Length of stay and efficiency in pediatric intensive care units

VEEG monitoring and electrographic seizures in 232 pediatric patients in ICU at a tertiary hospital in China

Objectives

To investigate neonatal electroencephalography (EEG) background activity and electrographic seizures in patients in the pediatric intensive care unit (PICU) who underwent bedside video-electroencephalography (vEEG) monitoring.

Methods

A total of 232 pediatric patients admitted or transferred to PICU that underwent vEEG monitoring were retrospectively enrolled in this study, and electrographic status epilepticus was observed after vEEG monitoring.

Results

The median age was 1.56 years [95% confidence interval (CI) = 1.12-2.44]. Electrographic seizures occurred in 88 patients (37.9%), out of which 36 cases (40.9%) had electrographic status epilepticus. Prior epileptic encephalopathy diagnosis [odds ratio (OR) = 6.57, 95% CI = 1.91-22.59, p = 0.003], interictal epileptiform discharges (OR = 46.82, 95%CI = 5.31-412.86, p = 0.0005), slow disorganized EEG background (OR = 11.92, 95%CI = 1.31-108.71, p = 0.028), and burst-suppression EEG background (OR = 23.64, 95%CI = 1.71-327.57, p = 0.018) were the risk factors for electrographic seizures' occurrence. Of the 232 patients, the condition of 179 (77.2%) patients improved and they were discharged, 34 cases (14.7%) were withdrawn, and 18 cases (7.8%) died. The in-hospital death rate was 47.6% (10 in 21 cases) in patients with attenuated/featureless, compared to 0/23 with normal EEG background.

Conclusions

Electrographic status epilepticus occurs in more than one-third of patients with electrographic seizures. vEEG is an efficient method to determine electrographic seizures in children. Abnormal EEG background activity is associated with both electrographic seizures' occurrence and unfavorable in-hospital outcomes.

Relationship between Pediatric ICU Length of Stay and 24-Hour-Unplanned Readmission Rate

OBJECTIVE

To evaluate the relationship between Pediatric Intensive Care Unit (PICU) Severity-Adjusted Length of Stay (LOS) and 24-hour-unplanned readmission rate.

DATA SOURCE

10-year cohort from 2009-2018 from the Virtual Pediatric Systems (VPS, LLC) database.

STUDY DESIGN

In this retrospective study, Standardized Length of Stay Ratio was computed for each Pediatric Intensive Care Unit as the ratio of the sum of actual Length of Stay divided by the predicted Length Of Stay for each Pediatric Intensive Care Unit using VPS predictive length of stay model. Correlation between Standardized Length of Stay Ratios and 24-hour-unplanned readmission rates were computed using Pearson's correlation coefficient.

PRINCIPAL FINDINGS

There was practically no relationship between Standardized Length of Stay Ratio and 24-hour readmission rate (R² = 0.05).

DATA COLLECTION/EXTRACTION METHODS

Not Applicable CONCLUSIONS: Severity-Adjusted Length of Stay has no relationship with 24-hour- unplanned readmission rate. These findings suggest that the relationship between PICU severity adjusted LOS and 24-hour- unplanned readmission rate should not be used as a balancing quality measure.

Pediatric Risk of Mortality III Score - Predictor of Mortality and Hospital Stay in Pediatric Intensive Care Unit

Background

Pediatric Risk of Mortality (PRISM) III score is one of the widely used scoring systems to quantify critical illness in the pediatric age group. This study was carried out to find the association of PRISM III score with the outcome (discharge/mortality) and also hospital stay in survivors and nonsurvivors.

Setting

The study was conducted in a tertiary care hospital from January 2014 to June 2015.

Materials and Methods

A total of 524 patients were admitted, and after excluding the patients who met the exclusion criteria, 486 patients were analyzed.

Statistical Analysis

Logistic regression was used to find the association of variables under the PRISM III score with mortality. Linear regression was used to find the association of PRISM III score with length of stay.

Results

Mortality was 31%; male: female ratio was 1.5:1. Maximum patients presented with respiratory system involvement (26.3%), and maximum mortality (20.3%) was observed in the patients with respiratory involvement. Discrimination by the model between mortality and survival was excellent (receiver operating characteristic curve [0.903]). Maximum risk of mortality was noticed in mechanically ventilated patients (odds ratio [OR]: 10.87) followed by lower systolic blood pressure (OR: 2.72), deranged prothrombin time, partial thromboplastin time (OR: 1.50), deranged mental status (OR: 1.41), and tachycardia (OR: 1.37). Length of stay (LOS) in patients increased till PRISM III score of 25. Average LOS in survivors was 4.327 days which was not accounted by difference in PRISM III score between different patients. With each unit increase in PRISM III score, LOS increased by 5 h.

Conclusions

PRISM III score has excellent capacity to discriminate between survival and mortality. PRISM III score can be used to predict LOS among survivors.

Long-stay pediatric patients in Japanese intensive care units: their significant presence and a newly developed, simple predictive score

Background

The length of stay (LOS) in intensive care units (ICUs) has been used as a good indicator not only for resource consumption but also for health outcomes of patients. However, data regarding pediatric LOS in Japanese ICUs are limited. The primary aim of this study was to characterize the Japanese pediatric ICU patients based on their LOS. Second, we aimed to develop a simple scoring system to predict long-stay pediatric ICU patients on admission.

Methods

We performed a retrospective cohort study using consecutive pediatric data (aged < 16 years) registered in the Japanese Registry of Pediatric Acute Care (JaRPAC) from October 2013 to September 2016, which consisted of descriptive and diagnostic information. The factors for long-stay patients (LSPs; LOS > 14 days) were identified using multiple regression analysis, and subsequently, a simple predictive scoring system was developed based on the results. The validity of the score was prospectively tested using data from the JaRPAC registration from October 2016 to September 2017.

