PIC, a paediatric-specific intensive care database

Zeng, Xian; Yu, Gang; Lu, Yang; Tan, Linhua; Wu, Xiujing; Shi, Shanshan; Duan, Huilong; Shu, Qiang; Li, Haomin

doi:10.1038/s41597-020-0355-4

Cited by 65 publications

(63 citation statements)

References 24 publications

(18 reference statements)

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“…The pediatric intensive care (PIC) database is a large, open, dedicated, and single-center pediatric database ( 14 ) that collects the clinical data of all children admitted to multiple ICUs at the Children's Hospital of Zhejiang University School of Medicine, China; between 2010 and 2018, information concerning 13,499 admissions of a total of 12,881 different pediatric patients was collected ( Figure 1 ). The collected data included the patients' demographics, diagnosis, laboratory test results, pathogenic microorganism test results, medications, clinical outcomes, and information regarding vital signs during operations.…”

Section: Methodsmentioning

confidence: 99%

Early Plasma Osmolality Levels and Clinical Outcomes in Children Admitted to the Pediatric Intensive Care Unit: A Single-Center Cohort Study

Wang

et al. 2021

Front. Pediatr.

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Objective: Identifying high-risk children with a poor prognosis in pediatric intensive care units (PICUs) is critical. The aim of this study was to assess the predictive value of early plasma osmolality levels in determining the clinical outcomes of children in PICUs.Methods: We retrospectively assessed critically ill children in a pediatric intensive care database. The locally weighted-regression scatter-plot smoothing (LOWESS) method was used to explore the approximate relationship between plasma osmolality and in-hospital mortality. Linear spline functions and stepwise expansion models were applied in conjunction with a multivariate logistic regression to further analyze this relationship. A subgroup analysis by age and complications was performed.Results: In total, 5,620 pediatric patients were included in this study. An approximately “U”-shaped relationship between plasma osmolality and mortality was detected using LOWESS. In the logistic regression model using a linear spline function, plasma osmolality ≥ 290 mmol/L was significantly associated with in-hospital mortality [odds ratio (OR) 1.020, 95% confidence interval (CI) 1.010–1.031], while plasma osmolality <290 mmol/L was not significantly associated with in-hospital mortality (OR 0.990, 95% CI 0.966–1.014). In the logistic regression model with plasma osmolality as a tri-categorical variable, only high osmolality was significantly associated with in-hospital mortality (OR 1.90, 95% CI 1.38–2.64), whereas low osmolality was not associated with in-hospital mortality (OR 1.28, 95% CI 0.84–1.94). The interactions between plasma osmolality and age or complications were not significant.Conclusion: High osmolality, rather than low osmolality, can predict a poor prognosis in children in PICUs.

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Section: Methodsmentioning

confidence: 99%

Early Plasma Osmolality Levels and Clinical Outcomes in Children Admitted to the Pediatric Intensive Care Unit: A Single-Center Cohort Study

Wang

et al. 2021

Front. Pediatr.

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“…The pediatric-specific intensive care database (PIC), a large China-based pediatric critical care database, was analyzed [ 17 ]. The PIC is an integrated, de-identified, comprehensive clinical dataset containing routine hospital care records from the Children’s Hospital, Zhjiang University School of Medicine, from 2010 to 2018.…”

Section: Methodsmentioning

confidence: 99%

Prognostic Impact of Parameters of Metabolic Acidosis in Critically Ill Children with Acute Kidney Injury: A Retrospective Observational Analysis Using the PIC Database

et al. 2020

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Acute kidney injury (AKI) is a major complication of sepsis that induces acid-base imbalances. While creatinine levels are the only indicator for assessing the prognosis of AKI, prognostic importance of metabolic acidosis is unknown. We conducted a retrospective observational study by analyzing a large China-based pediatric critical care database from 2010 to 2018. Participants were critically ill children with AKI admitted to intensive care units (ICUs). The study included 1505 children admitted to ICUs with AKI, including 827 males and 678 females. The median age at ICU admission was 22 months (interquartile range 7–65). After a median follow-up of 10.87 days, 4.3% (65 patients) died. After adjusting for confounding factors, hyperlactatemia, low pH, and low bicarbonate levels were independently associated with 28-day mortality (respective odds ratio: 3.06, 2.77, 2.09; p values: <0.01, <0.01, <0.01). The infection had no interaction with the three parameters. The AKI stage negatively interacted with bicarbonate and pH but not lactate. The current study shows that among children with AKI, hyperlactatemia, low pH, and hypobicarbonatemia are associated with 28-day mortality.

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“…Dataset. We use a recently released publicly available pediatric ICU dataset named PIC [17]. It comprises information relating to patients admitted to critical care units at the Children's Hospital of Zhejiang University School of Medicine in China.…”

Section: Methodsmentioning

confidence: 99%

Predicting Risk of Mortality in Pediatric ICU Based on Ensemble Step-Wise Feature Selection

et al. 2021

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Background. Prediction of mortality risk in intensive care units (ICU) is an important task. Data-driven methods such as scoring systems, machine learning methods, and deep learning methods have been investigated for a long time. However, few data-driven methods are specially developed for pediatric ICU. In this paper, we aim to amend this gap—build a simple yet effective linear machine learning model from a number of hand-crafted features for mortality prediction in pediatric ICU. Methods. We use a recently released publicly available pediatric ICU dataset named pediatric intensive care (PIC) from Children’s Hospital of Zhejiang University School of Medicine in China. Unlike previous sophisticated machine learning methods, we want our method to keep simple that can be easily understood by clinical staffs. Thus, an ensemble step-wise feature ranking and selection method is proposed to select a small subset of effective features from the entire feature set. A logistic regression classifier is built upon selected features for mortality prediction. Results. The final predictive linear model with 11 features achieves a 0.7531 ROC-AUC score on the hold-out test set, which is comparable with a logistic regression classifier using all 397 features (0.7610 ROC-AUC score) and is higher than the existing well known pediatric mortality risk scorer PRISM III (0.6895 ROC-AUC score). Conclusions. Our method improves feature ranking and selection by utilizing an ensemble method while keeping a simple linear form of the predictive model and therefore achieves better generalizability and performance on mortality prediction in pediatric ICU.

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PIC, a paediatric-specific intensive care database

Cited by 65 publications

References 24 publications

Early Plasma Osmolality Levels and Clinical Outcomes in Children Admitted to the Pediatric Intensive Care Unit: A Single-Center Cohort Study

Early Plasma Osmolality Levels and Clinical Outcomes in Children Admitted to the Pediatric Intensive Care Unit: A Single-Center Cohort Study

Prognostic Impact of Parameters of Metabolic Acidosis in Critically Ill Children with Acute Kidney Injury: A Retrospective Observational Analysis Using the PIC Database

Predicting Risk of Mortality in Pediatric ICU Based on Ensemble Step-Wise Feature Selection

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