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Biomedical subjects

M M Pollack

Publications and source records attributed to M M Pollack.

At least 37 records · Page 2Linked to original sources
PubMed1998

A method for assessing emergency department performance using patient outcomes.

OBJECTIVE: To determine the rates of correct patient disposition after an ED evaluation. METHODS: In a university pediatric hospital, a 25% random sample of ED patients for 4 consecutive months was reviewed, after exclusion of minor injuries and patients triaged to the nonurgent clinic. Patients were categorized into one of 4 outcomes on the basis of inpatient resource use: appropriate admission, inappropriate admission, appropriate release, or inappropriate release. A 10% random sample of released patients was contacted by telephone to detect patients who sought care elsewhere after ED release. RESULTS: 642 of 2,682 ED patients (23.9%) were admitted; 159 (24.7%) were inappropriately admitted, and 26 (1.3%) were inappropriately released. The correct identification of the need for hospitalization (sensitivity) was 94.9%, and for release (specificity) 92.7%. Overall, the correct classification rate was 93.1%. Inappropriate admissions were associated with diagnoses of trauma, seizures, and burns. CONCLUSION: Inappropriate admissions occur at a substantial rate and occur more commonly than inappropriate releases. The correct disposition of patients is a practical and meaningful outcome-based measure of the quality of ED care. This methodology is suitable for use in other EDs.

Emergency Service, Hospital↗
PubMed1997

The Pediatric Risk of Mortality III--Acute Physiology Score (PRISM III-APS): a method of assessing physiologic instability for pediatric intensive care unit patients.

OBJECTIVE: To develop a physiology-based measure of physiologic instability for use in pediatric patients that has an expanded scale compared with the Pediatric Risk of Mortality (PRISM) III score. STUDY DESIGN: Data were collected from consecutive admissions to 32 pediatric ICUs (11,165 admission, 543 deaths). Patient-level data included physiologic data, outcomes, descriptive information, and diagnoses. Physiologic data included the most abnormal values in the first 24 hours of pediatric ICU stay from 27 variables. Initially, ranges of each physiologic variable were evaluated for their association with mortality. A multi-variate logistic regression analysis was used to determine the final variables and their ranges. Integer scores reflecting the relative contribution to mortality risk were assigned to the variable ranges. RESULTS: A total of 59 ranges of 21 physiologic variables were selected. This score is called the Pediatric Risk of Mortality III--Acute Physiology Score (PRISM III-APS). Mortality increased as the PRISM III-APS score increased. Most patients have PRISM III-APS scores less than 10, and these patients have a mortality risk of less than 1%. At the other extreme, the mortality rate of the 137 patients with a PRISM III-APS score of greater than 80 was greater than 97%. CONCLUSION: The PRISM III-APS score is an expanded measure of physiologic instability that has been validated against mortality. Compared with PRISM III, PRISM III-APS should be more sensitive to small changes in physiologic status.

Acute Disease↗
PubMed1997

Pediatric critical care training programs have a positive effect on pediatric intensive care mortality.

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 < .05) invasive monitoring, including intra-arterial catheters and central venous pressure catheters, and more technological therapies such as mechanical ventilation. CONCLUSIONS: Pediatric ICUs with critical care fellowship programs are generally associated with better risk-adjusted mortality rates than pediatric ICUs without such fellowship training programs. The cause for this effect requires a more in-depth study. The presence or absence of such training programs does not guarantee superior or inferior performance.

Child↗
PubMed1997

Cardiopulmonary resuscitation in pediatric intensive care units.