Results

Overall, 4107 patients were included. Although LSPs were few (8.0% [n = 330]), they consumed 38.0% of ICU bed days (9750 for LSPs versus 25,659 overall). Mortality was seven times higher in LSPs than in short-stay patients (9.1% versus 1.3%). An 11-variable simple predictive scoring system was constructed, including Pediatric Index of Mortality 2 ≥ 1 (2 points), liver dysfunction (non-post operation) (2 points), post-cardiopulmonary resuscitation (1 point), circulatory disorder (1 point), post-operative management of liver transplantation (1 point), encephalitis/encephalopathy (1 point), myocarditis/cardiomyopathy (1 point), congenital heart disease (non-post operation) (1 point), lung tissue disease (1 point), Pediatric Cerebral Performance Category scores ≥ 2 (1 point), and age < 2 years (1 point). A score of ≥ 3 points yielded an area under the receiver operating characteristic curve (AUC) of 0.79, sensitivity of 87.0%, and specificity of 59.4% in the original dataset. Reproducibility was confirmed with the internal validation dataset (AUC 0.80, sensitivity 92.6%, and specificity 60.2%).

Conclusions

Pediatric LSPs possess a significant presence in Japanese ICUs with high rates of bed utilization and mortality. The newly developed predictive scoring system may identify pediatric LSPs on admission.

Electronic supplementary material

The online version of this article (10.1186/s40560-019-0392-2) contains supplementary material, which is available to authorized users.

PICU Length of Stay: Factors Associated With Bed Utilization and Development of a Benchmarking Model

OBJECTIVES

ICU length of stay is an important measure of resource use and economic performance. Our primary aims were to characterize the utilization of PICU beds and to develop a new model for PICU length of stay.

DESIGN

Prospective cohort. The main outcomes were factors associated with PICU length of stay and the performance of a regression model for length of stay.

SETTING

Eight PICUs.

PATIENTS

Randomly selected patients (newborn to 18 yr) from eight PICUs were enrolled from December 4, 2011, to April 7, 2013. Data consisted of descriptive, diagnostic, physiologic, and therapeutic information.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

The mean length of stay for was 5.0 days (SD, 11.1), with a median of 2.0 days. The 50.6% of patients with length of stay less than 2 days consumed only 11.1% of the days of care, whereas the 19.6% of patients with length of stay 4.9-19 days and the 4.6% with length of stay greater than or equal to 19 days consumed 35.7% and 37.6% of the days of care, respectively. Longer length of stay was observed in younger children, those with cardiorespiratory disease, postintervention cardiac patients, and those who were sicker assessed by Pediatric Risk of Mortality scores receiving more intensive therapies. Patients in the cardiac ICU stayed longer than those in the medical ICU. The length of stay model using descriptive, diagnostic, severity, and therapeutic factors performed well (patient-level R-squared of 0.42 and institution-level R-squared of 0.76). Standardized (observed divided by expected) length of stay ratios at the individual sites ranged from 0.87 to 1.09.

CONCLUSIONS

PICU bed utilization was dominated by a minority of patients. The 5% of patients staying the longest used almost 40% of the bed days. The multivariate length of stay model used descriptive, diagnostic, therapeutic, and severity factors and has potential applicability for internal and external benchmarking.

Variation in NICU Admission Rates Without Identifiable Cause

OBJECTIVES

Admission to the NICU is influenced by physiologic compromise and by hospital care protocols. Providing appropriate care must be balanced with adverse consequences of NICU admission, such as interrupting maternal-infant bonding and unnecessary interventions. This study aims to determine the variation in NICU admissions in term and late preterm infants among 19 hospitals.

METHODS

We used the Consortium on Safe Labor (CSL) database to determine NICU admission rates. This database includes data from 217 442 infants aged 35 to 42 weeks within 19 US maternal delivery hospitals from 2002 to 2008. NICU admission rates were evaluated for absolute factors including, but not limited to, sepsis, asphyxia, respiratory distress, and intracranial hemorrhage, as well as relative factors, such as maternal drug use, chorioamnionitis, and infant birth weight ≤ 2500 g.

RESULTS

Percentage of infants 35 to 42 weeks' gestation admitted to the NICU without an identifiable absolute or relative cause for intensive care services ranged from 0% to 59.4% (mean, 10.8%; P < .001). Among infants 35 to 42 weeks' gestation and ≥ 2500 g, infants without absolute or relative identified cause accounted for 9.1% of total NICU days and had lower length of stays (-2.7 days; 95% confidence interval -3.4; -2.1) compared to those with an identified reason.

CONCLUSIONS

There is significant variation in admission rates among NICUs that cannot be explained by infant health conditions. Further analysis is needed to determine the cause of between-site variation and potential opportunities to refine protocols and optimize use of NICU services.

Pediatric Intensive Care Databases for Quality Improvement

The availability and breadth of collected data has grown exponentially in pediatric critical care medicine. This growth is driven by the practitioners' desire to understand and improve practice. In this manuscript, the author details the registry design factors that must be considered to meet quality improvement and safety needs in pediatric critical care units. The challenges to maintain a high standard database and data on health care delivery performances using the VPS registry data are provided.

Evaluating processes of care and outcomes of children in hospital (EPOCH): study protocol for a randomized controlled trial

Background

The prevention of near and actual cardiopulmonary arrest in hospitalized children is a patient safety imperative. Prevention is contingent upon the timely identification, referral and treatment of children who are deteriorating clinically. We designed and validated a documentation-based system of care to permit identification and referral as well as facilitate provision of timely treatment. We called it the Bedside Paediatric Early Warning System (BedsidePEWS). Here we describe the rationale for the design, intervention and outcomes of the study entitled Evaluating Processes and Outcomes of Children in Hospital (EPOCH).

Methods/Design

EPOCH is a cluster-randomized trial of the BedsidePEWS. The unit of randomization is the participating hospital. Eligible hospitals have a Pediatric Intensive Care Unit (PICU), are anticipated to have organizational stability throughout the study, are not using a severity of illness score in hospital wards and are willing to be randomized. Patients are >37 weeks gestational age and <18 years and are hospitalized in inpatient ward areas during all or part of their hospital admission.

