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Arterial diastolic blood pressure (DBP) greater than 25 mm Hg in infants and greater than 30 mm Hg in children greater than 1 year old during cardiopulmonary resuscitation (CPR) was associated with survival to hospital discharge in one prospective study. We sought to validate these potential hemodynamic targets in a larger multicenter cohort.Prospective observational study.Eighteen PICUs in the ICU-RESUScitation prospective trial from October 2016 to March 2020.Children less than or equal to 18 years old with CPR greater than 30 seconds and invasive blood pressure (BP) monitoring during CPR.None.Invasive BP waveform data and Utstein-style CPR data were collected, including prearrest patient characteristics, intra-arrest interventions, and outcomes. Primary outcome was survival to hospital discharge, and secondary outcomes were return of spontaneous circulation (ROSC) and survival to hospital discharge with favorable neurologic outcome. Multivariable Poisson regression models with robust error estimates evaluated the association of DBP greater than 25 mm Hg in infants and greater than 30 mm Hg in older children with these outcomes. Among 1,129 children with inhospital cardiac arrests, 413 had evaluable DBP data. Overall, 85.5% of the patients attained thresholds of mean DBP greater than or equal to 25 mm Hg in infants and greater than or equal to 30 mm Hg in older children. Initial return of circulation occurred in 91.5% and 25% by placement on extracorporeal membrane oxygenator. Survival to hospital discharge occurred in 58.6%, and survival with favorable neurologic outcome in 55.4% (i.e. 94.6% of survivors had favorable neurologic outcomes). Mean DBP greater than 25 mm Hg for infants and greater than 30 mm Hg for older children was significantly associated with survival to discharge (adjusted relative risk [aRR], 1.32; 1.01-1.74; p = 0.03) and ROSC (aRR, 1.49; 1.12-1.97; p = 0.002) but did not reach significance for survival to hospital discharge with favorable neurologic outcome (aRR, 1.30; 0.98-1.72; p = 0.051).These validation data demonstrate that achieving mean DBP during CPR greater than 25 mm Hg for infants and greater than 30 mm Hg for older children is associated with higher rates of survival to hospital discharge, providing potential targets for DBP during CPR.

OBJECTIVE: To describe at the individual patient level the pathophysiologic processes contributing to morbidity and mortality in pediatric intensive care units (PICUs) and therapeutic additions and advances that could potentially prevent or reduce morbidity and mortality. DESIGN: Qualitative content analysis of intensivists’ conclusions on pathophysiologic processes and needed therapeutic advances formulated by structured medical record review. SETTING: Eight children’s hospitals affiliated with the Eunice Kennedy Shriver National Institute of Child Health and Human Development Collaborative Pediatric Critical Care Research Network. PATIENTS: A randomly selected cohort of critically ill children with a new functional morbidity or mortality at hospital discharge. New morbidity was assessed using the Functional Status Scale and defined as worsening by two or more points in a single domain from pre-illness baseline. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Of 292 children, 175 (59.9%) had a new morbidity and 117 (40.1%) died. The most common pathophysiology was impaired substrate delivery (n=158, 54.1%) manifesting as global or regional hypoxia or ischemia due to low cardiac output or cardiac arrest. Other frequent pathophysiologies were inflammation (n=104, 35.6%) related to sepsis, respiratory failure, acute respiratory distress syndrome or multiple organ dysfunction; and direct tissue injury (n=64, 21.9%) including brain and spinal cord trauma. Chronic conditions were often noted (n=156, 53.4%) as contributing to adverse outcomes. Drug therapies (n=149, 51.0%) including chemotherapy, inotropes, vasoactive agents and sedatives were the most frequently proposed needed therapeutic advances. Other frequently proposed therapies included cell regeneration (n=115, 39.4%) mainly for treatment of neuronal injury, and improved immune and inflammatory modulation (n=79, 27.1%). CONCLUSIONS: Low cardiac output and cardiac arrest, inflammation-related organ failures, and central nervous system trauma were the most common pathophysiologies leading to morbidity and mortality in PICUs. A research agenda focused on better understanding and treatment of these conditions may have high potential to directly impact patient outcomes.

Objectives Developing effective therapies to reduce morbidity and mortality requires knowing the responsible pathophysiologies and the therapeutic advances that are likely to be impactful. Our objective was to determine at the individual patient level the important pathophysiological processes and needed therapeutic additions and advances that could prevent or ameliorate morbidities and mortalities. Design Structured chart review by pediatric intensivists of PICU children discharged with significant new morbidity or mortality to determine the pathophysiologies responsible for poor outcomes and needed therapeutic advances. Setting Multicenter study (eight sites) from the Collaborative Pediatric Critical Care Research Network of general and cardiac PICUs. Patients First PICU admission of patients from December 2011 to April 2013. Interventions None. Measurements and main results Two-hundred ninety-two patients were randomly selected from 681 patients discharged with significant new morbidity or mortality. The median age was 2.4 years, 233 (79.8%) were in medical/surgical ICUs, 59 (20.2%) were in cardiac ICUs. Sixty-five (22.3%) were surgical admissions. The outcomes included 117 deaths and 175 significant new morbidities. The most common pathophysiologies contributing to the poor outcomes were impaired substrate delivery (n = 158, 54.1%) and inflammation (n = 104, 35.6%). There were no strong correlations between the pathophysiologies and no remarkable clusters among them. The most common therapeutic needs involved new drugs (n = 149, 51.0%), cell regeneration (n = 115, 39.4%), and immune and inflammatory modulation (n = 79, 27.1%). As with the pathophysiologies, there was a lack of strong correlations or meaningful clusters in the suggested therapeutic needs. Conclusions There was no single dominant pathophysiology or cluster of pathophysiologies responsible for poor pediatric critical care outcomes. Therapeutic needs often involved therapies that are not close to implementation such as cell regeneration, improved organ transplant, improved extracorporeal support and artificial organs, and improved drugs.

