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OBJECTIVES: To describe the prevalence of pediatric acute respiratory distress syndrome (pARDS) and the characteristics of children with pARDS in South African PICUs. DESIGN: Observational multicenter, cross-sectional point-prevalence study. SETTING: Eight PICUs in four South African provinces. PATIENTS: All children beyond the neonatal period and under 18 years of age admitted to participating PICUs. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Clinical and demographic data were prospectively collected on a single day of each month, from February to July 2022, using a centralized database. Cases with or at risk of pARDS were identified using the 2015 Pediatric Acute Lung Injury Consensus Conference criteria. Prevalence was calculated as the number of children meeting pARDS criteria/the total number of children admitted to PICU at the same time points. Three hundred ten patients were present in the PICU on study days: 166 (53.5%) male, median (interquartile range [IQR]) age 9.8 (3.1-32.9) months, and 195 (62.9%) invasively mechanically ventilated. Seventy-one (22.9%) patients were classified as being "at risk" of pARDS and 95 patients (prevalence 30.6%; 95% CI, 24.7-37.5%) fulfilled pARDS case criteria, with severity classified as mild (58.2%), moderate (25.3%), and severe (17.6%). Median (IQR) admission Pediatric Index of Mortality 3 risk of mortality in patients with and without pARDS was 5.6 (3.4-12.1) % versus 3.9 (1.0-8.2) % ( p = 0.002). Diagnostic categories differed between pARDS and non-pARDS groups ( p = 0.002), with no difference in age, sex, or presence of comorbidities. On multivariable logistic regression, increasing admission risk of mortality (adjusted odds ratio [aOR] 1.02; 95% CI, 1.00-1.04; p = 0.04) and being admitted with a respiratory condition (aOR 2.64; 95% CI, 1.27-5.48; p = 0.01) were independently associated with an increased likelihood of having pARDS. CONCLUSIONS: The 30.6% prevalence of pARDS in South Africa is substantially higher than reports from other sociogeographical regions, highlighting the need for further research in this setting.
Assuntos
Síndrome do Desconforto Respiratório , Recém-Nascido , Criança , Humanos , Masculino , Lactente , Adolescente , Feminino , Estudos Transversais , África do Sul/epidemiologia , Prevalência , Unidades de Terapia Intensiva PediátricaRESUMO
Objectives: The performance of mortality prediction models remain a challenge in lower- and middle-income countries. We developed an artificial neural network (ANN) model for the prediction of mortality in two tertiary pediatric intensive care units (PICUs) in South Africa using free to download and use software and commercially available computers. These models were compared to a logistic regression model and a recalibrated version of the Pediatric Index of Mortality 3. Design: This study used data from a retrospective cohort study to develop an artificial neural model and logistic regression model for mortality prediction. The outcome evaluated was death in PICU. Setting: Two tertiary PICUs in South Africa. Patients: 2,089 patients up to the age of 13 completed years were included in the study. Interventions: None. Measurements and Main Results: The AUROC was higher for the ANN (0.89) than for the logistic regression model (LR) (0.87) and the recalibrated PIM3 model (0.86). The precision recall curve however favors the ANN over logistic regression and recalibrated PIM3 (AUPRC = 0.6 vs. 0.53 and 0.58, respectively. The slope of the calibration curve was 1.12 for the ANN model (intercept 0.01), 1.09 for the logistic regression model (intercept 0.05) and 1.02 (intercept 0.01) for the recalibrated version of PIM3. The calibration curve was however closer to the diagonal for the ANN model. Conclusions: Artificial neural network models are a feasible method for mortality prediction in lower- and middle-income countries but significant challenges exist. There is a need to conduct research directed toward the acquisition of large, complex data sets, the integration of documented clinical care into clinical research and the promotion of the development of electronic health record systems in lower and middle income settings.
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OBJECTIVES: To evaluate the performance of the Pediatric Index of Mortality 3 as mortality risk assessment model. DESIGN: This prospective study included all admissions 30 days to 18 years old for 12 months during 2016 and 2017. Data gathered included the following: age and gender, diagnosis and reason for PICU admission, data specific for the Pediatric Index of Mortality 3 calculation, PICU outcomes (death or survival), and length of PICU stay. SETTING: Nine units that care for children within tertiary or quaternary academic hospitals in South Africa. PATIENTS: All admissions 30 days to 18 years old, excluding premature infants, children who died within 2 hours of admission, or children transferred to other PICUs, and those older than 18 years old. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: There were 3,681 admissions of which 2,253 (61.3%) were male. The median age was 18 months (interquartile range, 6-59.5 mo). There were 354 deaths (9.6%). The Pediatric Index of Mortality 3 predicted 277.47 deaths (7.5%). The overall standardized mortality ratio was 1.28. The area under the receiver operating characteristic curve was 0.81 (95% CI 0.79-0.83). The Hosmer-Lemeshow goodness-of-fit test statistic was 174.4 (p < 0.001). Standardized mortality ratio for all age groups was greater than 1. Standardized mortality ratio for diagnostic subgroups was mostly greater than 1 except for those whose reason for PICU admission was classified as accident, toxin and envenomation, and metabolic which had an standardized mortality ratio less than 1. There were similar proportions of respiratory patients, but significantly greater proportions of neurologic and cardiac (including postoperative) patients in the Pediatric Index of Mortality 3 derivation cohort than the South African cohort. In contrast, the South African cohort contained a significantly greater proportion of miscellaneous (including injury/accident victims) and postoperative noncardiac patients. CONCLUSIONS: The Pediatric Index of Mortality 3 discrimination between death and survival among South African units was good. Case-mix differences between these units and the Pediatric Index of Mortality 3 derivation cohort may partly explain the poor calibration. We need to recalibrate Pediatric Index of Mortality 3 to the local setting.