Randomization is to either BedsidePEWS or control (no severity of illness score) in a 1:1 ratio within two strata (<200, ≥200 hospital beds). All-cause hospital mortality is the selected primary outcome. It is objective, independent of do-not-resuscitate status and can be reliably measured. The secondary outcomes include (1) clinical outcomes: clinical deterioration, severity of illness at and during ICU admission, and potentially preventable cardiac arrest; (2) processes of care outcomes: immediate calls for assistance, hospital and ICU readmission, and perceptions of healthcare professionals; and (3) resource utilization: ICU days and use of ICU therapies.

Discussion

Following funding by the Canadian Institutes of Health Research and local ethical approvals, site enrollment started in 2010 and was closed in February 2014. Patient enrollment is anticipated to be complete in July 2015. The results of EPOCH will strengthen the scientific basis for local, regional, provincial and national decision-making and for the recommendations of national and international bodies. If negative, the costs of hospital-wide implementation can be avoided. If positive, EPOCH will have provided a scientific justification for the major system-level changes required for implementation.

Trial registration: NCT01260831 ClinicalTrials.gov date: 14 December 2010.

Electronic supplementary material

The online version of this article (doi:10.1186/s13063-015-0712-3) contains supplementary material, which is available to authorized users.

Hypertension and health outcomes in the PICU

OBJECTIVES

Reports of the burden of hypertension in hospitalized children are emerging, but the prevalence and significance of this condition within the PICU are not well understood. The aims of this study were to validate a definition of hypertension in the PICU and assess the associations between hypertension and acute kidney injury, PICU length of stay, and mortality.

DESIGN AND SETTING

Single-center retrospective study using a database of PICU discharges between July 2011 and February 2013.

PATIENTS

All children discharged from the PICU with length of stay more than 6 hours, aged 1 month through 17 years. Exclusions were traumatic brain injury, incident renal transplant, or hypotension.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Potential definitions of hypertension utilizing combinations of standardized cutoff percentiles, durations, initiation or dose escalation of antihypertensives, and/or billing diagnosis codes for hypertension were compared using receiver operator characteristic curves against a manual medical record review. Multivariable logistic and linear regression analyses were conducted using the selected definition of hypertension to assess its independent association with acute kidney injury and PICU length of stay, respectively. A definition requiring three systolic and/or diastolic readings above standardized 99th percentiles plus 5 mm Hg over 1 day was selected (area under the curve, 0.91; sensitivity, 94%; specificity, 87%). Among the 1,215 patients in this analysis, the prevalence of hypertension was 25%. Hypertension was independently associated with acute kidney injury (odds ratio, 2.89; 95% CI, 1.64-5.09; p < 0.01) and increased PICU length of stay (1.50 d; 95% CI, 0.94-2.05; p < 0.01) in multivariable analyses. Deaths were rare-0 in the normotension group and 3 (1%) in the hypertension group-but were statistically different (p = 0.02).

CONCLUSIONS

Hypertension is common in the PICU and is associated with worse clinical outcomes. Future studies are needed to confirm these results.

Potassium abnormalities in a pediatric intensive care unit: frequency and severity

BACKGROUND

Potassium abnormalities are common in critically ill patients. We describe the spectrum of potassium abnormalities in our tertiary-level pediatric intensive care unit (PICU).

METHODS

Retrospective observational cohort of all the patients admitted to a single-center tertiary PICU over a 1-year period. Medical records and laboratory results were obtained through a central electronic data repository.

RESULTS

A total of 512 patients had a potassium measurement. Of a total of 4484 potassium measurements, one-third had abnormal values. Hypokalemia affected 40% of the admissions. Mild hypokalemia (3-3.4 mmol/L) affected 24% of the admissions. Moderate or severe hypokalemia (K <3.0 mmol/L) affected 16% of the admissions. Hyperkalemia affected 29% of the admissions. Mild hyperkalemia (5.1-6.0 mmol/L) affected 17% of the admissions. Moderate or severe hyperkalemia (>6.0 mmol/L) affected 12%. Hemolysis affected 2% of all the samples and 24% of hyperkalemic values. On univariate analysis, severity of hypokalemia was associated with mortality (odds ratio 2.2, P = .003).

CONCLUSIONS

Mild potassium abnormalities are common in the PICU. Repeating hemolyzed hyperkalemic samples may be beneficial. Guidance in monitoring frequencies of potassium abnormalities in pediatric critical care is needed.

Direct observation of bed utilization in the pediatric intensive care unit

BACKGROUND

The pediatric intensive care unit (PICU), with limited number of beds and resource-intensive services, is a key component of patient flow. Because the PICU is a crossroads for many patients, transfer or discharge delays can negatively impact a patient's clinical status and efficiency.

OBJECTIVE

The objective of this study was to describe, using direct observation, PICU bed utilization.

METHODS

We conducted a real-time, prospective observational study in a convenience sample of days in the PICU of an urban, tertiary-care children's hospital.

RESULTS

Among 824 observed hours, 19,887 bed-hours were recorded, with 82% being for critical care services and 18% for non-critical care services. Fourteen activities accounted for 95% of bed-hours. Among 200 hours when the PICU was at full capacity, 75% of the time included at least 1 bed that was used for non-critical care services; 37% of the time at least 2 beds. The mean waiting time for a floor bed assignment was 9 hours (median, 5.5 hours) and accounted for 4.62% of all bed-hours observed.

CONCLUSIONS

The PICU delivered critical care services most of the time, but periods of non-critical care services represented a significant amount of time. In particular, periods with no bed available for new patients were associated with at least 1 or more PICU beds being used for non-critical care activities. The method should be reproducible in other settings to learn more about the structure and processes of care and patient flow and to make improvements.

Are all ICUs the same?