Objectives Assessing outcomes after pediatric critical illness is imperative to evaluate practice and improve recovery of patients and their families. We conducted a scoping review of the literature to identify domains and instruments previously used to evaluate these outcomes. Design Scoping review. Setting We queried PubMed, EMBASE, PsycINFO, Cumulative Index of Nursing and Allied Health Literature, and the Cochrane Central Register of Controlled Trials Registry for studies evaluating pediatric critical care survivors or their families published between 1970 and 2017. We identified articles using key words related to pediatric critical illness and outcome domains. We excluded articles if the majority of patients were greater than 18 years old or less than 1 month old, mortality was the sole outcome, or only instrument psychometrics or procedural outcomes were reported. We used dual review for article selection and data extraction and categorized outcomes by domain (overall health, emotional, physical, cognitive, health-related quality of life, social, family). Subjects Manuscripts evaluating outcomes after pediatric critical illness. Interventions None. Measurements and main results Of 60,349 citations, 407 articles met inclusion criteria; 87% were published after 2000. Study designs included observational (85%), interventional (7%), qualitative (5%), and mixed methods (3%). Populations most frequently evaluated were traumatic brain injury (n = 96), general pediatric critical illness (n = 87), and congenital heart disease (n = 72). Family members were evaluated in 74 studies (18%). Studies used a median of 2 instruments (interquartile range 1-4 instruments) and evaluated a median of 2 domains (interquartile range 2-3 domains). Social (n = 223), cognitive (n = 183), and overall health (n = 161) domains were most frequently studied. Across studies, 366 unique instruments were used, most frequently the Wechsler and Glasgow Outcome Scales. Individual domains were evaluated using a median of 77 instruments (interquartile range 39-87 instruments). Conclusions A comprehensive, generalizable understanding of outcomes after pediatric critical illness is limited by heterogeneity in methodology, populations, domains, and instruments. Developing assessment standards may improve understanding of postdischarge outcomes and support development of interventions after pediatric critical illness.

OBJECTIVES: The objective of this study was to compare survival outcomes and intra-arrest arterial blood pressures between children receiving cardiopulmonary resuscitation for bradycardia and poor perfusion and those with pulseless cardiac arrests. DESIGN: Prospective, multicenter observational study. SETTING: PICUs and cardiac ICUs of the Collaborative Pediatric Critical Care Research Network. PATIENTS: Children (< 19 yr old) who received greater than or equal to 1 minute of cardiopulmonary resuscitation with invasive arterial blood pressure monitoring in place. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Of 164 patients, 96 (59%) had bradycardia and poor perfusion as the initial cardiopulmonary resuscitation rhythm. Compared to those with initial pulseless rhythms, these children were younger (0.4 vs 1.4 yr; p = 0.005) and more likely to have a respiratory etiology of arrest (p < 0.001). Children with bradycardia and poor perfusion were more likely to survive to hospital discharge (adjusted odds ratio, 2.31; 95% CI, 1.10–4.83; p = 0.025) and survive with favorable neurologic outcome (adjusted odds ratio, 2.21; 95% CI, 1.04–4.67; p = 0.036). There were no differences in diastolic or systolic blood pressures or event survival (return of spontaneous circulation or return of circulation via extracorporeal cardiopulmonary resuscitation). Among patients with bradycardia and poor perfusion, 49 of 96 (51%) had subsequent pulselessness during the cardiopulmonary resuscitation event. During cardiopulmonary resuscitation, these patients had lower diastolic blood pressure (point estimate, −6.68 mm Hg [−10.92 to −2.44 mm Hg]; p = 0.003) and systolic blood pressure (point estimate, −12.36 mm Hg [−23.52 to −1.21 mm Hg]; p = 0.032) and lower rates of return of spontaneous circulation (26/49 vs 42/47; p < 0.001) than those who were never pulseless. CONCLUSIONS: Most children receiving cardiopulmonary resuscitation in ICUs had an initial rhythm of bradycardia and poor perfusion. They were more likely to survive to hospital discharge and survive with favorable neurologic outcomes than patients with pulseless arrests, although there were no differences in immediate event outcomes or intra-arrest hemodynamics. Patients who progressed to pulselessness after cardiopulmonary resuscitation initiation had lower intra-arrest hemodynamics and worse event outcomes than those who were never pulseless.

