Invasive Mechanical Ventilation and Risk of Hospital-Acquired Venous Thromboembolism.

BACKGROUND: This study sought to estimate the overall cumulative incidence and odds of Hospital-acquired venous thromboembolism (VTE) among critically ill children with and without exposure to invasive ventilation. In doing so, we also aimed to describe the temporal relationship between invasive ventilation and hospital-acquired VTE development. METHODS: We performed a retrospective cohort study using Virtual Pediatric Systems (VPS) data from 142 North American pediatric ICUs among children < 18 y of age from January 1, 2016-December 31, 2022. After exclusion criteria were applied, cohorts were identified by presence of invasive ventilation exposure. The primary outcome was cumulative incidence of hospital-acquired VTE, defined as limb/neck deep venous thrombosis or pulmonary embolism. Multivariate logistic regression was used to determine whether invasive ventilation was an independent risk factor for hospital-acquired VTE development. RESULTS: Of 691,118 children studied, 86,922 (12.4%) underwent invasive ventilation. The cumulative incidence of hospital-acquired VTE for those who received invasive ventilation was 1.9% and 0.12% for those who did not (P < .001). The median time to hospital-acquired VTE after endotracheal intubation was 6 (interquartile range 3-14) d. In multivariate models, invasive ventilation exposure and duration were each independently associated with development of hospital-acquired VTE (adjusted odds ratio 1.64 [95% CI 1.42-1.86], P < .001; and adjusted odds ratio 1.03 [95% CI 1.02-1.03], P < .001, respectively). CONCLUSIONS: In this multi-center retrospective review from the VPS registry, invasive ventilation exposure and duration were independent risk factors for hospital-acquired VTE among critically ill children. Children undergoing invasive ventilation represent an important target population for risk-stratified thromboprophylaxis trials.

Relationship between pediatric intensive care unit length of stay and 24-h unplanned readmission rate.

OBJECTIVE: To evaluate the relationship between pediatric intensive care unit (PICU) severity-adjusted length of stay (LOS) and 24-h unplanned readmission rate. DATA SOURCE: Data were obtained from a 10-year cohort from 2009 to 2018 from the Virtual Pediatric Systems (VPS, LLC) database. STUDY DESIGN: In this retrospective study, standardized LOS ratio was computed for each PICU as the ratio of the sum of actual LOS divided by the predicted LOS for each PICU using VPS predictive LOS model. Correlation between standardized LOS ratios and 24-h unplanned readmission rates were computed using Pearson's correlation coefficient. PRINCIPAL FINDINGS: There was practically no relationship between standardized LOS ratio and 24-h readmission rate (R2  = 0.05). DATA COLLECTION/EXTRACTION METHODS: Not Applicable. CONCLUSIONS: Severity-adjusted LOS has no relationship with 24-h unplanned readmission rate. These findings suggest that the relationship between PICU severity-adjusted LOS and 24-h unplanned readmission rate should not be used as a balancing quality measure.

Outcomes of Children With Firearm Injuries Admitted to the PICU in the United States.

OBJECTIVES: Firearm-related injury is the second leading cause of injury and death for children 1-18 years old in United States. The objective of our study was to analyze the outcomes of children admitted to the PICU with firearm injuries. DESIGN: Retrospective study. SETTING: PICUs in United States contributing data to Virtual Pediatric Systems, LLC, from January 2009 to December 2017. PATIENTS: Children age 1 month to 18 years old admitted to the PICU with firearm injury, identified by external cause of injury E-codes and International Classification of Diseases, 9th Edition, and International Classification of Diseases, 10th Edition, codes were identified. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: There were 1,447 cases identified of which 175 (12%) died in the PICU. Unintentional firearm injury (67.7%) and assault with a firearm injury (20%) comprised 90% of the cases. Males comprised 78% of the cohort (1,122) and race distribution included 45% Black (646), 27% White (390), and 12% Hispanic (178). Among the children who died in the PICU, 55% were 13-18 years old. Children attempting suicide with a firearm were more likely to die in the PICU as compared to the other causes of firearm injury. Based on their Pediatric Overall Performance Category and Pediatric Cerebral Performance Category scores at discharge, there is high morbidity in children with firearm injuries. CONCLUSIONS: Mortality rate of children with firearm injury admitted to the PICU is high. Children admitted to the PICU with suicide attempt with a firearm carried the highest mortality. Further studies may help further define the epidemiology of firearm injuries in children and plan interventions to minimize these unnecessary deaths.