The ability to compare intensive care units (ICUs) and determine whether they provide the same level of care with regard to efficacy, efficiency, and quality is a cornerstone of understanding critical care and improving the quality of that care. Without collecting high-quality data, adjusted for severity of illness and analyzed in a comparative fashion, it would not be possible to describe best practices objectively, to identify which ICUs are doing a good job or to learn from those units that are. This review article discusses how and why ICUs are compared. Particular attention is focused on the severity of illness scores, standardized mortality, and comparative reporting. A data collecting network, Virtual Pediatric Systems, limited liability corporation (VPS, LLC), designed for the purposes of determining where differences in critical care can be identified and the value that this adds in improving quality is discussed. Finally, results from this large data sharing collaborative describing the practice of pediatric critical care are included for the purpose of pediatric intensive care units practice benchmarks.

Variation in duration of respiratory support among Australian and New Zealand pediatric intensive care units

OBJECTIVE

To test if there was significant variation of mean adjusted duration of respiratory support (RS) at the site level among Australian and New Zealand pediatric intensive care units (PICUs) and to determine whether the pattern of variation differed from the site-level pattern of variation in length of stay (LoS).

DESIGN

Separate outcome prediction models for estimating PICU LoS and duration of RS were constructed, using patient data collected between 2002 and 2007. Gamma regression was used to model LoS for all admissions included in the study population, and log normal regression was used to model duration of RS for the subset of patients receiving RS. For both models, case-mix adjustment was achieved by entering patient risk factors as fixed effects, and the PICU or site of care was entered as a random effect.

SETTING

Data for 31,358 admissions were collected from nine specialist PICUs in Australia and New Zealand.

MEASUREMENTS AND MAIN RESULTS

Average risk-adjusted duration of RS and LoS for each PICU. There was significant unit-level variation in the adjusted mean LoS and duration of RS among PICUs in Australia and New Zealand. One site had a mean duration of RS that was significantly longer than expected, whereas two sites had a mean duration of RS that was significantly shorter than expected at the 95% level. Unit-level variation in duration of RS is consistent with unit-level variation in LoS for six PICUs and significantly different in two units.

CONCLUSION

There is unit-level variation in LoS and duration of RS, not accounted for by case-mix. Concurrent analysis of unit-level variation in LoS and duration of RS can help to identify differences in discharge practice and provide direction for improvements in clinical or administrative efficiency.

Comorbid illnesses among critically ill hospitalized children: Impact on hospital resource use and mortality, 1997-2006

OBJECTIVES

To describe and compare hospital resource use and in-hospital mortality among critically ill hospitalized children according to comorbid illness status.

DESIGN

Secondary analysis of administrative data with generation of national estimates.

SETTING

None.

PATIENTS

Hospitalized children 0 to 18 yrs old with receipt of critical care services between 1997 and 2006.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

There were 24,954 and 45,521 child hospitalizations with receipt of critical care services nationally in 1997 and 2006, respectively. In 1997, 35% of these hospitalizations had comorbid illnesses and 65% of these were in teaching hospitals. In 2006, 41% of critical care hospitalizations had comorbid illnesses, and 82% were in teaching hospitals. Cardiovascular diseases were the most common comorbid illnesses in 1997 (18%) and 2006 (22%). Mortality was significantly higher among patients with comorbid illness versus those without in 1997 (12.5% vs. 8.6%; p < .01) and in 2006 (10.8% vs. 7.8%; p < .01). Critically ill patients with comorbid illness vs. those without had significantly longer hospital stay in 1997 (30 days vs. 15 days; p < .01) and in 2006 (26 days vs. 14 days; p < .01). Corresponding charges were also significantly higher in the presence of comorbid illnesses vs. without, in 1997 ($131,203 vs. $62,070; p < .01) and in 2006 ($141,586 vs. $70,532; p < .01), expressed in 2006 U.S dollars. Across the 10-yr study period, hospital mortality was higher and hospital resource use greater among children with comorbid illness than children without.

CONCLUSIONS

Among pediatric hospitalizations requiring use of critical care services, comorbid illness was associated with significantly higher in-hospital mortality and significantly greater hospital resource use pattern predominantly occurring in teaching hospitals. Policymaking regarding child critical care service delivery should anticipate exacerbation of these trends in the future, which have implications for bed availability and the overall acuity level in critical care settings.

Characteristics associated with pediatric inpatient death

OBJECTIVE

The primary objective of this study was to obtain a broad understanding of inpatient deaths across academic children's hospitals.

METHODS

A nonconcurrent cohort study of children hospitalized in 37 academic children's hospitals in 2005 was performed. The primary outcome was death. Patient characteristics including age, gender, race, diagnostic grouping, and insurance status and epidemiological measures including standardized mortality rate and standardized mortality ratios (SMRs) were used.

RESULTS

A total of 427 615 patients were discharged during the study period, of whom 4529 (1.1%) died. Neonates had the highest mortality rate (4.03%; odds ratio: 8.66; P < .001), followed by patients >18 years of age (1.4%; odds ratio: 2.86; P < .001). The SMRs ranged from 0.46 (all patient-refined, diagnosis-related group 663, other anemias and disorders of blood) to 30.0 (all patient-refined, diagnosis-related group 383, cellulitis and other bacterial skin infections). When deaths were compared according to institution, there was considerable variability in both the number of children who died and the SMRs at those institutions.

CONCLUSIONS

Patient characteristics, such as age, severity, and diagnosis, were all substantive factors associated with the death of children. Opportunities to improve the environment of care by reducing variability within and between hospitals may improve mortality rates for hospitalized children.

Measuring efficiency in Australian and New Zealand paediatric intensive care units

PURPOSE

To develop a measure of paediatric intensive care unit (PICU) efficiency and compare the efficiency of PICUs in Australia and New Zealand.