Objectives:To determine the incidence of cardiopulmonary resuscitation in PICUs and subsequent outcomes.Design, Setting, and Patients:Multicenter prospective observational study of children younger than 18 years old randomly selected and intensively followed from PICU admission to hospital discharge

Assessments of care including quality assessments adjusted for physiological status should include the development of new morbidities as well as mortalities. We hypothesized that morbidity, like mortality, is associated with physiological dysfunction and could be predicted simultaneously with mortality.Prospective cohort study from December 4, 2011, to April 7, 2013.General and cardiac/cardiovascular PICUs at seven sites.Randomly selected PICU patients from their first PICU admission.None.Among 10,078 admissions, the unadjusted morbidity rates (measured with the Functional Status Scale and defined as an increase of ≥ 3 from preillness to hospital discharge) were 4.6% (site range, 2.6-7.7%) and unadjusted mortality rates were 2.7% (site range, 1.3-5.0%). Morbidity and mortality were significantly (p0.001) associated with physiological instability (measured with the Pediatric Risk of Mortality III score) in dichotomous (survival and death) and trichotomous (survival without new morbidity, survival with new morbidity, and death) models without covariate adjustments. Morbidity risk increased with increasing Pediatric Risk of Mortality III scores and then decreased at the highest Pediatric Risk of Mortality III values as potential morbidities became mortalities. The trichotomous model with covariate adjustments included age, admission source, diagnostic factors, baseline Functional Status Scale, and the Pediatric Risk of Mortality III score. The three-level goodness-of-fit test indicated satisfactory performance for the derivation and validation sets (p0.20). Predictive ability assessed with the volume under the surface was 0.50 ± 0.019 (derivation) and 0.50 ± 0.034 (validation) (vs chance performance = 0.17). Site-level standardized morbidity ratios were more variable than standardized mortality ratios.New morbidities were associated with physiological status and can be modeled simultaneously with mortality. Trichotomous outcome models including both morbidity and mortality based on physiological status are suitable for research studies and quality and other outcome assessments. This approach may be applicable to other assessments presently based only on mortality.

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.Nonconcurrent cohort study.A total of 16 randomly selected PICUs and 16 volunteer PICUs.A total of 11,165 consecutive admissions to the 32 PICUs.None.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: age12 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.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.

None of the currently available physiology-based mortality risk prediction models incorporate subjective judgements of healthcare professionals, a source of additional information that could improve predictor performance and make such systems more acceptable to healthcare professionals. This study compared the performance of subjective mortality estimates by physicians and nurses with a physiology-based method, the Pediatric Risk of Mortality (PRISM) III. Then, healthcare provider estimates were combined with PRISM III estimates using Bayesian statistics. The performance of the Bayesian model was then compared with the original two predictions.Concurrent cohort study.A tertiary pediatric intensive care unit at a university affiliated children's hospital.Consecutive admissions to the pediatric intensive care unit.None.For each of the 642 consecutive eligible patients, an exact mortality estimate and the degree of certainty (continuous scale from 1 to 5) associated with the estimate was collected from the attending, fellow, resident, and nurse responsible for the patient's care. Bayesian statistics were used to combine the PRISM III and certainty weighted subjective predictions to create a third Bayesian estimate of mortality. PRISM III discriminated survivors from nonsurvivors very well (area under curve [AUC], 0.924) as did the physicians and nurses (AUCs attendings, 0.953; fellows, 0.870; residents, 0.923; nurses, 0.935). Although the AUCs of the healthcare providers were not significantly different from the AUCs of PRISM III, the Bayesian AUCs were higher than both the healthcare providers' AUCs (por = .09 for all) and PRISM III AUCs. Similarly, the calibration statistics for the Bayesian estimates were superior to the calibration statistics for both the healthcare providers and PRISM III models.The results of this study demonstrated that healthcare providers' subjective mortality predictions and PRISM III mortality predictions perform equally well. The Bayesian model that combined provider and PRISM III mortality predictions was more accurate than either provider or PRISM III alone and may be more acceptable to physicians. A methodology using subjective outcome predictions could be more relevant to individual patient decision support.

Objectives To develop and validate a pediatric nosocomial infection risk (PNIR) assessment model, and to compare the daily trends in risk factors between patients with nosocomial infection (cases) and without nosocomial infection (controls) in the pediatric intensive care unit (ICU). Design Prospective cohort. Setting A 16-bed pediatric ICU in an urban, university-affiliated, multidisciplinary, regional referral center. Patients Patients available for study included consecutive admissions to the unit between May 1, 1992, and April 30, 1993, and between May 9, 1995, and December 11, 1995. Patients from both data collection periods were pooled and randomly divided into training (70%) and validation (30%) samples. Measurements and Main Results In the logistic regression analysis using admission day data, three factors were shown to remain as independent risk factors. Invasive device use, parenteral nutrition, and the interaction between severity of illness-modified Pediatric Risk of Mortality III-24 score and postoperative care were associated with 2, 6, and 1.5 times the risk of developing nosocomial infection, respectively. This PNIR model performed well in both the training and validation samples as indicated by the goodness-of-fit test, which evaluated standardized nosocomial infection rates (observed vs. predicted nosocomial infection rates). The internal validity of the PNIR model was good. In trend analysis, severity of illness and invasive device use appear to have similar trend patterns, during the first week of pediatric ICU stay. There was no difference in any of these risk factors between cases and controls after 7 days of pediatric ICU stay. Conclusions The PNIR assessment model incorporates intrinsic factors, such as patient severity of illness, and extrinsic factors contributing to the development of nosocomial infection in this high-risk population. The methodology using intrinsic and extrinsic factors to adjust for nosocomial infections should be taken into consideration when evaluating interhospital comparison of nosocomial infection rates, quality assessment, intervention strategies, and use of treatment modalities.