Declining Procedures in Pediatric Critical Care Medicine Using a National Database.

OBJECTIVES: To investigate the change in rate of invasive procedures (endotracheal intubation, central venous catheters, arterial catheters, and peripheral inserted central venous catheters) performed in PICUs per admission over time. Secondarily, to investigate the change in type of respiratory support over time. DESIGN: Retrospective study of prospectively collected data using the Virtual Pediatric Systems (VPS; LLC, Los Angeles, CA) database. SETTING: North American PICUs. PATIENTS: Patients admitted from January 2009 to December 2017. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: There were 902,624 admissions from 161 PICUs included in the analysis. Since 2009, there has been a decrease in rate of endotracheal intubations, central venous catheters placed, and arterial catheters placed and an increase in the rate of peripheral inserted central venous catheter insertion per admission over time after controlling for severity of illness and unit level effects. As compared to 2009, the incident rate ratio for 2017 for endotracheal intubation was 0.90 (95% CI, 0.83-0.98; p = 0.017), for central venous line placement 0.69 (0.63-0.74; p < 0.001), for arterial catheter insertion 0.85 (0.79-0.92; p < 0.001), and for peripheral inserted central venous catheter placement 1.14 (1.03-1.26; p = 0.013). Over this time period, in a subgroup with available data, there was a decrease in the rate of invasive mechanical ventilation and an increase in the rate of noninvasive respiratory support (bilevel positive airway pressure/continuous positive airway pressure and high-flow nasal oxygen) per admission. CONCLUSIONS: Over 9 years across multiple North American PICUs, the rate of endotracheal intubations, central catheter, and arterial catheter insertions per admission has decreased. The use of invasive mechanical ventilation has decreased with an increase in noninvasive respiratory support. These data support efforts to improve exposure to invasive procedures in training and structured systems to evaluate continued competency.

Performance of Pediatric Mortality Prediction Scores for PICU Mortality and 90-Day Mortality.

OBJECTIVES: The use of mortality prediction scores in clinical trials in the PICU is essential for comparing patient groups. Because of the decline in PICU mortality over the last decades, leading to a shift toward later deaths, recent trials use 90-day mortality as primary outcome for estimating mortality and survival more accurately. This study assessed and compared the performance of two frequently used PICU mortality prediction scores for prediction of PICU and 90-day mortality. DESIGN: This secondary analysis of the randomized controlled Early versus Late Parenteral Nutrition in the Pediatric Intensive Care Unit trial compared the discrimination (area under the receiver operating characteristic curve) and calibration of the Pediatric Index of Mortality 3 and the Pediatric Risk of Mortality III scores for prediction of PICU and 90-day mortality. SETTING: Three participating PICUs within academic hospitals in Belgium, the Netherlands, and Canada. PATIENTS: One-thousand four-hundred twenty-eight critically ill patients 0-17 years old. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Although Pediatric Index of Mortality 3 only includes information available at the time of PICU admission, thus before any intervention in the PICU, it showed good discrimination (area under the receiver operating characteristic curve, 0.894; 95% CI, 0.892-0.896) and good calibration (no deviation from the diagonal, p = 0.58) for PICU mortality. Pediatric Risk of Mortality III, which involves the worst values for the evaluated variables during the first 24 hours of PICU stay, was statistically more discriminant (area under the receiver operating characteristic curve, 0.920; 95% CI, 0.918-0.921; p = 0.04) but poor in calibration (significant deviation from the diagonal; p = 0.04). Pediatric Index of Mortality 3 and Pediatric Risk of Mortality III discriminated equally well between 90-day mortality and survival (area under the receiver operating characteristic curve, 0.867; 95% CI, 0.866-0.869 and area under the receiver operating characteristic curve, 0.882; 95% CI, 0.880-0.884, respectively, p = 0.77), but Pediatric Risk of Mortality III was not well calibrated (p = 0.04), unlike Pediatric Index of Mortality 3 (p = 0.34). CONCLUSIONS: Pediatric Index of Mortality 3 performed better in calibration for predicting PICU and 90-day mortality than Pediatric Risk of Mortality III and is not influenced by intervention or PICU quality of care. Therefore, Pediatric Index of Mortality 3 seems a better choice for use in clinical trials with 90-day mortality as primary outcome.