METHODS

Separate outcome prediction models for estimating clinical performance and resource usage were constructed using patient data from 20,742 admissions between 2005 and 2007. A standardised mortality ratio was calculated using a recalibrated Paediatric Index of Mortality 2 model. A random effects length of stay (LoS) prediction model was used to provide an indicator of unit-level variation in resource use. A modified Rapoport-Teres plot of risk-adjusted mortality versus unit mean LoS provided a visual representation of efficiency. To account for potential differences in admission threshold, the calculation of performance measures was repeated on patients receiving mechanical respiratory support and compared to those estimated for all patients.

RESULTS

The modified plot provides a useful tool for visualising ICU efficiency. Two units were identified as potentially inefficient with higher SMR and risk-adjusted mean LoS at the 95% level. One unit had a significantly lower SMR and significantly higher risk-adjusted mean LoS. The measures for both SMR and risk-adjusted mean LoS showed good agreement between all patients and those who received mechanical respiratory support.

CONCLUSION

There is significant variation in efficiency among PICUs in Australia and New Zealand. Two units were designated as inefficient and one unit was considered to be effective at the expense of high resource use. Application of these methods may help to identify inefficiencies in units located in other countries or regions.

Children's hospitals do not acutely respond to high occupancy

OBJECTIVE

High hospital occupancy may lead to overcrowding in emergency departments and inpatient units, having an adverse impact on patient care. It is not known how children's hospitals acutely respond to high occupancy. The objective of this study was to describe the frequency, direction, and magnitude of children's hospitals' acute responses to high occupancy.

METHODS

Patients who were discharged from 39 children's hospitals that participated in the Pediatric Health Information System database during 2006 were eligible. Midnight census data were used to construct occupancy levels. Acute response to high occupancy was measured by 8 variables, including changes in hospital admissions (4 measures), transfers (2 measures), and length of stay (2 measures).

RESULTS

Hospitals were frequently at high occupancy, with 28% of midnights at 85% to 94% occupancy and 42% of midnights at > or =95% occupancy. Whereas half of children's hospitals used occupancy-mitigating responses, there was variability in responses and magnitudes were small. When occupancy was >95%, no more than 8% of hospitals took steps to reduce admissions, 13% increased transfers out, and up to 58% reduced standardized length of stay. Two-day lag response was more common but remained of too small a magnitude to make a difference in hospital crowding. Additional modeling techniques also revealed little response.

CONCLUSIONS

We found a low rate of acute response to high occupancy. When there was a response, the magnitude was small.

Insulin therapy in the pediatric intensive care unit

BACKGROUND & AIMS

Hyperglycemia is a major risk factor for increased morbidity and mortality in the intensive care unit. Insulin therapy has emerged in adult intensive care units and several pediatric studies are currently being conducted. This review discusses hyperglycemia and the effects of insulin on metabolic and non-metabolic pathways, with a focus on pediatric critical illness.

METHODS

A PubMed search was performed by using the following keywords and limits (("hyperglycemia"[MeSH terms] or ("insulin resistance"[MeSH major topic]) and ("critical care"[MeSH terms] or "critical illness"[MeSH terms])) in different combinations with ("metabolism"[MeSH terms] or "metabolic networks and pathways"[MeSH terms]) and ("outcome"[all fields]) and ("infant"[MeSH terms] or "child"[MeSH terms] or "adolescent"[MeSH terms]). Quality assessment of selected studies included clinical pertinence, publication in peer-reviewed journals, objectivity of measurements and techniques used to minimize bias. Reference lists of such studies were included.

RESULTS

The magnitude and duration of hyperglycemia are associated with increased morbidity and mortality in the pediatric intensive care unit (PICU), but prospective, randomized controlled studies with insulin therapy have not been published yet. Evidence concerning the mechanism and the effect of insulin on glucose and lipid metabolism in pediatric critical illness is scarce. More is known about the positive effect on protein homeostasis, especially in severely burned children. The effect in septic children is less clear and seems age dependent. Some non-metabolic properties of insulin such as the modulation of inflammation, endothelial dysfunction and coagulopathy have not been fully investigated in children.

CONCLUSION

Future studies on the effect of insulin on morbidity and mortality as well as on the mechanisms through which insulin exerts these effects are necessary in critically ill children. We propose these studies to be conducted under standardized conditions including precise definitions of hyperglycemia and rates of glucose intake.

Review of the acuity scoring systems for the pediatric intensive care unit and their use in quality improvement

Acuity scoring systems quantitate the severity of clinical conditions and stratify patients according to presenting patient condition. In the pediatric intensive care unit, the complexity and number of clinical scoring systems are increasing as their applications for clinicians, health services researches, and quality improvement broaden. This article is a review of acuity scoring systems for the pediatric intensive care unit, including examples of scoring systems available, the methods used in assessing these tools, the ways in which these systems are used, and the utility of acuity scoring systems in accurate benchmarking. It is anticipated that with increasing health care costs and competition and increased focus on medical error reduction and quality improvement, the demands for risk-adjusted outcomes and institutional benchmarking will increase; therefore, as clinicians, academicians, and administrators, it is imperative that we be knowledgeable of the methods and applications of these acuity scoring systems to ensure their quality and appropriate use.

Going back for more: an evaluation of clinical outcomes and characteristics of readmissions to a pediatric intensive care unit

OBJECTIVE

To determine mortality, length of stay, and factors associated with readmissions to the pediatric intensive care unit (PICU).

DESIGN

A retrospective analysis of prospectively collected data.

SETTING

A 16-bed medical-surgical tertiary PICU and a coexisting 15-bed pediatric cardiac intensive care unit.

PATIENTS

All admissions from July 1, 1998, through June 30, 2004.

INTERVENTIONS

None.

