Characteristics of Long-Stay Patients in a PICU and Healthcare Resource Utilization after Discharge

OBJECTIVES: To examine the characteristics of long-stay patients (LSPs) admitted to a PICU and to investigate discharge characteristics of medical complexity among discharged LSP. DESIGN: We performed a retrospective cohort study where clinical data were collected on all children admitted to our PICU between July 1, 2017, and January 1, 2020. SETTING: A single-center study based at Erasmus MC Sophia Children's Hospital, a level III interdisciplinary PICU in The Netherlands, providing all pediatric and surgical subspecialties. PATIENTS:LSP was defined as those admitted for at least 28 consecutive days. INTERVENTIONS: None. MEASUREMENTS: Length of PICU stay, diagnosis at admission, length of mechanical ventilation, need for extracorporeal membrane oxygenation, mortality, discharge location after PICU and hospital admission, medical technical support, medication use, and involvement of allied healthcare professionals after hospital discharge. MAIN RESULTS: LSP represented a small proportion of total PICU patients (108 patients; 3.2%) but consumed 33% of the total admission days, 47% of all days on extracorporeal membrane oxygenation, and 38% of all days on mechanical ventilation. After discharge, most LSP could be classified as children with medical complexity (CMC) (76%); all patients received discharge medications (median 5.5; range 2-19), most patients suffered from a chronic disease (89%), leaving the hospital with one or more technological devices (82%) and required allied healthcare professional involvement after discharge (93%). CONCLUSIONS: LSP consumes a considerable amount of resources in the PICU and its impact extends beyond the point of PICU discharge since the majority are CMC. This indicates complex care needs at home, high family needs, and a high burden on the healthcare system across hospital borders.</p

Association between fat-free mass and survival in critically ill patients with COVID-19:A prospective cohort study

Background: Most critically ill patients with COVID-19 experience malnutrition and weight loss associated with negative clinical outcomes. Our primary aim was to assess body composition during acute and late phase of illness in these patients in relation to clinical outcome and secondary to tailored nutrition support. Methods: This prospective cohort study included adult critically ill patients with COVID-19. Body composition (fat-free mass [FFM] [exposure of interest], fat mass [FM], skeletal muscle mass [SMM], and phase angle [PA]) was determined with multifrequency bioelectrical impedance analyses in the acute and late phase. Nutrition support data were collected simultaneously. Clinical outcome was defined as intensive care unit (ICU) survival (primary outcome) and 30–90 days thereafter, duration of mechanical ventilation, and length of ICU stay and length of hospital stay (LOS). Nonparametric tests and regression analyses were performed. Results: We included 70 patients (73% male, median age 60 years). Upon admission, median BMI was 30 kg/m 2, 54% had obesity (BMI &gt; 30 kg/m 2). Median weight change during ICU stay was −3 kg: +3 kg FM and −6 kg FFM (−4 kg SMM). Body composition changed significantly (P &lt; 0.001). Regarding clinical outcome, only low PA was associated with prolonged LOS (odds ratio = 0.83, 95% CI = 0.72–0.96; P = 0.015). Patients with optimal protein intake (&gt;80%) during acute phase maintained significantly more FFM (2.7 kg, P = 0.047) in the late phase compared with patients who received &lt;80%. Conclusion: FFM decreased significantly during acute and late phase of illness, but we observed no association with ICU survival. Only low PA was associated with prolonged LOS. FFM wasting likely occurred because of disease severity and immobility.</p

Association between fat-free mass and survival in critically ill patients with COVID-19:A prospective cohort study

