Medications for Children Receiving Intensive Care: A National Sample

Exploring industry stakeholder perspectives on a clinical testbed for evaluating the handling of protein drugs in hospitals

Protein drugs, such as therapeutic antibodies, are complex and require careful handling to maintain their efficacy and quality. Stress factors in hospitals, like temperature variations and mechanical shocks during transport, may negatively impact the stability of protein drugs (e.g. various monoclonal antibodies). The pharmaceutical industry possesses extensive knowledge about their product formulations but often the transfer of knowledge from lab studies into in-hospital handling procedures is challenging. To address this gap and find a way to bridge academia, healthcare, and industry, seven semi-structured interviews were conducted with experts from pharmaceutical companies across five countries. This study aimed to explore the opinions of formulation experts regarding stress evaluation in clinical settings. Thematic analysis of the interviews revealed four key themes: The human factor in clinical sites, clinical sites as data providers, potential complexities in conducting tests within a clinical setting, and challenges associated with product-specific methods, equipment and devices. This study also suggests tools for setting up clinical test beds that can help the pharmaceutical industry improve stress evaluation and understand clinical product handling. Direct collaboration with clinical sites is crucial, as experts perceive improved evaluation methods and education to be necessary for ensuring safe medicines for patients.

Exploring Manipulated Prescribed Medicines for Novel Leads in 3D Printed Personalized Dosage Forms

Background: On-demand personalized drug production is currently not addressed with large-scale drug manufacturing. In our study, we focused primarily on identifying possible active pharmaceutical ingredients (APIs) for 3D Printing (3DP) in the current healthcare setting. Methods: We conducted a retrospective cross-sectional study in the Netherlands using three different sources; community pharmacies (n = 5), elderly care homes (n = 3), and the Erasmus MC Sophia Children's Hospital. The primary endpoint was the percentage of prescriptions of medication manipulated before administration, thereby being a candidate for 3DP. Around a million prescriptions were analyzed in our study. Results: This study shows that around 3.0% of the prescribed drugs dispensed by Dutch community pharmacies were manipulated before administration, while around 10.5% of the prescribed drugs in the Erasmus MC Sophia Children's Hospital were manipulated prior to administration. Conclusions: With our study, we show that the most manipulated drugs come from the groups of constipation, psychopharmaceutical, cardiovascular, and anti-infectant drugs. Successful introduction of a compounded API drug by 3DP does not only rely on the API, but it also comes with an optimal balance between technical, economic as well as societal impact factors. Our study gives direction for potential future research on the introduction of 3DP of medicine in the healthcare setting.

What to consider for successful administration of oral liquids via enteral feeding tubes? a case study with paediatric ibuprofen suspensions

Administration of medications via enteral feeding tubes (EFTs) is a common practice for children who cannot swallow properly. Although liquid formulations are the preferred dosage forms for this route of administration, little attention has been paid to the amount of drug that reaches the site of absorption after administration via an EFT. This systematic in vitro study aimed to identify formulation parameters and administration approaches that are critical for successful dose delivery via EFTs. For this purpose, drug recovery after administration of three different paediatric ibuprofen suspensions via different types of EFTs was studied using derivative UV spectrophotometry for quantification. Study results indicate that in addition to formulation parameters, feeding tube characteristics and the administration process can have a significant impact on the administered dose. The ratio between the total administered fluid volume (TAV), represented by the sum of dose- and flushing volume, and the feeding tube volume (FTV) proved to be a valuable indicator for assessing successful administration. Incorrect dosing and complications could be avoided if the TAV/FTV ratio was greater than 4. This and other knowledge gained in the study will help to make the administration of liquid paediatric medicines via EFTs both more effective and safer.

Hospital survival following pediatric HSCT: changes in complications, ICU therapies and outcomes over 10 years

Introduction

Hematopoietic stem cell transplantation (HSCT) is an increasingly utilized therapy for malignant and non-malignant pediatric diseases. HSCT complications, including infection, organ dysfunction, and graft-versus-host-disease (GVHD) often require intensive care unit (ICU) therapies and are associated with mortality. Our aims were to identify the HSCT characteristics, complications and ICU therapies associated with (1) survival, and (2) survival changes over a ten-year period in a national dataset.