MEASUREMENTS AND RESULTS

Of 8,885 total eligible admissions, 711 (8%) were readmissions to the PICU. The median age of the overall cohort was 35.2 months (interquartile range, 5.5-128.2). Readmitted patients were younger (10.4 vs. 37.7 months, p < .01), had greater severity of illness (p < .01), and were more likely to be admitted emergently (p < .01), in comparison with single admissions. In multivariate analyses, readmitted patients had a trend toward higher odds of mortality (odds ratio, 1.39; 95% confidence interval, 0.98-1.98) and stayed 2.96 days longer in the PICU (95% confidence interval, 1.98-3.94) compared with single admissions to the PICU. Factors independently associated with PICU readmission were infant age (odds ratio, 1.98; 95% confidence interval, 1.57-2.49), emergent admission (odds ratio, 2.21; 95% confidence interval, 1.78-2.77), illness severity (odds ratio, 1.03; 95% confidence interval, 1.01-1.04), and time of the year between July and September (odds ratio, 1.52; 95% confidence interval, 1.20-1.93). A diagnosis of trauma was associated with low likelihood of PICU readmission (odds ratio, 0.30; 95% confidence interval, 0.18-0.50).

CONCLUSIONS

Patients readmitted to the PICU during the same hospitalization have significantly adverse outcomes. The study highlights important factors associated with PICU readmissions that can be incorporated into efforts to reduce mortality and resource utilization associated with readmission of critically ill children.

Safety in the Pediatric ICU: The Key to Quality Outcomes

Patient safety is a major concern in the pediatric ICU. The acuity has never been higher, patient needs are extremely complex, and the margin for error is small. The concentration on safety needs to revolve around designing safe systems and processes. This article discusses communication, patient identification, catheter-related bloodstream infections, unplanned extubations, restraints and medication administration. The health care system of the future must be transparent, making safety information to insurers, patients and health care providers easily available.

Current trends of clinical and genetic characteristics influencing the resource use and the nurse-patient balance in an intensive care setting

PURPOSE

To determine the impact of resource use on the nurse/patient ratio in a pediatric intensive care unit (PICU). To examine the longitudinal influence of chronic or genetically influenced diseases on this interrelation.

MATERIALS AND METHODS

Overall, 1586 patients admitted to the PICU through various modes of admission during a 5-year period were prospectively studied.

RESULTS

The mean daily number of bed use increased from 5 to 8.1, leading to a significant skew from the ideal nurse/patient ratio of 1:1, to an overloaded one of 1:3-5. An increasing longitudinal trend of patients with metabolic diseases (P < .0001) or with genetic influence (62.8% in 1997, 70.7% in 2001) was noted. More patients with a genetic influence died than those without (13.8% vs 8.5%, P < .001), and more patients supported by mechanical ventilation suffered from a genetically influenced disease (64% vs 36%, P < .03). The mortality rate showed a trend for longitudinal reduction from 12.6% to 12%.

CONCLUSIONS

The increasing trend of occupation of PICU bed and ventilator days by patients with chronic diseases may be related to the increasing trend of hospitalization of patients with recognized genetic influence. Although this new trend does not influence mortality, it significantly increases resource use and has a large impact on the staffing needs.

Integrating the Institute of Medicine's six quality aims into pediatric critical care: relevance and applications

The Institute of Medicine's report Crossing the Quality Chasm recommends "six aims for improvement." The aims are safety, effectiveness, equity, timeliness, patient-centeredness, and efficiency. This review focuses on the quality of care information relevant to the Institute of Medicine's six aims to assess their relevance, potential impact, and affect on pediatric critical care practice. It is concluded that if the care for pediatric intensive care patients is to be fundamentally improved, an understanding of the current care environment, the existing evidence base, the opportunities for improvement, and the documentation of the improvements needs to be realized. The Institute of Medicine's six aims provide a useful framework to advance the quality of care in this pediatric subspecialty and perhaps others.

Acute and chronic paediatric intensive care patients: current trends and perspectives on resource utilization

BACKGROUND

Advances in paediatric critical care have resulted in increased survival of critically ill patients, many of whom require long-term ventilation as a means of life support.

AIM

To determine current trends in resource utilization, and problems in the care of acute and chronic paediatric intensive care patients.

DESIGN

Open observational study.

METHODS

We evaluated consecutive admissions (n = 1629) to a 10-bed paediatric intensive care unit (PICU) over a 5-year period. Three previously defined criteria for resource utilization were used: mean length of stay (LOS); length of mechanical ventilation (LOMV); and LOMV/LOS ratio.

RESULTS

A total of 10 310 patient bed days and 5223 ventilator days were used. Mean LOS increased from 5.3 +/- 12 days in 1998 to 8.7 +/- 27 days in 2001 (p < 0.05). Although LOMV/LOS ratio (50.7%) was significantly correlated with Paediatric Risk of Mortality score (p < 0.0001), there was no significant change in mortality rate (12.6% vs. 12%). Patients hospitalized for >2 weeks (n = 320, 20%) used 55% of LOS and 57% of LOMV, in contrast to the 1298 (80%) hospitalized for <7 days, who used only 29% of LOS and 20% of LOMV. Patients hospitalized for >3 months (11, 0.7%) consumed 17% of LOS and 23% of LOMV. Five of these (45%) were eventually discharged home, two on ventilators.

CONCLUSION

The increasing trend of occupation of PICU bed and ventilator days by critically ill children may be related to the increasing trend for hospitalization of chronic care patients. Severity scoring systems were predictive of resource consumption, but not of the overall trend in mortality rate.

Can PRISM predict length of PICU stay? an analysis of 2000 cases

PRISM is claimed to score disease severity which has attributed an impact on length of PICU stay (LOS).

PRIMARY OBJECTIVE

To determine the impact of PRISM on LOS, and evaluate an Artificial Neural Network's (ANN) performance to estimate LOS from PRISM item patterns.

RESEARCH DESIGN AND METHODS

Retrospectively we performed correlation and regression analyses on routinely scored PRISM data of all consecutive admissions to our level-III PICU from 1994 to 1999 (n > 2000) with individual LOS. In addition, an ANN was trained on the chronologically first 75% of those data (inputs, PRISM items + age + sex; output, LOS). The ANN's performance was tested on the remaining most recent 25% of the data sets.

