering bad news.

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BACKGROUND: Infants with a high risk of extubation failure are often treated with noninvasive ventilation (NIV) or CPAP, but data on the role of these support modalities following extubation are sparse. This report describes our experience using NIV or CPAP to support infants following extubation in our pediatric ICUs (PICUs).
METHODS: We performed a retrospective study of children < 10 kg receiving postextubation NIV or CPAP in our PICUs. Data on demographics, medical history, type of support, vital signs, pulse oximetry, near-infrared spectroscopy (NIRS), gas exchange, support settings, and re-intubation were extracted from the electronic medical record. Support was classified as prophylactic if planned before extubation and rescue if initiated within 24 h of extubation. We compared successfully extubated and re-intubated subjects using chi-square test for categorical variables and Mann-Whitney test for continuous variables.
RESULTS: We studied 51 subjects, median age 44 (interquartile range 0.5-242) d and weight 3.7 (3-4.9) kg. There were no demographic differences between groups, except those re-intubated were more likely to have had cardiac surgery prior to admission (0% vs 14%, P = .040). NIV was used in 31 (61%) and CPAP in 20 (39%) subjects. Prophylactic support was initiated in 25 subjects (49%), whereas rescue support was needed in 26 subjects (51%). Twenty-two subjects (43%) required re-intubation. Re-intubation rate was higher for rescue support (58% vs 28%, P = .032). Subjects with a pH < 7.35 (4.3% vs 42.0%, P = .003) and lower somatic NIRS (39 [24-56] vs 62 [46-72], P = .02) were more likely to be re-intubated. The inspiratory positive airway pressure, expiratory positive airway pressure, and FIO2 were higher in subjects who required re-intubation.
CONCLUSIONS: NIV or CPAP use was associated with a re-intubation rate of 43% in a heterogeneous sample of high-risk infants. Acidosis, cardiac surgery, higher FIO2 , lower somatic NIRS, higher support settings, and application of rescue support were associated with the need for re-intubation.

OBJECTIVE: We developed a new nomogram for the prediction of mortality risk in children in pediatric intensive care units (PICU).
METHODS: We conducted a retrospective analysis using the PICU Public Database, a study that included a total of 10,538 children, to develop a new risk model for mortality in children in the intensive care units (ICU). The prediction model was analyzed using multivariate logistic regression with predictors including age and physiological indicators, and the prediction model was presented as a nomogram. The performance of the nomogram was evaluated based on its discriminative power and was internally validated.
RESULTS: Predictors contained in the individualized prediction nomogram included the neutrophils, platelets, albumin, lactate, oxygen saturation (P<0.1). The area under the receiver operating characteristic (ROC) curve for this prediction model is 0.7638 (95% CI: 0.7415-0.7861), which has effective discriminatory power. The area under the ROC curve of the prediction model in the validation dataset is 0.7404 (95% CI: 0.7016-0.7793), which is still effectively discriminative.
CONCLUSIONS: The mortality risk prediction model constructed in this study can be easily used for individualized prediction of mortality risk in children in pediatric intensive care units.