OBJECTIVE: To determine the effectiveness of cardiopulmonary resuscitation (CPR) in the pediatric intensive care unit (ICU). DESIGN: A nonconcurrent cohort study of consecutive admissions. SETTING: Thirty-two pediatric ICUs. PATIENTS: Consecutive admissions to 32 pediatric ICUs. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Pediatric ICU patients were followed for the occurrence of a cardiopulmonary arrest (external cardiac massage for at least 2 mins). Patients who were in a state of continuous cardiopulmonary arrest on admission, or who never achieved stable vital signs, were excluded from the study. A total of 205 patients, from a sample of 11,165 (1.8%) pediatric admissions, experienced a cardiopulmonary arrest. Overall, 28 (13.7%) patients survived to hospital discharge. Neither mean ages nor age distribution affected survival. Only two diagnostic categories, traumatic illness, and other etiologies, were associated with survival. None of the patients fitting this category survived (p = .0028). The durations of CPR for survivors and nonsurvivors were 22.5 +/- 10.1 and 24.8 +/- 1.9 mins, respectively (p = .015). For CPR durations of <15 mins, 15 to 30 mins, and >30 mins, the survival rates were 18.6%, 12.2%, and 5.6%, respectively (linear trend p = .022). Thirty-five (17.1%) patients had a cardiopulmonary arrest before pediatric ICU admission and another arrest in the pediatric ICU. Only two (5.7%) of these 35 patients survived to discharge. Pediatric ICU survival decreased as the number of pediatric ICU arrests increased. Patients with one arrest (n = 155), two arrests (n = 29), and more than three arrests (n = 21) experienced survival rates of 14%, 14%, and 9.5%, respectively. Severity of illness, as measured by the Pediatric Risk of Mortality III score, was a significant predictor of survival (p < .001). CONCLUSIONS: Pediatric ICU cardiac arrest is an uncommon event. When it does occur, prehospital CPR, duration of resuscitation, traumatic etiology, and severity of illness are important factors associated with survival.

Adolescent↗
PubMed1996

Variability in duration of stay in pediatric intensive care units: a multiinstitutional study.

OBJECTIVE: Development of a statistical model to predict length of stay (LOS) in a pediatric intensive care unit (PICU) that adjusts for patient-related risk factors at admission. DESIGN: Randomized selection of sites by cluster sampling from a 1989 national survey of all hospitals with PICUs, stratified for four quality-of-care factors into 16 clusters (size, presence of an intensive care specialist, medical school affiliation, coordination of care). The data collection was prospective in the selected units. PATIENTS: 5415 consecutive medical, surgical, or emergency admissions to 16 PICUs. MEASUREMENTS: Patients: Pediatric Risk of Mortality (PRISM) score for the initial 24 hours, admission diagnosis classified into system and cause of the primary dysfunction, operative status, preadmission care, critical care modalities required during the first 24 hours, age, sex, PICU length of stay, and outcome. PICU sites: admission volume, coordination of care, presence of an intensivist, presence of residents, and number of pediatric ICU and pediatric hospital beds. METHODS: Log-logistic regression analysis of LOS on patient-related and institution-related factors. RESULTS: Significant (p < 0.05) patient-related predictors of LOS included PRISM, 10 diagnostic groups, 3 preadmission factors (operative status, inpatient/outpatient, previous PICU admission), and first-day use of mechanical ventilation. The ratio of observed to predicted LOS varied among PICUs from 0.83 to 1.25, with three PICUs displaying significantly (p < 0.05) shorter and three PICUs longer LOS. The PICU factors associated (p < 0.05) with shorter (5% to 11%) LOS were presence of an intensivist, presence of residents, and coordination of care, whereas an increased ratio of PICU to hospital beds was associated with longer (p < 0.05) LOS. Medical school affiliation, admission volume, number of pediatric hospital beds, and PICU mortality rates did not have statistically significant effects on LOS when adjusted for patient conditions. CONCLUSIONS: The predictor can be used to adjust LOS in PICUs for patient-related risk factors, enabling the comparison of resource utilization among different institutions. Organizational factors known to foster team-oriented care are associated with shorter LOS, whereas increased relative PICU size may pose an incentive to keep PICU beds occupied longer.

Adolescent↗
PubMed1996

Frequency of variable measurement in 16 pediatric intensive care units: influence on accuracy and potential for bias in severity of illness assessment.

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.

Child↗
PubMed1996

Prediction of three outcome states from pediatric intensive care.

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 = 1,663) for predictor development, and consecutive admissions (n = 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 outcome 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 = 3.16, 2 degrees of freedom, p = .206), with area indices of 0.96 +/- 0.01 for discrimination of fully functional vs. the combination of the two poor outcome states (compromised or death), and 0.94 +/- 0.02 for discrimination of fully or compromised functional vs. death. The 3 x 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.

Arkansas↗
PubMed1996

PRISM III: an updated Pediatric Risk of Mortality score.