MAIN RESULTS

The Spearman and Pearson coefficients of correlation between PRISM and LOS were 0.2 (p < 0.001) and 0.08 (p = 0.0003), the latter being slightly higher when LOS was logarithmically transformed. Pearson's coefficient of correlation between ANN derived LOS estimate and actual LOS was 0.21 (p < 0.001) (LOS logarithmically transformed: 0.34; p < 0.001) in the independent validation sample.

CONCLUSIONS

The ANN with its intrinsic ability to detect non-linear correlation, and to relate specific item patterns to LOS, outperformed linear statistics but was still disappointing in estimating individual LOS. It might be speculated that therapeutic intervention modulates the natural course of the disease thus counteracting both disease severity as initially scored by PRISM, and LOS. This being true, the inverse of the correlation between PRISM (or PRISM based LOS estimate) and LOS might be a candidate indicator of quality of care.

Timed Pediatric Risk of Mortality Scores predict outcomes in pediatric liver transplant recipients

More reliable methods are needed to identify children at risk for poor outcomes following liver transplantation. The Pediatric Risk of Mortality (PRISM) Score is a physiology-based scoring system used to quantify risk of mortality in pediatric intensive care unit (ICU) populations. We evaluated the PRISM Score as a predictor of outcomes including survival in the pediatric liver transplant (LT) population. We retrospectively reviewed the records of 67 consecutive LTs performed between August 1997 and February 2000 at an urban, tertiary children's hospital in Chicago, IL, USA. Four PRISM Scores were calculated to determine which periods were most meaningful. A Classic PRISM Score was calculated during first 24 h of ICU admission, and three PRISM Scores were timed with the patient's transplant: a pre-LT PRISM Score (24 h prior to transplant whether in ICU or not), a 24-h post-LT PRISM Score and a 48-h post-LT PRISM Score. These PRISM Scores and other predictors including transplant number, UNOS status and PELD Score were compared with outcomes including survival using univariate methods. The pre-LT, the 24- and the 48-h PRISM Score were associated with the post-LT number of ventilated days (p < 0.05), ICU days (p < 0.05) and with 1-yr survival (p < 0.04). The PRISM Scores were not related to the post-LT hospital length of stay (LOS) or to 1-yr re-transplantation. The PELD Score correlated with the post-LT hospital LOS, but was not associated with mortality or with the ICU LOS. A patient's UNOS status and Classic PRISM Score were not associated with any of the outcomes measured. PRISM Scores are valid predictors of outcome including survival in pediatric LT recipients. These findings help to demonstrate the importance in this population of a patient's general physiologic condition and its influence on the overall hospital course and survival.

Risk factors for long intensive care unit stay after cardiopulmonary bypass in children

OBJECTIVES

To determine whether children who experience longer intensive care unit (ICU) stays after open heart surgery may be identified at admission by clinical criteria. To identify factors associated with longer ICU stays that are potential targets for quality improvement.

SETTING

Tertiary pediatric cardiac surgical center.

DESIGN

A retrospective review was performed of pre-, intra-, and postoperative factors for children undergoing open heart surgery. All factors were evaluated for strength of association with length of ICU stay (LOS) using a negative binomial model. After multiple analysis, factors were deemed significant if associated with a LOS with p < .02.

PATIENTS

A total of 355 pediatric patients who had cardiac surgery with cardiopulmonary bypass in a 1-yr period from April 1999 until March 2000.

MEASUREMENTS AND MAIN RESULTS

Children who fell above the 95th percentile for LOS in our institution occupied 30% of bed days and had a three-fold greater mortality. Of all clinical factors considered, those significantly associated with LOS were as follows: preoperative--mechanical ventilation, neonatal status, medical problems, and transfer from abroad; intraoperative--higher operative complexity, increased cardiopulmonary bypass time or ischemic time, and circulatory arrest; and postoperative--delayed sternal closure, sepsis, renal failure, pulmonary hypertension, chylothorax, diaphragm paresis, and arrhythmia. A model combining all factors identified preoperative mechanical ventilation, neonatal status, major medical problems, operative complexity, cardiopulmonary bypass time, and a postoperative complication score as independently associated with LOS (p < .01).

CONCLUSIONS

At the time of ICU admission after open heart surgery, clinical criteria are evident that highlight a child's risk of longer ICU stay. These pre- and intraoperative factors relate to LOS independent of subsequent postoperative events. Those postoperative complications that are most strongly associated with increased LOS are identified and, therefore, made accessible to quality control.

[A sixteen-year epidemiological profile of a pediatric intensive care unit, Brazil]

OBJECTIVE

To review epidemiological data from patients admitted to a Pediatric Intensive Care Unit (PICU), Brazil, and to compare them to medical aspects associated to disease severity and mortality. Also, to profile these patients, including demographic data, disease prevalence, mortality rates and associated factors.

METHODS

Retrospective data were collected from all patients admitted in a PICU of a university hospital between 1978 and 1994. Data were presented as percentages and compared using Chi-square, and calculating the relative risk (RR) with a 95% confidence interval, p<0.05.

RESULTS

Of 13, 101 patients selected, most of them were boys (58.4%), younger than 12 months of age (40.4%), well-nourished (69.5%), and with clinical disease (73.1%). The general mortality rate was 7.4%. Patients aged less than 12 months showed a RR of 1.86 (CI 1.65 - 2.10; p<0.0001), while the RR of malnutrition was 2.98 (CI 2.64 - 3.36; p<0.0001).

CONCLUSIONS

The epidemiological survey showed that the mortality is higher in malnourished children younger than 12 months of age. Sepsis was the most main cause of death.

Long-stay patients in the pediatric intensive care unit

OBJECTIVE

Length of stay in the pediatric intensive care unit (PICU) is a reflection of patient severity of illness and health status, as well as PICU quality and performance. We determined the clinical profiles and relative resource use of long-stay patients (LSPs) and developed a prediction model to identify LSPs for early quality and cost saving interventions.