PICU Volume and Outcome: A Severity-Adjusted Analysis.

OBJECTIVES: To determine the relationship between PICU volume and severity-adjusted mortality in a large, national dataset. DESIGN: Retrospective cohort study. SETTING: The VPS database (VPS, LLC, Los Angeles, CA), a national multicenter clinical PICU database. PATIENTS: All patients with discharge dates between September 2009 and March 2012 and valid Pediatric Index of Mortality 2 and Pediatric Risk of Mortality III scores, who were not transferred to another ICU and were seen in an ICU that collected at least three quarters of data. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Anonymized data received included ICU mortality, hospital and patient demographics, and Pediatric Index of Mortality 2 and Pediatric Risk of Mortality III scores. PICU volume/quarter was determined (VPS sites submit data quarterly) per PICU and was divided by 100 to assess the impact per 100 discharges per quarter (volume). A mixed-effects logistic regression model accounting for repeated measures of patients within ICUs was performed to assess the association of volume on severity-adjusted mortality, adjusting for patient and unit characteristics. Multiplicative interactions between volume and severity of illness were also modeled. We analyzed 186,643 patients from 92 PICUs, with an overall ICU mortality rate of 2.6%. Volume ranged from 0.24 to 8.89 per ICU per quarter; the mean volume was 2.61. The mixed-effects logistic regression model found a small but nonlinear relationship between volume and mortality that varied based on the severity of illness. When severity of illness is low, there is no clear relationship between volume and mortality up to a Pediatric Index of Mortality 2 risk of mortality of 10%; for patients with a higher severity of illness, severity of illness-adjusted mortality is directly proportional to a unit's volume. CONCLUSIONS: For patients with low severity of illness, ICU volume is not associated with mortality. As patient severity of illness rises, higher volume units have higher severity of illness-adjusted mortality. This may be related to differences in quality of care, issues with unmeasured confounding, or calibration of existing severity of illness scores.

Monitoring Dead Space in Mechanically Ventilated Children: Volumetric Capnography Versus Time-Based Capnography.

