Introduction: Heart failure represents 10-33% of pediatric cardiac admissions. We present diagnostic challenges of LCOS in an adolescent patient with pre-existent severe heart failure. Description: A 15-year-old female with non-obstructive hypertrophic cardiomyopathy and severe left ventricular dysfunction presented with 2 days of vomiting, and worsening somnolence. Her past history included Danon disease, non-sustained ventricular tachycardia s/p implantable cardiac defibrillator placement, type 1 diabetes mellitus, remote cerebrovascular infarct, and cognitive impairment. Home medications included carvedilol, lisinopril, verapamil, furosemide, insulin, warfarin and aspirin. Her examination revealed sinus bradycardia (HR: 55-58/min), hypotension (BP: 88/54 mmHg), and profound somnolence. Her laboratory studies revealed lactic acidosis, hyperkalemia without ketoacidosis, and normal glucose levels. She was treated with calcium gluconate, insulin and dextrose, fluid bolus with resolution of her hyperkalemia. Her ECG revealed sinus bradycardia with prolonged PR interval, and her echocardiogram demonstrated severe left ventricular dysfunction. A tentative diagnosis of acute on chronic heart failure was made. Epinephrine infusion (titrated up to 0.2 mcg/kg/min), milrinone infusion, and mechanical ventilation were initiated without response. A suspicion of beta-blocker (BB) and calcium channel blocker (CCB) overdose was entertained for refractory bradycardia and hypotension. A bolus of glucagon followed by infusion, insulin drip and glucose infusion were administered with resolution of her bradycardia and hypotension (HR: 90-114/min; BP: 114/64 mmHg). She was weaned off inotropic support and extubated after 3 days. Discussion: Our case demonstrates diagnostic dilemma of patients presenting in LCOS in the context of pre-existing cardiac dysfunction. A lack of significant improvement on standard therapy should trigger a work-up for additional causes for LCOS. Hospital admissions for intentional overdose have doubled with the sharpest increases observed in adolescent age group. Treatment of BB and CCB overdose is similar, with high-dose insulin and dextrose infusions in addition to catecholamine infusion. Glucagon is utilized to treat refractory bradycardia.
Noninfectious pulmonary complications of hematopoietic stem cell transplant are currently more prevalent than infectious complications.Unfortunately, the pathophysiology basis is not completely understood.However, there is a string association with graft-versus-host disease for many of them.Therefore, an important component of their pathophysiology is likely an allo-immune response.There is much research that needs to be conducted to improve the less than optimal outcomes for these disorders.
Abstract Background This study aims to externally validate a clinical mathematical model designed to predict urine output (UOP) during the initial post-operative period in pediatric patients who underwent cardiac surgery with cardiopulmonary bypass (CPB). Methods Children aged 0–18 years admitted to the pediatric cardiac intensive care unit at Cleveland Clinic Children’s from April 2018 to April 2023, who underwent cardiac surgery with CPB were included. Patients were excluded if they had pre-operative kidney failure requiring kidney replacement therapy (KRT), re-operation or extracorporeal membrane oxygenation or KRT requirement within the first 32 post-operative hours or had indwelling urinary catheter for fewer than the initial 32 post-operative hours, or had vasoactive-inotrope score of 0, or those with missing data in the electronic health records. Results A total of 213 encounters were analyzed; median age (days): 172 (IQR 25–75th%: 51–1655), weight (kg): 6.1 (IQR 25–75th%: 3.8–15.5), median UOP ml/kg/hr in the first 32 post-operative hours: 2.59 (IQR 25–75th%: 1.93–3.26) and post-operative 30-day mortality: 1, (0.4%). The mathematical model achieved the following metrics in the entire dataset: mean absolute error (95th% Confidence Interval (CI)): 0.70 (0.67–0.73), median absolute error (95th% CI): 0.54 (0.52–0.56), mean squared error (95th% CI): 0.97 (0.89–1.05), root mean squared error (95th% CI): 0.99 (0.95–1.03) and R2 Score (95th% CI): 0.29 (0.24–0.34). Conclusions This study provides encouraging external validation results of a mathematical model predicting post-operative UOP in pediatric cardiac surgery patients. Further multicenter studies must explore its broader applicability. Graphical abstract
Pediatric Index of Mortality 3 is a validated tool including 11 variables for the assessment of mortality risk in PICU patients. With the recent advances in explainable machine learning algorithms, we aimed to assess feasibility of application of these machine learning models to simplify the Pediatric Index of Mortality 3 scoring system in order to decrease time and labor required for data collection and entry for Pediatric Index of Mortality 3.Single-center, retrospective cohort study. Data from the Virtual Pediatric Systems for patients admitted to Cleveland Clinic Children`s PICU between January 2008 and December 2019 was obtained. Light Gradient Boosting Machine Regressor (a gradient boosting decision tree algorithm) was used for building the machine learning models. Variable importance was analyzed by SHapley Additive exPlanations. All of the 11 Pediatric Index of Mortality 3 variables were used as input variables in the machine learning models to predict Pediatric Index of Mortality 3 risk of mortality as the outcome variable. Mean absolute error, root mean squared error, and R-squared were calculated for each of the 11 machine learning models as model performance parameters.Quaternary children's hospital.PICU patients.None.Five-thousand sixty-eight patients were analyzed. The machine learning models were able to maintain similar predictive error until the number of input variables decreased to four. The machine learning model with five input variables (mechanical ventilation in the first hour of PICU admission, very-high-risk diagnosis, surgical recovery from a noncardiac procedure, low-risk diagnosis, and base excess) produced lowest mean root mean squared error of 1.49 (95% CI, 1.05-1.93) and highest R-squared of 0.73 (95% CI, 0.6-0.86) with mean absolute error of 0.43 (95% CI, 0.35-0.5) among all the 11 machine learning models.Explainable machine learning methods were feasible in simplifying the Pediatric Index of Mortality 3 scoring system with similar risk of mortality predictions compared to the original Pediatric Index of Mortality 3 model tested in a single-center dataset.