DESIGN

Nonconcurrent cohort study.

SETTING

A total of 16 randomly selected PICUs and 16 volunteer PICUs.

PATIENTS

A total of 11,165 consecutive admissions to the 32 PICUs.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

LSPs were defined as patients having a length of stay greater than the 95th percentile (>12 days). Logistic regression analysis was used to determine which clinical characteristics, available within the first 24 hrs after admission, were associated with LSPs and to create a predictive algorithm. Overall, LSPs were 4.7% of the population but represented 36.1% of the days of care. Multivariate analysis indicated that the following factors are predictive of long stays: age <12 months, previous ICU admission, emergency admission, no CPR before admission, admission from another ICU or intermediate care unit, chronic care requirements (total parenteral nutrition and tracheostomy), specific diagnoses including acquired cardiac disease, pneumonia, and other respiratory disorders, having never been discharged from the hospital, need for ventilatory support or an intracranial catheter, and a Pediatric Risk of Mortality III score between 10 and 33. The performance of the prediction algorithm in both the training and validation samples for identifying LSPs was good for both discrimination (area under the receiver operating characteristics curve of 0.83 and 0.85, respectively), and calibration (goodness of fit, p = .33 and p = .16, respectively). LSPs comprised from 2.1% to 8.1% of individual ICU patients and occupied from 15.2% to 57.8% of individual ICU bed days.

CONCLUSIONS

LSPs have less favorable outcomes and use more resources than non-LSPs. The clinical profile of LSPs includes those who are younger and those that require chronic care devices. A predictive algorithm could help identify patients at high risk of prolonged stays appropriate for specific interventions.

Relevance of diagnostic diversity and patient volumes for quality and length of stay in pediatric intensive care units

OBJECTIVE

Investigation of associations of the diagnostic diversity and volumes with efficiency and quality of care.

DESIGN

Prospective observational study.

SETTING

Thirty-two pediatric intensive care units (PICUs), 16 selected by random cluster sampling, and 16 volunteering.

PATIENTS

Consecutive admissions of 11,165 patients.

MEASUREMENTS AND MAIN RESULTS

The main outcome measures were length of PICU stay (LOS) and mortality rate, adjusted by generalized linear regression and multivariate logistic regression, respectively. Each diagnosis was categorized into 21 predefined, mutually exclusive categories. Diagnostic diversity of each PICU was characterized by an information-theoretical measure (entropy). For a patient-level analysis, the associations of this measure and PICU patient volume with outcomes were using regression models. For an institution-level analysis, the outcome measures of each PICU were adjusted using ratios of observed/predicted (by the regression models) values, and the associations of these ratios with diagnostic diversity and patient volume were investigated using linear bivariate regressions. Diagnostic diversity ranged in the PICUs from 0.823 to 0.928, when standardized to the uniform distribution with entropy of 1. Congenital heart diseases (12.6%) head traumas (11.5%), other central nervous system conditions (9.7%), and pneumonias (8.7%) constituted the largest diagnostic categories. Patient-level analysis indicated that longer adjusted LOS was associated with larger diagnostic diversity (p <.0001) and lower admission volumes (p <.0001). However, for a given increase in diagnostic diversity, a large LOS increase was associated with low-volume, but not high-volume units. Severity-adjusted mortality rates were inversely related (p =.036) only with admission volumes, but not diagnostic mix. Institution-level standardized LOS ratios correlated with diagnostic diversity (r2 = 0.145; p =.031). Institution-level standardized mortality ratios were inversely related (r2 = 0.123; p =.049) with admission volumes.

CONCLUSIONS

Patient volumes encountered in a PICU are important for maintaining quality and efficiency of care. In low-volume units, fewer diagnoses and higher volumes were both associated with higher efficiencies. In high volume units, diagnosis-specific volumes were generally large enough for achieving diagnosis-independent efficiency. Diagnostic mix was not associated with PICU mortality ratios, but higher PICU volumes were associated with lower mortality rates.

Volume-outcome relationships in pediatric intensive care units

CONTEXT

Pediatric intensive care units (PICUs) have expanded nationally, yet few studies have examined the potential impact of regionalization and no study has demonstrated whether a relationship between patient volume and outcome exists in these units. Documentation of an inverse relationship between volume and outcome has important implications for regionalization of care.

OBJECTIVES

This study examines relationships between the volume of patients and other unit characteristics on patient outcomes in PICUs. Specifically, we investigate whether an increase in patient volume improves mortality risk and reduces length of stay.

DESIGN AND SETTING

A prospective multicenter cohort design was used with 16 PICUs. All of the units participated in the Pediatric Critical Care Study Group. Participants. Data were collected on 11 106 consecutive admissions to the 16 units over a 12-month period beginning in January 1993.

MAIN OUTCOME MEASURES

Risk-adjusted mortality and length of stay were examined in multivariate analyses. The multivariate models used the Pediatric Risk of Mortality score and other clinical measures as independent variables to risk-adjust for illness severity and case-mix differences.

RESULTS

The average patient volume across the 16 PICUs was 863 with a standard deviation of 341. We found significant effects of patient volume on both risk-adjusted mortality and patient length of stay. A 100-patient increase in PICU volume decreased risk-adjusted mortality (adjusted odds ratio:.95; 95% confidence interval:.91-.99), and reduced length of stay (incident rate ratio:.98; 95% confidence interval:.975-.985). Other PICU characteristics, such as fellowship training program, university hospital affiliation, number of PICU beds, and children's hospital affiliation, had no effect on risk-adjusted mortality or patient length of stay.

CONCLUSIONS

The volume of patients in PICUs is inversely related to risk-adjusted mortality and patient length of stay. A further understanding of this relationship is needed to develop effective regionalization and referral policies for critically ill children.