Methods

A national sample from the Health Facts (Cerner Corporation, Kansas City, MO) database from 2009 to 2018 was utilized. Inclusion criteria were age 30 days to <22 years and HSCT procedure code. For patients with >1 HSCT, the first was analyzed. Data included demographics, hospital length of stay (LOS), hospital outcome, transplant type and indication. HSCT complications included GVHD and infections. ICU therapies were positive pressure ventilation (PPV), vasoactive infusion, and dialysis. Primary outcome was survival to discharge. Statistical methods included bivariate analyses and multivariate logistic regression.

Results

473 patients underwent HSCT with 93% survival. 62% were allogeneic (89% survival) and 38% were autologous (98% survival). GVHD occurred in 33% of allogeneic HSCT. Infections occurred in 26% of all HSCT. ICU therapies included PPV (11% of patients), vasoactive (25%), and dialysis (3%). Decreased survival was associated with allogeneic HSCT (p < 0.01), GVHD (p = 0.02), infection (p < 0.01), and ICU therapies (p < 0.01). Survival improved from 89% (2009-2013) to 96% (2014-2018) (p < 0.01). Allogeneic survival improved (82%-94%, p < 0.01) while autologous survival was unchanged. Survival improvement over time was associated with decreasing infections (33%-21%, p < 0.01) and increasing vasoactive infusions (20%-28%, p = 0.05). On multivariate analysis, later time period was associated with improved survival (p < 0.01, adjusted OR 4.28).

Discussion

Hospital survival for HSCT improved from 89% to 96% from 2009 to 2018. Factors associated with mortality included allogeneic HSCT, GVHD, infections and ICU therapies. Improving survival coincided with decreasing infections and increasing vasoactive use.

A point-of-care pharmacokinetic/pharmacodynamic trial in critically ill children: Study design and feasibility

Background

High-quality, efficient, pharmacokinetic (PK), pharmacodynamic (PD), and safety studies in children are needed. Point-of-care trials in adults have facilitated clinical trial participation for patients and providers, minimized the disruption of clinical workflow, and capitalized on routine data collection. The feasibility and value of point-of-care trials to study PK/PD in children are unknown, but appear promising. The Opportunistic PK/PD Trial in Critically Ill Children with Heart Disease (OPTIC) is a programmatic point-of-care approach to PK/PD trials in critically ill children that seeks to overcome barriers of traditional pediatric PK/PD studies to generate safety, efficacy, PK, and PD data across multiple medications, ages, and disease processes.

Methods

This prospective, open-label, non-randomized point-of-care trial will characterize the PK/PD and safety of multiple drugs given per routine care to critically ill children with heart disease using opportunistic and scavenged biospecimen samples and data collected from the electronic health record. OPTIC has one informed consent form with drug-specific appendices, streamlining study structure and institutional review board approval. OPTIC capitalizes on routine data collection through multiple data sources that automatically capture demographics, medications, laboratory values, vital signs, flowsheets, and other clinical data. This innovative automatic data collection minimizes the burden of data collection and facilitates trial conduct. Data will be validated across sources to ensure accuracy of dataset variables.

Discussion

OPTIC's point-of-care trial design and automated data acquisition via the electronic health record may provide a mechanism for conducting minimal risk, minimal burden, high efficiency trials and support drug development in historically understudied patient populations.

Trial registration

clinicaltrials.gov number: NCT05055830. Registered on September 24, 2021.

The Association of the Lactate-Albumin Ratio With Mortality and Multiple Organ Dysfunction in PICU Patients

OBJECTIVES

To compare the relative associations of lactate, albumin, and the lactate-albumin ratio (LAR) measured early in disease course against mortality and prevalence of multiple organ dysfunction syndrome (MODS) in a general sample of critically ill pediatric patients.

DESIGN

Retrospective analysis of the Health Facts (Cerner Corporation, Kansas City, MO) national database.

SETTING

U.S. hospitals with PICUs.