Background: Most critically ill patients with COVID-19 experience malnutrition and weight loss associated with negative clinical outcomes. Our primary aim was to assess body composition during acute and late phase of illness in these patients in relation to clinical outcome and secondary to tailored nutrition support. Methods: This prospective cohort study included adult critically ill patients with COVID-19. Body composition (fat-free mass [FFM] [exposure of interest], fat mass [FM], skeletal muscle mass [SMM], and phase angle [PA]) was determined with multifrequency bioelectrical impedance analyses in the acute and late phase. Nutrition support data were collected simultaneously. Clinical outcome was defined as intensive care unit (ICU) survival (primary outcome) and 30–90 days thereafter, duration of mechanical ventilation, and length of ICU stay and length of hospital stay (LOS). Nonparametric tests and regression analyses were performed. Results: We included 70 patients (73% male, median age 60 years). Upon admission, median BMI was 30 kg/m 2, 54% had obesity (BMI &gt; 30 kg/m 2). Median weight change during ICU stay was −3 kg: +3 kg FM and −6 kg FFM (−4 kg SMM). Body composition changed significantly (P &lt; 0.001). Regarding clinical outcome, only low PA was associated with prolonged LOS (odds ratio = 0.83, 95% CI = 0.72–0.96; P = 0.015). Patients with optimal protein intake (&gt;80%) during acute phase maintained significantly more FFM (2.7 kg, P = 0.047) in the late phase compared with patients who received &lt;80%. Conclusion: FFM decreased significantly during acute and late phase of illness, but we observed no association with ICU survival. Only low PA was associated with prolonged LOS. FFM wasting likely occurred because of disease severity and immobility.</p

Assessment of aberrant DNA methylation two years after paediatric critical illness:a pre-planned secondary analysis of the international PEPaNIC trial

Critically ill children requiring intensive care suffer from impaired physical/neurocognitive development 2 y later, partially preventable by omitting early use of parenteral nutrition (early-PN) in the paediatric intensive-care-unit (PICU). Altered methylation of DNA from peripheral blood during PICU-stay provided a molecular basis hereof. Whether DNA-methylation of former PICU patients, assessed 2 y after critical illness, is different from that of healthy children remained unknown. In a pre-planned secondary analysis of the PEPaNIC-RCT (clinicaltrials.gov-NCT01536275) 2-year follow-up, we assessed buccal-mucosal DNA-methylation (Infinium-HumanMethylation-EPIC-BeadChip) of former PICU-patients (N = 406 early-PN; N = 414 late-PN) and matched healthy children (N = 392). CpG-sites differentially methylated between groups were identified with multivariable linear regression and differentially methylated DNA-regions via clustering of differentially methylated CpG-sites using kernel-estimates. Analyses were adjusted for technical variation and baseline risk factors, and corrected for multiple testing (false-discovery-rate <0.05). Differentially methylated genes were functionally annotated (KEGG-pathway database), and allocated to three classes depending on involvement in physical/neurocognitive development, critical illness and intensive medical care, or pre-PICU-admission disorders. As compared with matched healthy children, former PICU-patients showed significantly different DNA-methylation at 4047 CpG-sites (2186 genes) and 494 DNA-regions (468 genes), with most CpG-sites being hypomethylated (90.3%) and with an average absolute 2% effect-size, irrespective of timing of PN initiation. Of the differentially methylated KEGG-pathways, 41.2% were related to physical/neurocognitive development, 32.8% to critical illness and intensive medical care and 26.0% to pre-PICU-admission disorders. Two years after critical illness in children, buccal-mucosal DNA showed abnormal methylation of CpG-sites and DNA-regions located in pathways known to be important for physical/neurocognitive development

Predicting adverse long-term neurocognitive outcomes after pediatric intensive care unit admission