Objective: Comparison of severity and diagnosis-adjusted mortality rates from pediatric intensive care units (ICUs) staffed by physicians training in pediatric critical care, as well as pediatric residents, with mortality rates from pediatric ICUs staffed with only pediatric residents. Design: Cohort study. Setting: Sixteen volunteer pediatric ICUs, eight with critical care fellowships, and eight without such programs. Patients: Consecutive admissions until at least 14 deaths occurred at each site. Interventions: None. Measurements and Main Results: Descriptive data and Pediatric Risk of Mortality scores were collected. Severity and diagnosis-adjusted mortality risk for each patient was computed by a predictor developed in an independent sample. The effect of fellowship programs was analyzed at the institution level by ranking the pediatric ICUs in terms of observed/predicted mortality rates, and, at the patient level, by including a training factor into the predictor model. The use of monitoring and therapeutic modalities was compared in the two types of pediatric ICUs by severity-adjusted odds ratios. There were 2,744 admissions (145 deaths) to the eight fellowship pediatric ICUs and 3,006 admissions (150 deaths) to the eight nonfellowship pediatric ICUs. Institutional characteristics were not different between the two pediatric ICU sets. The raw mortality rates were similar (fellowship 5.28%; nonfellowship 4.99%, p = .714). Institution-level analyses indicated that fellowship pediatric ICUs performed better than nonfellowship pediatric ICUs; fellowship pediatric ICUs ranked better than pediatric ICUs without such programs (Wilcoxon rank-sum test, p = .020). However, both the best and the worst ranked pediatric ICUs had fellowships. Patient-level analyses also indicated that outcome was significantly influenced by the fellowship status of the pediatric ICU. Using two different patient-level analytic approaches, the odds of dying in a fellowship pediatric ICU vs. a nonfellowship pediatric ICU were 0.592 (95% confidence interval 0.468 to 0.749, p = .0001) and 0.714 (95% confidence interval 0.529 to 0.964, p = .028). Pediatric ICUs with fellowship programs performed more (p

Objectives We evaluated: a) whether the frequency of variable measurement could influence the performance of the Pediatric Risk of Mortality (PRISM) score; b) whether measurement frequency of physiologic variables varied between individual pediatric intensive care units (ICUs), and c) if so, how much of this variability could be attributed to institution-level and patient-level factors. Design Prospective cohort. Setting Sixteen pediatric ICUs, chosen for their diversity. Patients Consecutive admissions (n = 5,415). Interventions None. Measurements and main results First, the measurement frequency of the 14 physiologic variables in the PRISM score was included in the logistic regression model predicting mortality risk. Measurement frequency was not significant, alone or in its interaction with the PRISM score. Second, the presence or absence of measurement of each physiologic variable was included in the logistic model using indicator variables; none was significant. Finally, the contribution of the individual pediatric ICUs and patient factors in explaining the variability in the frequency of physiologic variable measurement were investigated with linear regression analysis. In this analysis, the separation of severity of illness from measurement frequency was accomplished by computing the PRISM score from the first 4 hrs and measurement frequencies from hours 5 through 24. Overall, 70.22% (r2) of the variability of measurement frequency could be explained by the factors included in the linear regression model. The individual ICUs accounted for a total of only 6.23% of the explained variability and no individual hospital accounted for > 1.44% of the variability. Other variables positively correlated with measurement frequency included the presence or absence of a pediatric intensivist, and whether the institution was a children's hospital or not. Variables negatively correlated with measurement frequency included larger ICUs and house officers assigned to the ICU. Conclusions Although measurement frequency is associated with unit-level factors, their contribution to the overall variability is small and unlikely to influence the accuracy or reliability of the PRISM score. It is unlikely that there are routine biases associated with differences in measurement frequency of PRISM variables within the spectrum of care practices that now exist.

Objectives To review the existing literature and task force opinions on regionalization of critical care services, and to synthesize a judgement on possible costs, benefits, disadvantages, and strategies. Data sources Pertinent literature in the English language. Study selection One hundred forty-six English language papers were studied to determine possible ramifications of regionalization of critical care or other similar services. Data extraction Information on possible influence on the care of the critically ill was sought and integrated with the opinions of task force members. Possible costs, benefits, as well as disadvantages to the patient, transferring and receiving institutions, and region as a whole were sought. Data synthesis Regionalization of critical care services was thought to be advantageous to the patient. The larger academic institutions tend to have more resources, better subspecialty availability, and expertise in the care of the critically ill. Efficiency and safety during transport need to be in place. Disadvantages of overutilization, possible costliness to both the referring institution as well as to the receiving institution were outlined. It was agreed that pediatric critical care medicine was a separate issue. Conclusions Regionalization of critical care medicine probably is beneficial and the concept should be explored.

To compare outcomes from pediatric intensive care in hospitals with different levels of resources.Prospective, blinded comparison of outcome and care.Tertiary (n = 3) and nontertiary (n = 71) hospitals in Oregon and southwestern Washington.All critically ill children admitted with respiratory failure and head trauma for 6 months.Severity of illness adjusted mortality rates were determined using admission day, physiologic profiles (Pediatric Risk of Mortality score) and care modalities were assessed daily. The crude mortality rate of the tertiary patients was four times higher than for the nontertiary patients (23.4% vs. 6.0%, p less than .0001). In the tertiary patients, the numbers of outcomes were accurately predicted by physiologic profiles (observed: 30 deaths and 98 survivors; predicted: 29.3 deaths and 98.7 survivors, z = -.25, p greater than .4). However, for the nontertiary patients, the number of the deaths were significantly different than predicted (observed: 20 deaths and 315 survivors; predicted: 14.4 deaths and 320.6 survivors, z = -2.08, p less than .05). The odds ratios of dying in a nontertiary vs. a tertiary facility were about 1.1, 2.3, and 8 (p less than .05) for mortality risk groups of less than 5%, 5% to 30%, and greater than 30%. Patients in tertiary facilities received more (p less than .05) invasive (e.g., arterial catheters) and complex (e.g., mechanical ventilation) care, whereas patients in nontertiary facilities received more (p less than .05) labor-intensive care (e.g., hourly vital signs).Care of the most seriously ill children in tertiary pediatric ICUs could improve their chances of survival.