PATIENTS

Children admitted to the ICU ( n = 648) from 2009 to 2018 who had lactate and albumin measured within 6 hours of admission.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

A total of 648 admissions were included, with an overall mortality rate of 10.8% ( n = 70) and a MODS prevalence of 29.3% ( n = 190). Compared with survivors, deaths had higher initial lactates (7.3 mmol/L [2.6-11.7 mmol/L] vs 1.9 mmol/L [1.2-3.1 mmol/L]; p < 0.01), lower initial albumins (3.3 g/dL [2.7-3.8 g/dL] vs 4.2 g/dL [3.7-4.7 g/dL]; p < 0.01), and higher LARs (2.2 [1.0-4.2] vs 0.5 [0.3-0.8]; p < 0.01), with similar trends in patients with MODS versus those without MODS. LAR demonstrated a higher odds ratio (OR) for death than initial lactate alone (2.34 [1.93-2.85] vs 1.29 [1.22-1.38]) and a higher OR for MODS than initial lactate alone (2.10 [1.73-2.56] vs 1.22 [1.16-1.29]). Area under the receiver operating characteristic (AUROC) curve of LAR for mortality was greater than initial lactate (0.86 vs 0.82; p < 0.01). The LAR AUROC for MODS was greater than the lactate AUROC (0.71 vs 0.66; p < 0.01). Trends of lactate, albumin, and LAR for mortality were consistent across several diagnostic subgroups (trauma, primary respiratory failure, toxicology), but not all.

CONCLUSIONS

LAR measured early in the course of critical illness is significantly associated with mortality and development of MODS when compared with initial lactate or initial albumin alone in critically ill pediatric patients.

Intensive care drug therapy and its potential adverse effects on blood pressure and heart rate in critically ill children

BACKGROUND

Owing to complex treatment, critically ill children may experience alterations in their vital parameters. We investigated whether such hemodynamic alterations were temporally and causally related to drug therapy.

METHODS

In a university pediatric intensive care unit, we retrospectively analyzed hemodynamic alterations defined as values exceeding the limits set for heart rate (HR) and blood pressure (BP). For causality assessment, we used the World Health Organization-Uppsala Monitoring Center (WHO-UMC) system, which categorizes the probability of causality as "certain," "probable," "possible," and "unlikely."

RESULTS

Of 315 analyzed patients with 43,200 drug prescriptions, 59.7% experienced at least one hemodynamic alteration; 39.0% were affected by increased HR, 19.0% by decreased HR, 18.1% by increased BP, and 16.2% by decreased BP. According to drug information databases, 83.9% of administered drugs potentially lead to hemodynamic alterations. Overall, 88.3% of the observed hemodynamic alterations had a temporal relation to the administration of drugs; in 80.2%, more than one drug was involved. Based on the WHO-UMC system, a drug was rated as a "probable" causing factor for only 1.4% of hemodynamic alterations. For the remaining alterations, the probability ratings were lower because of multiple potential causes, e.g., several drugs.

CONCLUSIONS

Critically ill children were frequently affected by hemodynamic alterations. The administration of drugs with potentially adverse effects on hemodynamic parameters is often temporally related to hemodynamic alterations. Hemodynamic alterations are often multifactorial, e.g., due to administering multiple drugs in rapid succession; thus, the influence of individual drugs cannot easily be captured with the WHO-UMC system.

Dynamic Mortality Risk Predictions for Children in ICUs: Development and Validation of Machine Learning Models

OBJECTIVES

Assess a machine learning method of serially updated mortality risk.

DESIGN

Retrospective analysis of a national database (Health Facts; Cerner Corporation, Kansas City, MO).

SETTING

Hospitals caring for children in ICUs.

PATIENTS

A total of 27,354 admissions cared for in ICUs from 2009 to 2018.

INTERVENTIONS

None.

MAIN OUTCOME

Hospital mortality risk estimates determined at 6-hour time periods during care in the ICU. Models were truncated at 180 hours due to decreased sample size secondary to discharges and deaths.

MEASUREMENTS AND MAIN RESULTS

The Criticality Index, based on physiology, therapy, and care intensity, was computed for each admission for each time period and calibrated to hospital mortality risk (Criticality Index-Mortality [CI-M]) at each of 29 time periods (initial assessment: 6 hr; last assessment: 180 hr). Performance metrics and clinical validity were determined from the held-out test sample (n = 3,453, 13%). Discrimination assessed with the area under the receiver operating characteristic curve was 0.852 (95% CI, 0.843-0.861) overall and greater than or equal to 0.80 for all individual time periods. Calibration assessed by the Hosmer-Lemeshow goodness-of-fit test showed good fit overall (p = 0.196) and was statistically not significant for 28 of the 29 time periods. Calibration plots for all models revealed the intercept ranged from--0.002 to 0.009, the slope ranged from 0.867 to 1.415, and the R2 ranged from 0.862 to 0.989. Clinical validity assessed using population trajectories and changes in the risk status of admissions (clinical volatility) revealed clinical trajectories consistent with clinical expectations and greater clinical volatility in deaths than survivors (p < 0.001).