BACKGROUND: Volumetric capnography dead-space measurements (physiologic dead-space-to-tidal-volume ratio [VD/VT] and alveolar VD/VT) are considered more accurate than the more readily available time-based capnography dead-space measurement (end-tidal alveolar dead-space fraction [AVDSF]). We sought to investigate the correlation between volumetric capnography and time-based capnography dead-space measurements. METHODS: This was a single-center prospective cohort study of 65 mechanically ventilated children with arterial lines. Physiologic VD/VT, alveolar VD/VT, and AVDSF were calculated with each arterial blood gas using capnography data. RESULTS: We analyzed 534 arterial blood gases from 65 children (median age 4.9 y, interquartile range 1.7-12.8). The correlation between physiologic VD/VT and AVDSF (r = 0.66, 95% CI 0.59-0.72) was weaker than the correlation between alveolar VD/VT and AVDSF (r = 0.8, 95% CI 0.76-0.85). The correlation between physiologic VD/VT and AVDSF was weaker in children with low PaO2 /FIO2 (< 200 mm Hg), low exhaled VT (< 100 mL), a pulmonary reason for mechanical ventilation, or large airway VD (> 3 mL/kg). All 3 dead-space measurements were highly correlated (r > 0.7) in children without hypoxemia (PaO2 /FIO2 > 300 mm Hg), mechanically ventilated for a neurologic or cardiac reason, or on significant inotropes or vasopressors. CONCLUSIONS: In mechanically ventilated children without significant hypoxemia or with cardiac output-related dead-space changes, physiologic VD/VT was highly correlated with AVDSF and alveolar VD/VT. In children with significant hypoxemia, physiologic VD/VT was poorly correlated with AVDSF. Alveolar VD/VT and AVDSF correlated well in most tested circumstances. Therefore, AVDSF may be useful in most children for alveolar dead-space monitoring.

Pediatric Index of Cardiac Surgical Intensive Care Mortality Risk Score for Pediatric Cardiac Critical Care.

OBJECTIVE: Comparison of clinical outcomes is imperative in the evaluation of healthcare quality. Risk adjustment for children undergoing cardiac surgery poses unique challenges, due to its distinct nature. We developed a risk-adjustment tool specifically focused on critical care mortality for the pediatric cardiac surgical population: the Pediatric Index of Cardiac Surgical Intensive care Mortality score. DESIGN: Retrospective analysis of prospectively collected pediatric critical care data. SETTING: Pediatric critical care units in the United States. PATIENTS: Pediatric cardiac intensive care surgical patients. INTERVENTIONS: Prospectively collected data from consecutive patients admitted to ICUs were obtained from The Virtual PICU System (VPS, LLC, Los Angeles, CA), a national pediatric critical care database. Thirty-two candidate physiologic, demographic, and diagnostic variables were analyzed for inclusion in the development of the Pediatric Index of Cardiac Surgical Intensive care Mortality model. Multivariate logistic regression with stepwise selection was used to develop the model. MEASUREMENTS AND MAIN RESULTS: A total of 16,574 cardiac surgical patients from the 55 PICUs across the United States were included in the analysis. Thirteen variables remained in the final model, including the validated Society of Thoracic Surgeons-European Association of Cardio-Thoracic Surgery Congenital Heart Surgery Mortality (STAT) score and admission time with respect to cardiac surgery, which identifies whether the patient underwent the index surgical procedure before or after admission to the ICU. Pediatric Index of Cardiac Surgical Intensive Care Mortality (PICSIM) performance was compared with the performance of Pediatric Risk of Mortality-3 and Pediatric Index of Mortality-2 risk of mortality scores, as well as the STAT score and STAT categories by calculating the area under the curve of the receiver operating characteristic from a validation dataset: PICSIM (area under the curve = 0.87) performed better than Pediatric Index of Mortality-2 (area under the curve = 0.81), Pediatric Risk of Mortality-3 (area under the curve = 0.82), STAT score (area under the curve = 0.77), STAT category (area under the curve = 0.75), and Risk Adjustment for Congenital Heart Surgery-1 (area under the curve = 0.74). CONCLUSIONS: This newly developed mortality score, PICSIM, consisting of 13 risk variables encompassing physiology, cardiovascular condition, and time of admission to the ICU showed better discrimination than Pediatric Index of Mortality-2, Pediatric Risk of Mortality-3, and STAT score and category for mortality in a multisite cohort of pediatric cardiac surgical patients. The introduction of the variable "admission time with respect to cardiac surgery" allowed prediction of mortality when patients are admitted to the ICU either before or after the index surgical procedure.

Evidence-Based Pediatric Outcome Predictors to Guide the Allocation of Critical Care Resources in a Mass Casualty Event.