Iatrogenic illness may be an important determinant of the need for pediatric intensive care. We prospectively evaluated consecutive admissions to a pediatric ICU (PICU) over two time periods totaling 6 months. Twenty-five (4.6%) admissions were necessitated by iatrogenic illnesses. Drug-induced conditions accounted for eight (32%) of the iatrogenic patients, and complications of medical-surgical acts accounted for 17 (68%). Diagnoses included six respiratory failures due to seizure medications, six chronic upper airway complications of neonatal intensive care, four posttonsillectomy and postadenoidectomy complications, two chronic postcardiac surgery complications, two cardiac catheterization complications, and five miscellaneous conditions. One (3.7%) patient with iatrogenic illness died. As a group, patients with iatrogenic illness were at a risk of dying similar to other patients. We conclude that iatrogenic illness is a significant cause of PICU admission.

Treatment plans for pediatric septic shock advocate increasing oxygen consumption (VO2). Recent studies in septic shock indicate that improving oxygen delivery (DO2) by increasing blood flow will increase VO2. We prospectively examined the effect on VO2 of improving DO2 by increasing oxygen content (CO2) with blood transfusion in eight hemodynamically stable septic shock patients. Transfusion consisted of 8 to 10 ml/kg of packed RBC over 1 to 2 h. Hemodynamic and oxygen transport measurements were obtained before and after blood transfusion. Transfusion significantly (p less than .05) increased Hgb and Hct from 10.2 +/- 0.8 g/dl and 30 +/- 2% to 13.2 +/- 1.4 g/dl and 39 +/- 4%, respectively (mean +/- SD). DO2 significantly (p less than .05) increased after transfusion (599 +/- 65 to 818 +/- 189 ml/min.m2), but VO2 did not change (166 +/- 68 to 176 +/- 74 ml/min.m2; NS). In pediatric septic shock patients, increasing CO2 by blood transfusion may not increase VO2.

Objective To evaluate the current standards for apnea testing in the evaluation of brain death in children. Design Case report. Setting A tertiary pediatric intensive care unit (ICU). Patients A single patient admitted to the ICU. Interventions None. Measurements and Main Results A formal brain death examiniation was performed on a 4-yr-old male with a diagnosis of acute pilocystic astrocytoma and global cerebral hypoxic ischemic damage secondary to a cardiorespiratory arrest. The patient fulfilled all criteria for brain death, except the apnea test. An apnea test was performed for 9 mins 23 secs, at which time, spontaneous respiratory effort was noted. The respiratory efforts were initiated with a pH of 7.08 and a PaCO2 of 91 torr (12.1 kPa). Conclusion This case report suggests that current guidelines for apnea testing may lead to erroneous evaluation of medullary-respiratory drive. (Crit Care Med 1998; 26:1917-1919)

Health outcomes may be influenced by organizational and management characteristics. We hypothesized that neonatal intensive care unit managerial practices and organizational processes could affect outcomes of births 500-1499 g, and we assessed this in eight neonatal intensive care units.Multiple-center cohort.Eight acute care neonatal intensive care units in Washington, DC.Infants weighing 500-1499 g, born from October 1, 1994, to February 19, 1997, to a resident of Washington, DC.None.Outcomes included 28-day survival/death, bronchopulmonary dysplasia (BPD), periventricular/intraventricular hemorrhage or periventricular leukomalacia (PIVH/PVL), retinopathy of prematurity (ROP), length of hospital stay, and days of mechanical ventilation. Managerial practices and organizational processes were rated by nurses (n = 218), physicians (n = 73), and respiratory therapists (n = 77). Risk-adjusted logistic and linear mixed models were used to assess the association of outcomes with the caregiver ratings. A lower incidence of PIVH/PVL was associated with better overall scores (p =.0036) and better subscores for leadership (p.0001), coordination (p =.047), and conflict resolution (p =.020). Better values of the respiratory therapists', nurses', and physicians' scores were associated with lower mortality rates (p =.045) and BPD (p =.0057), PIVH/PVL (p.0001), and ROP (p =.049), respectively. In multivariate analysis, the joint effects of the professional groups' scores were associated with the incidence of PIVH/PVL (p =.0047); the nurses' scores were associated with lower incidence of PIVH/PVL (p =.0051). The association between the respiratory therapists' scores and lower mortality rate (p =.025) also remained significant in multivariate analysis.We found that the organizational processes and managerial practices as rated by healthcare professionals in neonatal intensive care units were associated with the development of mortality and chronic, severe morbidity. We did not identify specific processes or practices that accounted for our results.