CONCLUSIONS

Machine learning models incorporating physiology, therapy, and care intensity can track changes in hospital mortality risk during intensive care. The CI-M's framework and modeling method are potentially applicable to monitoring clinical improvement and deterioration in real time.

Association of Intravenous Acetaminophen Administration With the Duration of Intravenous Opioid Use Among Hospitalized Pediatric Patients

Importance

Adoption of multimodal pain regimens that incorporate nonopioid analgesic medications to reduce inpatient opioid administration can prevent serious opioid-related adverse effects in children, including tolerance, withdrawal, delirium, and respiratory depression. Intravenous (IV) acetaminophen is in widespread pediatric use; however, its effectiveness as an opioid-sparing agent has not been evaluated in general pediatric inpatients.

Objective

To determine if IV acetaminophen administered prior to IV opioids is associated with a reduction in the total duration of IV opioids administered compared with IV opioids administered without IV acetaminophen in general pediatric inpatients.

Design, Setting, and Participants

This comparative effectiveness research study included data on pediatric inpatients from 274 US hospitals between January 2011 and June 2016 collected from a national database. Outcomes were compared with a propensity score-matched analysis of pediatric inpatients administered IV opioids without IV acetaminophen (control) and those administered IV acetaminophen prior to IV opioids (intervention). Data were analyzed from January 2020 through October 2021.

Exposures

Patients in the intervention group received IV acetaminophen prior to IV opioids. Patients in the control group received IV opioids without IV acetaminophen.

Main Outcomes and Measures

Total duration of all IV opioids administered during a patient's hospitalization.

Results

Of 893 293 pediatric inpatients, a total of 104 579 were included in analysis (median [IQR] age, 1.3 [0-14.7] years; 59 806 [57.2%] female; 21 485 [21.5%] African American, 56 309 [53.8%] White), of whom 18 197 (2.0%) received IV acetaminophen, and 287 504 (34.0%) received IV opioids. After applying exclusion criteria, among patients who received IV acetaminophen, 1739 (10.8%) received IV acetaminophen prior to IV opioids within a median (IQR) treatment time of 1.5 (0.02-7.3) hours. After propensity score matching produced comparable groups in the control and intervention groups (with 839 patients in each group), the multivariable model estimated a 15.5% shorter duration of IV opioid use in the intervention group, with an absolute IV opioid reduction of 7.5 hours (95% CI, 0.7-15.8 hours).

Conclusions and Relevance

In this comparative effectiveness study, IV acetaminophen administered prior to IV opioids was associated with a reduction in IV opioid duration by 15.5%. Multimodal pain regimens that use IV acetaminophen prior to IV opioids could reduce IV opioid duration.

Predicting Future Care Requirements Using Machine Learning for Pediatric Intensive and Routine Care Inpatients

Supplemental Digital Content is available in the text.

OBJECTIVES:

Develop and compare separate prediction models for ICU and non-ICU care for hospitalized children in four future time periods (6–12, 12–18, 18–24, and 24–30 hr) and assess these models in an independent cohort and simulated children’s hospital.

DESIGN:

Predictive modeling used cohorts from the Health Facts database (Cerner Corporation, Kansas City, MO).

SETTING:

Children hospitalized in ICUs.

PATIENTS:

Children with greater than or equal to one ICU admission (n = 20,014) and randomly selected routine care children without ICU admission (n = 20,130) from 2009 to 2016 were used for model development and validation. An independent 2017–2018 cohort consisted of 80,089 children.

INTERVENTIONS:

None.

MEASUREMENT AND MAIN RESULTS:

Initially, we undersampled non-ICU patients for development and comparison of the models. We randomly assigned 64% of patients for training, 8% for validation, and 28% for testing in both clinical groups. Two additional validation cohorts were tested: a simulated children’s hospitals and the 2017–2018 cohort. The main outcome was ICU care or non-ICU care in four future time periods based on physiology, therapy, and care intensity. Four independent, sequential, and fully connected neural networks were calibrated to risk of ICU care at each time period. Performance for all models in the test sample were comparable including sensitivity greater than or equal to 0.727, specificity greater than or equal to 0.885, accuracy greater than 0.850, area under the receiver operating characteristic curves greater than or equal to 0.917, and all had excellent calibration (all R²s > 0.98). Model performance in the 2017–2018 cohort was sensitivity greater than or equal to 0.545, specificity greater than or equal to 0.972, accuracy greater than or equal to 0.921, area under the receiver operating characteristic curves greater than or equal to 0.946, and R²s greater than or equal to 0.979. Performance metrics were comparable for the simulated children’s hospital and for hospitals stratified by teaching status, bed numbers, and geographic location.