OBJECTIVES: 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). DESIGN: Prospective cohort. SETTING: There were 32 pediatric intensive care units (ICUs): 16 pediatric ICUs were randomly chosen and 16 volunteered. PATIENTS: Consecutive admissions at each site were included until at least 11 deaths per site occurred. MEASUREMENTS AND MAIN RESULTS: 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). CONCLUSIONS: 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.

Adolescent↗
PubMed1996

Risk factors for nosocomial infection in critically ill children: a prospective cohort study.

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 (H2) 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.

Analysis of Variance↗
PubMed1996

Surfactant content in children with inflammatory lung disease.

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 rations 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 ration (p < .001), and those patients with bacterial pneumonia and viral pneumonitis had decreased surfactant protein A/protein concentration (p < .001). The lecithin/sphingomyelin ratios and surfactant protein A/protein concentration were significantly different among the groups (p < .001), with the bacterial pneumonia and viral pneumonitis groups having higher lecithin/sphingomyelin ratios and increased surfactant protein concentrations before extubation. Pulmonary compliance was lower and the oxygenation index was higher than controls (p < .001) in patients with bacterial pneumonia, viral pneumonitis, and ARDS. Pulmonary compliance was correlated weakly with lecithin/sphingomyelin ratio (r2 = .11, p < .001) and surfactant protein A/protein concentration (r2 = .03, p < .05). Surfactant protein B was similar in the diagnostic groups. Surfactant content in tracheal secretions from cardiopulmonary bypass patients was equivalent to controls. CONCLUSION: Abnormal tracheal aspirate surfactant phospholipids and surfactant protein A were noted in children with bacterial pneumonia, viral pneumonitis, and ARDS, but not in children on cardiopulmonary bypass.

Cardiopulmonary Bypass↗
PubMed1995

Variability in brain death determination practices in children.

OBJECTIVE: To investigate variability in practices for determining brain death and organ procurement results in pediatric intensive care units (PICUs). DESIGN: Prospective cohort study. SETTING: Pediatric ICUs. PATIENTS: Children undergoing brain death evaluations selected from 5415 consecutive PICU admissions. MAIN OUTCOME MEASURES: Data from children undergoing brain death evaluations including number of coma examinations, number and duration of apnea tests, PCO2 measurements at the end of the apnea test, ancillary tests used to confirm brain death, organ procurement, and reasons for nonprocurement. RESULTS: A total of 93 (37%) of 248 deaths were brain deaths. Compared with the other deaths, children who were classified as brain dead were sicker on admission (mean Pediatric Risk of Mortality [PRISM] score +/- SD: 31 +/- 11 vs 23 +/- 12, P < .001; pre-ICU cardiopulmonary resuscitation: 72% vs 40%, P < .001), and had more traumatic injuries (42% vs 12%, P < .001). Variability in apnea testing included lack of apnea testing in 23 patients (25%) and controversial apnea testing practices in 20 patients (22%). Three patients (3%) had brain death evaluations within hours of discontinuing barbiturate infusions, and four of 30 patients younger than 1 year did not have a confirmatory test. Solid organ procurement was successful in 32%. Reasons for nonprocurement included parental refusal (12%), disease state (12%), and medical examiner's case (22%). CONCLUSIONS: Substantial variability exists in the criteria used by clinicians for the diagnosis of brain death. Some practices are contradictory to the Guidelines for the Determination of Brain Death in Children and to recommendations for apnea testing. Organ procurement could be improved by increased medical examiner cooperation.

Apnea↗
PubMed1995

Catheter-related thrombosis in critically ill children: comparison of catheters with and without heparin bonding.