Background and objective: Critically ill children may suffer from impaired neurocognitive functions years after ICU (intensive care unit) discharge. To assess neurocognitive functions, these children are subjected to a fixed sequence of tests. Undergoing all tests is, however, arduous for former pediatric ICU patients, resulting in interrupted evaluations where several neurocognitive deficiencies remain undetected. As a solution, we propose using machine learning to predict the optimal order of tests for each child, reducing the number of tests required to identify the most severe neurocognitive deficiencies. Methods: We have compared the current clinical approach against several machine learning methods, mainly multi-target regression and label ranking methods. We have also proposed a new method that builds several multi-target predictive models and combines the outputs into a ranking that prioritizes the worse neurocognitive outcomes. We used data available at discharge, from children who participated in the PEPaNIC-RCT trial (ClinicalTrials.gov-NCT01536275), as well as data from a 2-year follow-up study. The institutional review boards at each participating site have also approved this follow-up study (ML8052; NL49708.078; Pro00038098). Results: Our proposed method managed to outperform other machine learning methods and also the current clinical practice. Precisely, our method reaches approximately 80% precision when considering top-4 outcomes, in comparison to 65% and 78% obtained by the current clinical practice and the state-of-the-art method in label ranking, respectively. Conclusions: Our experiments demonstrated that machine learning can be competitive or even superior to the current testing order employed in clinical practice, suggesting that our model can be used to severely reduce the number of tests necessary for each child. Moreover, the results indicate that possible long-term adverse outcomes are already predictable as early as at ICU discharge. Thus, our work can be seen as the first step to allow more personalized follow-up after ICU discharge leading to preventive care rather than curative.</p

Predicting adverse long-term neurocognitive outcomes after pediatric intensive care unit admission

Background and objective: Critically ill children may suffer from impaired neurocognitive functions years after ICU (intensive care unit) discharge. To assess neurocognitive functions, these children are subjected to a fixed sequence of tests. Undergoing all tests is, however, arduous for former pediatric ICU patients, resulting in interrupted evaluations where several neurocognitive deficiencies remain undetected. As a solution, we propose using machine learning to predict the optimal order of tests for each child, reducing the number of tests required to identify the most severe neurocognitive deficiencies. Methods: We have compared the current clinical approach against several machine learning methods, mainly multi-target regression and label ranking methods. We have also proposed a new method that builds several multi-target predictive models and combines the outputs into a ranking that prioritizes the worse neurocognitive outcomes. We used data available at discharge, from children who participated in the PEPaNIC-RCT trial (ClinicalTrials.gov-NCT01536275), as well as data from a 2-year follow-up study. The institutional review boards at each participating site have also approved this follow-up study (ML8052; NL49708.078; Pro00038098). Results: Our proposed method managed to outperform other machine learning methods and also the current clinical practice. Precisely, our method reaches approximately 80% precision when considering top-4 outcomes, in comparison to 65% and 78% obtained by the current clinical practice and the state-of-the-art method in label ranking, respectively. Conclusions: Our experiments demonstrated that machine learning can be competitive or even superior to the current testing order employed in clinical practice, suggesting that our model can be used to severely reduce the number of tests necessary for each child. Moreover, the results indicate that possible long-term adverse outcomes are already predictable as early as at ICU discharge. Thus, our work can be seen as the first step to allow more personalized follow-up after ICU discharge leading to preventive care rather than curative.</p

Health-related quality of life of children and their parents 2 years after critical illness

Background: Pediatric intensive care unit (PICU) survivors are at risk for prolonged morbidities interfering with daily life. The current study examined parent-reported health-related quality of life (HRQoL) in former critically ill children and parents themselves and aimed to determine whether withholding parenteral nutrition (PN) in the first week of critical illness affected children’s and parents’ HRQoL 2 years later. Methods: Children who participated in the pediatric early versus late parenteral nutrition in critical illness (PEPaNIC) trial and who were testable 2 years later (n = 1158) were included. Their HRQoL outcomes were compared with 405 matched healthy controls. At PICU admission, childre

Acute stress response in children with meningococcal sepsis: important differences in the growth hormone/insulin-like growth factor I axis between nonsurvivors and survivors

Septic shock is the most severe clinical manifestation of meningococcal disease and is predominantly seen in children under 5 yr of age. Very limited research has been performed to elucidate the alterations of the GH/IGF-I axis in critically ill children. We evaluated the GH/IGF-I axis and the levels of IGF-binding proteins (IGFBPs)