CONCLUSIONS:

Machine learning models using physiology, therapy, and care intensity predicting future care needs had promising performance metrics. Notably, performance metrics were similar as the prediction time periods increased from 6–12 hours to 24–30 hours.

The Association of Laboratory Test Abnormalities With Mortality Risk in Pediatric Intensive Care

OBJECTIVES

To determine the bivariable associations between abnormalities of 28 common laboratory tests and hospital mortality and determine how mortality risks changes when the ranges are evaluated in the context of commonly used laboratory test panels.

DESIGN

A 2009-2016 cohort from the Health Facts (Cerner Corporation, Kansas City, MO) database.

SETTING

Hospitals caring for children in ICUs.

PATIENTS

Children cared for in ICUs with laboratory data.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

There were 2,987,515 laboratory measurements in 71,563 children. The distribution of laboratory test values in 10 groups defined by population percentiles demonstrated the midrange of tests was within the normal range except for those measured predominantly when significant abnormalities are suspected. Logistic regression analysis at the patient level combined the population-based groups into ranges with nonoverlapping mortality odds ratios. The most deviant test ranges associated with increased mortality risk (mortality odds ratios > 5.0) included variables associated with acidosis, coagulation abnormalities and blood loss, immune function, liver function, nutritional status, and the basic metabolic profile. The test ranges most associated with survival included normal values for chloride, pH, and bicarbonate/total Co2. When the significant test ranges from bivariable analyses were combined in commonly used test panels, they generally remained significant but were reduced as risk was distributed among the tests.

CONCLUSIONS

The relative importance of laboratory test ranges vary widely, with some ranges strongly associated with mortality and others strongly associated with survival. When evaluated in the context of test panels rather than isolated tests, the mortality odds ratios for the test ranges decreased but generally remained significant as risk was distributed among the components of the test panels. These data are useful to develop critical values for children in ICUs, to identify risk factors previously underappreciated, for education and training, and for future risk score development.

Severity Trajectories of Pediatric Inpatients Using the Criticality Index

OBJECTIVES

To assess severity of illness trajectories described by the Criticality Index for survivors and deaths in five patient groups defined by the sequence of patient care in ICU and routine patient care locations.

DESIGN

The Criticality Index developed using a calibrated, deep neural network, measures severity of illness using physiology, therapies, and therapeutic intensity. Criticality Index values in sequential 6-hour time periods described severity trajectories.

SETTING

Hospitals with pediatric inpatient and ICU care.

PATIENTS

Pediatric patients never cared for in an ICU (n = 20,091), patients only cared for in the ICU (n = 2,096) and patients cared for in both ICU and non-ICU care locations (n = 17,023) from 2009 to 2016 Health Facts database (Cerner Corporation, Kansas City, MO).

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Criticality Index values were consistent with clinical experience. The median (25-75th percentile) ICU Criticality Index values (0.878 [0.696-0.966]) were more than 80-fold higher than the non-ICU values (0.010 [0.002-0.099]). Non-ICU Criticality Index values for patients transferred to the ICU were 40-fold higher than those never transferred to the ICU (0.164 vs 0.004). The median for ICU deaths was higher than ICU survivors (0.983 vs 0.875) (p < 0.001). The severity trajectories for the five groups met expectations based on clinical experience. Survivors had increasing Criticality Index values in non-ICU locations prior to ICU admission, decreasing Criticality Index values in the ICU, and decreasing Criticality Index values until hospital discharge. Deaths had higher Criticality Index values than survivors, steeper increases prior to the ICU, and worsening values in the ICU. Deaths had a variable course, especially those who died in non-ICU care locations, consistent with deaths associated with both active therapies and withdrawals/limitations of care.

CONCLUSIONS

Severity trajectories measured by the Criticality Index showed strong validity, reflecting the expected clinical course for five diverse patient groups.