OBJECTIVE: To compare the incidence of and factors associated with vascular thrombosis after placement of heparin-bonded and standard femoral venous catheters. DESIGN: Prospective, masked, clinical study. SETTING: Multidisciplinary, tertiary, pediatric intensive care unit. PATIENTS: Consecutive cases (n = 50) of critically ill children admitted to a pediatric intensive care unit in whom either a heparin-bonded (n = 25) or a standard (n = 25) femoral venous catheter was placed. MEASUREMENTS AND MAIN RESULTS: Patients were examined by ultrasonography within 3 days of catheter insertion, weekly while the catheter was in place, and after catheter removal for evidence of vascular thrombosis. Data were collected prospectively regarding clinical evidence of catheter thrombosis, infusate composition, and positive blood culture results. Of 50 patients, 13 (26%) had thrombotic complications, 11 (44%) of the 25 patients in the standard-catheter group, in comparison with 2 (8%) of the 25 patients in the heparin-bonded catheter group (p = 0.004). In addition, there was a significantly higher incidence of positive blood culture results among patients in the standard-catheter group (24% vs 0%; p = 0.009). Positive catheter blood culture results were obtained in 38% of patients with thrombosis versus 3% without thrombosis (p = 0.001). Clinical evidence of thrombosis was found in 69% of patients with, versus 27% of patients without, ultrasound-proved thrombosis (p = 0.007). CONCLUSION: Heparin bonding of catheters is associated with significantly fewer thrombotic complications. A reduced incidence of positive catheter-related blood culture results may be associated with the absence of thrombosis.

Catheterization, Central Venous↗
PubMed1994

Limitations and withdrawals of medical intervention in pediatric critical care.

OBJECTIVE: To investigate the use and implementation in pediatric intensive care units (PICUs) of three levels of restriction of medical intervention: do not resuscitate (DNR), additional limitations of medical interventions beyond DNR, and withdrawal of care. DESIGN: Consecutive patients admitted between December 1989 and January 1992. SETTING: A total of 16 PICUs randomly selected to represent variability in size, teaching status, and presence or absence of a pediatric intensivist and unit coordination. MAIN OUTCOME MEASURES: Profiles of children undergoing restrictions of medical interventions including the influence of chronic disease, the justifications for restrictions, and description of implementation practices. PATIENTS: All pediatric admissions undergoing restrictions (n = 119) drawn from 5415 consecutive PICU admissions. RESULTS: A total of 94 (79%) of the restriction patients died during the PICU course, representing 38% of all deaths. A total of 73 restrictions (61%) resulted from acute disease, most involving the central nervous system or respiratory system. Restrictions were evenly divided between DNR (39%), additional limitations of medical intervention beyond DNR (27%), and withdrawals of medical intervention (34%). Survival decreased with increasing levels of restriction from 35% of DNR patients to 9% of patients with additional limitations and 2% of withdrawal patients. Imminent death was cited as the justification for restrictions in 70% of cases, no relational potential was cited in 22%, and excessive burden was cited in 8%. CONCLUSIONS: Restrictions of medical intervention were used in all PICUs surveyed. Although severe chronic disease was common among restriction patients, acute disease was the predominant event precipitating placement of restrictions. Imminent death, not quality of life or excessive burden, was the most common justification.

Analysis of Variance↗
PubMed1994

Impact of quality-of-care factors on pediatric intensive care unit mortality.

OBJECTIVE: To determine the importance of the following care factors previously associated with hospital quality on survival from pediatric intensive care: size of the intensive care unit (ICU), medical school teaching status of the hospital housing the ICU, specialist status (pediatric intensivist), and unit coordination. DESIGN: After a national survey, consecutive case series were collected at 16 sites randomly selected to represent unique combinations of quality-of-care factors. SETTING: Pediatric ICUs. PATIENTS: Consecutive admissions to each site. MAIN OUTCOME MEASURE: Patient mortality adjusted for physiologic status, diagnosis, and other mortality risk factors. RESULTS: There were 5415 pediatric ICU admissions and 248 ICU deaths. The ICUs differed significantly with respect to descriptive variables, including mortality (range, 2.2% to 16.4%). Analysis of risk-adjusted mortality indicated that the hospital teaching status and the presence of a pediatric intensivist were significantly associated with a patient's chance of survival. The probability of patient survival after hospitalization in an ICU located in a teaching hospital was decreased (relative odds of dying, 1.79; 95% confidence interval [CI], 1.23 to 2.61; P = .002). In contrast, the probability of patient survival after hospitalization in an ICU with a pediatric intensivist was improved (relative odds of dying, 0.65; 95% CI, 0.44 to 0.95; P = .027). Post hoc analysis indicated that the higher severity-adjusted mortality in teaching hospitals may be explained by the presence of residents caring for ICU patients. CONCLUSION: Characteristics indicative of the best overall hospital quality may not be associated, or may be negatively associated, with quality of care in specialized care areas, including the pediatric ICU.

Child↗