Trauma triage scores, severity of illness measures, and mortality prediction models quantitate severity of injury and stratify patients according to a specified outcome. Triage scoring systems are typically used to assist prehospital personnel determine which patients require trauma center care, but they are not recommended as the sole determinant of triage. Severity of illness measures and mortality prediction models are used in clinical and health services research for risk-adjusted outcomes analyses and institutional benchmarking. As clinicians and researchers, it is imperative that we be knowledgeable of the methodologies and applications of these scoring and risk prediction systems to ensure their quality and appropriate utilization.

OBJECTIVE To evaluate the relative resource use of pediatric intensive care unit (PICU) patients who had been born prematurely. DESIGN Nonconcurrent cohort study. SETTING Consecutive admissions to 16 voluntary PICUs. PATIENTS A total of 431 formerly premature patients (FPP) and 5,319 nonpremature patients. INTERVENTIONS None METHODS Patients with a history of prematurity and a prematurity-related complication or an anatomical deformity were compared for demographic and resource requirements to a group of non-premature patients by a bivariable logistic regression analysis that controlled for age as a co-morbid factor. RESULTS Compared with other patients, FPP were younger (34.9 +/- 2.2 months vs. 72.4 +/- 1.0 months; p < .001), readmitted to the PICU more often during the same hospitalization (11.1% vs. 5.5%; p < .001), used more chronic technologies (ventilators, gastrostomy tubes, tracheostomy tubes, and parenteral nutrition; 30.3% vs. 5.6%; p < .001), and had longer lengths of stay (5.98 +/-0.59 days vs. 3.56 +/- 0.12 days; p = .004). FPP had significantly higher use of ventilators (45.5% vs. 35.0%; p < .007) and lower use of arterial catheters (27.8% vs. 35.9%, p = .006) and central venous catheters (16.9% vs. 20.9%, p = .026) than nonprematures. The need for other PICU resources, including vasopressors, were similar. CONCLUSIONS FPP used more chronic and acute care resources than patients who were not prematurely born. Continued improvements in neonatal care will influence change in many aspects of the health care system. This will also affect the delivery of care to the current patient base of the PICU.

Objective: Aminophylline has not been studied as an adjunct diuretic in critically ill children. Our purpose was to evaluate its use in the treatment of fluid overload in these patients. Design: Open, controlled clinical trial. Setting: Pediatric intensive care unit. Patients: Study subjects ranged from 2-46 months of age, were fluid overloaded, and were receiving a continuous infusion of furosemide (≥6 mg/kg/day). Patients with hemodynamic instability or liver dysfunction were excluded. Interventions: A single dose of aminophylline (6 mg/kg) was given after establishing baseline values. There were no additional diuretics or changes in vasoactive agents during the study. Measurements and Main Results: Urine output, creatinine clearance, and sodium and potassium excretion were measured before and after administration of the aminophylline bolus. Heart rate and mean arterial pressure (mm Hg) were recorded hourly. Urine output increased by >80% (p

To determine if decomplexification of heart rate dynamics occurs in critically ill and injured pediatric patients. We hypothesized that heart rate power spectra, a measure of heart rate dynamics, would inversely correlate with measures of severity of illness and outcome.A prospective clinical study.A 12-bed pediatric intensive care unit (ICU) in a tertiary care children's hospital.One hundred thirty-five consecutive pediatric ICU admissions.None.We compared heart rate power spectra with the Pediatric Risk of Mortality (PRISM) score, the Pediatric Cerebral Performance Category (PCPC), and the Pediatric Overall Performance Category (POPC). We found significant negative correlations between minimum low-frequency and high-frequency heart rate power spectral values recorded during ICU stay and the maximum PRISM score (log low-frequency heart rate power vs. PRISM, r2 = .293, p.001; and log high-frequency heart rate power vs. PRISM, r2 = .243, p.001) and outcome at ICU discharge (log low-frequency heart rate power vs. POPC or PCPC, r2 = .429, p.001; and log high-frequency heart rate power vs. POPC or PCPC, r2 = .271, p.001).Our data support the hypothesis that measures of heart rate power spectra are inversely related and negatively correlated to severity of illness and outcome in critically ill and injured children. The phenomenon of decomplexification of physiologic dynamics may have important clinical implications in critical illness and injury.

Objective : To determine surfactant profiles of tracheal secretions in mechanically ventilated children with respiratory failure secondary to bacterial pneumonia, viral pneumonitis, adult respiratory distress syndrome (ARDS), and cardiopulmonary bypass. Design : Prospective, cohort study. Setting : Tertiary, multidisciplinary, pediatric intensive care unit. Patients : One hundred twenty pediatric patients with respiratory failure requiring mechanical ventilation. Interventions : Routine tracheal aspirates were collected from children with bacterial pneumonia, viral pneumonitis, ARDS, postcardiopulmonary bypass, and a postsurgical control group. Samples were obtained on days 1, 2, 3, after every week of intubation and on the day of extubation. Measurements and Main Results : The tracheal aspirates were analyzed by high-performance liquid chromatography for lecithin/sphingomyelin ratios and by enzyme-linked immunosorbent assay for surfactant proteins A and B. Lung compliance and the oxygenation index were measured on each day of sample collection. On day 1, patients with bacterial pneumonia, viral pneumonitis, and ARDS had decreased lecithin/sphingomyelin ratios (p