OBJECTIVE: ICU resources may be overwhelmed by a mass casualty event, triggering a conversion to Crisis Standards of Care in which critical care support is diverted away from patients least likely to benefit, with the goal of improving population survival. We aimed to devise a Crisis Standards of Care triage allocation scheme specifically for children. DESIGN: A triage scheme is proposed in which patients would be divided into those requiring mechanical ventilation at PICU presentation and those not, and then each group would be evaluated for probability of death and for predicted duration of resource consumption, specifically, duration of PICU length of stay and mechanical ventilation. Children will be excluded from PICU admission if their mortality or resource utilization is predicted to exceed predetermined levels ("high risk"), or if they have a low likelihood of requiring ICU support ("low risk"). Children entered into the Virtual PICU Performance Systems database were employed to develop prediction equations to assign children to the exclusion categories using logistic and linear regression. Machine Learning provided an alternative strategy to develop a triage scheme independent from this process. SETTING: One hundred ten American PICUs SUBJECTS: : One hundred fifty thousand records from the Virtual PICU database. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The prediction equations for probability of death had an area under the receiver operating characteristic curve more than 0.87. The prediction equation for belonging to the low-risk category had lower discrimination. R for the prediction equations for PICU length of stay and days of mechanical ventilation ranged from 0.10 to 0.18. Machine learning recommended initially dividing children into those mechanically ventilated versus those not and had strong predictive power for mortality, thus independently verifying the triage sequence and broadly verifying the algorithm. CONCLUSION: An evidence-based predictive tool for children is presented to guide resource allocation during Crisis Standards of Care, potentially improving population outcomes by selecting patients likely to benefit from short-duration ICU interventions.

Health and economic outcomes for exenatide once weekly, insulin, and pioglitazone therapies in the treatment of type 2 diabetes: a simulation analysis.

BACKGROUND: Patients with type 2 diabetes (T2DM) are at risk of long-term vascular complications. In trials, exenatide once weekly (ExQW), a GLP-1R agonist, improved glycemia, weight, blood pressure (BP), and lipids in patients with T2DM. We simulated potential effects of ExQW on vascular complications, survival, and medical costs over 20 years versus standard therapies. PATIENTS AND METHODS: The Archimedes model was used to assess outcomes for ~25,000 virtual patients with T2DM (NHANES 1999-2006 [metformin ± sulfonylureas, age 57 years, body mass index 33 kg/m(2), weight 94 kg, duration T2DM 9 years, hemoglobin A1c [A1C] 8.1%]). The effects of three treatment strategies were modeled and compared to moderate-adherence insulin therapy: advancement to high-adherence insulin at A1C ≥ 8% (treat to target A1C < 7%) and addition of pioglitazone (PIO) or ExQW from simulation start. ExQW effects on A1C, weight, BP, and lipids were modeled from clinical trial data. Costs, inflated to represent 2010 $US, were derived from Medicare data, Drugstore.com, and publications. As ExQW was investigational, we omitted ExQW, PIO, and insulin pharmacy costs. RESULTS: By year 1, ExQW treatment decreased A1C (~1.5%), weight (~2 kg), and systolic BP (~5 mmHg). PIO and high-adherence insulin decreased A1C by ~1%, increased weight, and did not affect systolic BP. After 20 years, A1C was ~7% with all strategies. ExQW decreased rates of cardiovascular and microvascular complications more than PIO or high-adherence insulin versus moderate-adherence insulin. Over 20 years, ExQW treatment resulted in increased quality-adjusted life-years (QALYs) of ~0.3 years/person and cost savings of $469/life-year versus moderate adherence insulin. For PIO or high-adherence insulin, QALYs were virtually unchanged, and costs/life-year versus moderate-adherence insulin increased by $69 and $87, respectively. CONCLUSIONS: This long-term simulation demonstrated that ExQW treatment may decrease rates of cardiovascular and some microvascular complications of T2DM. Increased QALYs, and decreased costs were also projected.