The relationship between physiologic status and mortality risk should be reevaluated as new treatment protocols, therapeutic interventions, and monitoring strategies are introduced and as patient populations change. We developed and validated a third-generation pediatric physiology-based score for mortality risk, Pediatric Risk of Mortality III (PRISM III).Prospective cohort.There were 32 pediatric intensive care units (ICUs): 16 pediatric ICUs were randomly chosen and 16 volunteered.Consecutive admissions at each site were included until at least 11 deaths per site occurred.Physiologic data included the most abnormal values from the first 12 and the second 12 hrs of ICU stay. Outcomes and descriptive data were also collected. Physiologic variables where normal values change with age were stratified by age (neonate, infant, child, adolescent). The database was randomly split into development (90%) and validation (10%) sets. Variables and their ranges were chosen by computing the risk of death (odds ratios) relative to the midrange of survivors for each physiologic variable. Univariate and multivariate statistical procedures, including multiple logistic regression analysis, were used to develop the PRISM III score and mortality risk predictors. Data were collected on 11,165 admissions (543 deaths). The PRISM III score has 17 physiologic variables subdivided into 26 ranges. The variables most predictive of mortality were minimum systolic blood pressure, abnormal pupillary reflexes, and stupor/coma. Other risk factors, including two acute and two chronic diagnoses, and four additional risk factors, were used in the final predictors. The PRISM III score and the additional risk factors were applied to the first 12 hrs of stay (PRISM III-12) and the first 24 hours of stay (PRISM III-24). The Hosmer-Lemeshow chi-square goodness-of-fit evaluations demonstrated absence of significant calibration errors (p values: PRISM III-12 development = .2496; PRISM III-24 development = .1374; PRISM III-12 validation = .4168; PRISM III-24 validation = .5504). The area under the receiver operating curve and Flora's z-statistic indicated excellent discrimination and accuracy (area under the receiver operating curve - PRISM III-12 development 947 +/- 0.007; PRISM III-24 development 0.958 +/- 0.006; PRISM III-12 validation 0.941 +/- 0.021; PRISM III-24 validation 0.944 +/- 0.021; Flora's z-statistic - PRISM III-12 validation = .7479; PRISM III-24 validation = .9225), although generally, the PRISM III-24 performed better than the PRISM III-12 models. Excellent goodness-of-fit was also found for patient groups stratified by age (significance levels: PRISM III-12 = .1622; PRISM III-24 = .4137), and by diagnosis (significance levels: PRISM III-12 = .5992; PRISM III-24 = .7939).PRISM III resulted in several improvements over the original PRISM. Reassessment of physiologic variables and their ranges, better age adjustment for selected variables, and additional risk factors resulted in a mortality risk model that is more accurate and discriminates better. The large number of diverse ICUs in the database indicates PRISM III is more likely to be representative of United States units.

Objective : To identify factors in pediatric intensive care unit (ICU) patients that are associated with an increased risk of nosocomial infections. Design : A prospective, 1-yr cohort study. Setting : A 16-bed pediatric ICU in a multidisciplinary, regional referral center. Subjects : All patients admitted to the pediatric ICU. Interventions : None. Measurements and Main Results : The primary outcome variable was the development of nosocomial infection. Out of 945 consecutive admissions, 75 patients developed 96 nosocomial infections. The most frequent infection sites were the lower respiratory tract (35%), the bloodstream (21%), and the urinary tract (21%). The most common organisms isolated were Gram-negative bacteria (53%), Gram-positive bacteria (27%), and fungi (9%). Variables significantly associated with the development of nosocomial infections included age, weight, Pediatric Risk of Mortality (PRISM) score, device utilization ratio, antimicrobial therapy, histamine-2 (H 2 ) receptor blocker use, immune status, parenteral nutrition, and length of stay. When combined in a multivariate logistic regression model, the significant variables were operative status, PRISM score, device utilization ratio, antimicrobial therapy, parenteral nutrition, and length of stay before the onset of infection. The area under the receiver operating characteristic curve was 0.868. At a probability of 0.15, the sensitivity was 66.67%, and the specificity was 87.82%. Conclusions : Patients at risk for developing nosocomial infection can be identified using a multivariate logistic regression model with a high degree of sensitivity and specificity. These data indicate that institutional nosocomial rates need to be adjusted for risk factors. This model could help target patients at high risk for developing nosocomial infections for preventive strategies.

Objective To develop a method based on admission day data for predicting patient outcome status as independently functional, compromised functional, or dead. Design Prospectively acquired development and validation samples. Setting A pediatric intensive care unit located in a tertiary care center. Patients Consecutive admissions (n equals 1,663) for predictor development, and consecutive admissions (n equals 1,153) for predictor validation. Methods Pediatric Risk of Mortality score, baseline Pediatric Overall Performance Category score, age, operative status, and primary diagnosis classified into ten organ systems and nine etiologies were recorded at the time of intensive care unit admission. Predictor was developed by stepwise polychotomous logistic regression analysis for the outcomes functional, compromised, and dead. Model fit was evaluated by chi-square statistics; prediction performance was measured by the area under the receiver operating characteristic curve, and classification table analysis of observed vs. predicted outcomes. Measurements and Main Results The resulting predictor included Pediatric Risk of Mortality, baseline Pediatric Overall Performance Category, operative status, age, and diagnostic factors from four systems (cardiovascular, respiratory, neurologic, gastrointestinal), and six etiologies (infection, trauma, drug overdose, allergy/immunology, diabetes, miscellaneous/undetermined). Its application to the validation sample yielded good agreement between the total number expected and the observed outcomes for each state (chi-square equals 3.16, 2 degrees of freedom, p equals .206), with area indices of 0.96 plus minus 0.01 for discrimination of fully functional vs. the combination of the two poor outcome states (compromised or death), and 0.94 plus minus 0.02 for discrimination of fully or compromised functional vs. death. The 3 times 3 classification resulted in correct classification rates of 83.2%, 74.4%, and 81.3%, for the outcomes functional, compromised, and death, respectively. Conclusions Prediction of three outcome states using physiologic status, baseline functional level, and broad-based diagnostic groupings at admission is feasible and may improve the relevance of quality of care assessment.

Objective To determine the long-term outcomes and resource utilization of children discharged home in a vegetative state following neurologic injury. Design A case series. Setting Patients' homes. Patients Twenty children discharged from acute and chronic care hospitals with a diagnosis and discharge examination consistent with the vegetative state. Interventions Home care delivered, in part, by registered nurses. Measurements Assessed outcomes included survival/death, mental status, functional status, costs, and personnel requirements and technologies used for home care. A mailed questionnaire and telephone follow-up were used to assess patients awareness and caretaker satisfaction with home care. Results Children were followed in the vegetative state for 4.5 +/- 2.9 yrs. Six children died at home and two children died after rehospitalization. Twelve children survived at home, all for > 1 yr; eight children survived for > 3 yrs. Most patients were stable after the first year of home care. Twelve of 13 caretakers felt their child had some minimal awareness (e.g., voice recognition), although all children remained totally dependent. Costs of care averaged > $90,000/yr per patient. Care included 10 to 12 hrs/day of professional nursing care, and extensive time investments by other personnel, including public school personnel. Conclusions The long-term outcome for children discharged from the hospital in a persistent vegetative state was poor. Forty percent of the patients died and, at best, children showed only minimal awareness after an average of 4.5 yrs. Care costs were > $90,000/yr per patient.

OBJECTIVE To describe the structure and organization of pediatric intensive care units (ICUs) in the United States. SURVEY METHODS We directed a mail survey to pediatric ICU medical and nursing directors and hospital quality assurance officers. A total of 201 of 301 hospitals with pediatric ICUs initially responded. Telephone confirmation of the mail survey (n = 193) and telephone data collection for mail survey nonresponders (n = 42) were also undertaken. SURVEY RESULTS The largest proportion (40.0%) of pediatric ICUs had four to six beds per unit, while only 6.0% had > 18 beds per unit. The admissions per year averaged 528 +/- 24, and the mortality rates averaged 5.5 +/- 0.2%. Only 79.6% of the pediatric ICUs had full-time medical directors. A pediatric intensivist was available to 73.2% of the units. Physician coverage for 24 hrs/day dedicated only to the pediatric ICU was present in 48.5% of hospitals. As ICU size increased, the estimated mortality rates increased, as did the percentages with full-time directors, pediatric intensivists, and 24 hrs/day dedicated coverage. Medical school affiliation existed for 79.6% of pediatric ICU hospitals, and 81.1% of these hospitals were the primary teaching program sites for pediatrics. Other ICUs caring for children were present in 30.2% of the hospitals. SURVEY APPLICATION The mail survey respondents were stratified using four factors: size, teaching status, intensivist status, and coordination of care status. A total of 16 respondents were randomly selected for an ongoing outcomes study of the importance of these factors. CONCLUSIONS Substantial diversity exists in pediatric ICU structure and organization. Determining factors associated with quality of care is important for improving outcomes.

OBJECTIVE To develop and validate a mortality risk predictor based on physiologic data that estimates daily the probability of a patient dying within the next 24 hrs as that probability changes with disease and recovery. SETTING Nine pediatric ICUs in tertiary care centers. PATIENTS Data from 1,401 patients (116 deaths, 5,521 days of care) were used for predictor development, and 1,227 patients (105 deaths, 4,597 days of care) provided data for predictor validation. METHODS The predictor was developed by logistic regression analysis using the Pediatric Risk of Mortality scores of all previous days as potential predictor variables. Performance was measured by the area under the receiver operating characteristic curve (Az), and by the comparison of the daily predicted vs. observed patient status in five mortality risk groups (less than 0.01, 0.01 to 0.05, 0.05 to 0.15, 0.15 to 0.3, greater than 0.3) using chi-square goodness-of-fit tests. MEASUREMENTS AND MAIN RESULTS Only the most recent and the admission day Pediatric Risk of Mortality scores (with a weighting ratio of 3:1) contributed significantly (p less than .05) to the prediction. The overall prediction attained an accuracy of Az = 0.904. The daily number and distribution of survivors and nonsurvivors in the five mortality risk groups were well predicted in the total sample (chi 2 [5 degrees of freedom] = 2.51; p greater than .75), and each ICU separately (chi 2 [5 degrees of freedom] range 2.41 to 7.96; all p greater than .15). This dynamic predictor improved (p less than .01) ICU outcome prediction over an admission-day predictor and, in the opinion of the authors, is essential for pediatric ICU efficiency analysis. CONCLUSIONS The predictor is valid for assessing the 24-hr mortality risk in pediatric ICU patients hospitalized in other tertiary care institutions, different from those used for predictor development. The predicted mortality risks allow prospective patient stratification into risk groups. The ability of this predictor to follow risk changes over time expands its applicability over static predictors by enabling the charting of patient courses, and permitting ICU efficiency analysis.