Severity Trajectories of Pediatric Inpatients Using the Criticality Index

Rivera, Eduardo A Trujillo; Patel, Anita; Zeng‐Treitler, Qing; Chamberlain, James M.; Bost, James E.; Heneghan, Julia A.; Morizono, Hiroki; Pollack, Murray M.

doi:10.1097/pcc.0000000000002561

Cited by 11 publications

(28 citation statements)

References 35 publications

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“…The dot is grey if the test results in a p-value < 0.05. Construct validity in this study and others was assessed using clinical trajectories, overall and within clinical entities, which were consistent with clinical expectations (17,28). The validity for the Criticality Index, however, has been further explored.…”

Section: Discussionmentioning

confidence: 69%

“…Mortality risk groups were determined from the first 3-hour time period using mortality risk quartiles. A priori based on analyses of clinical trajectories from the national Criticality Index-Mortality model ( 28 ), we expected that the mortality risk of deaths in the highest risk quartile would remain high and other death groups would increase or remain relatively constant over time and the mortality risk of survivors in the highest survivor risk quartile group would decrease and the risk of other survivor risk groups would decrease or remain relatively constant over time. Second, we plotted the mortality risk trajectories using diagnostic groups with traditionally low (diabetic ketoacidosis, bronchiolitis) and high risk (congenital cardiovascular conditions, traumatic brain injury, bone marrow transplant, pediatric acute respiratory distress syndrome).…”

Section: Methodsmentioning

confidence: 99%

“…We hypothesized that deaths in the low-risk diagnoses would follow a similar clinical trajectory to deaths in the low-risk quartile – they would increase overtime, while survivors would maintain relatively low and constant mortality risks. Whereas, deaths in the high-risk diagnoses would rapidly increase their mortality risks or begin at a high mortality risk and remain high, while survivors' mortality risk trajectories would remain relatively constant or improve over time ( 28 ).…”

Section: Methodsmentioning

confidence: 99%

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The criticality Index-mortality: A dynamic machine learning prediction algorithm for mortality prediction in children cared for in an ICU

et al. 2022

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BackgroundThe Criticality Index-Mortality uses physiology, therapy, and intensity of care to compute mortality risk for pediatric ICU patients. If the frequency of mortality risk computations were increased to every 3 h with model performance that could improve the assessment of severity of illness, it could be utilized to monitor patients for significant mortality risk change.ObjectivesTo assess the performance of a dynamic method of updating mortality risk every 3 h using the Criticality Index-Mortality methodology and identify variables that are significant contributors to mortality risk predictions.PopulationThere were 8,399 pediatric ICU admissions with 312 (3.7%) deaths from January 1, 2018 to February 29, 2020. We randomly selected 75% of patients for training, 13% for validation, and 12% for testing.ModelA neural network was trained to predict hospital survival or death during or following an ICU admission. Variables included age, gender, laboratory tests, vital signs, medications categories, and mechanical ventilation variables. The neural network was calibrated to mortality risk using nonparametric logistic regression.ResultsDiscrimination assessed across all time periods found an AUROC of 0.851 (0.841–0.862) and an AUPRC was 0.443 (0.417–0.467). When assessed for performance every 3 h, the AUROCs had a minimum value of 0.778 (0.689–0.867) and a maximum value of 0.885 (0.841,0.862); the AUPRCs had a minimum value 0.148 (0.058–0.328) and a maximum value of 0.499 (0.229–0.769). The calibration plot had an intercept of 0.011, a slope of 0.956, and the R2 was 0.814. Comparison of observed vs. expected proportion of deaths revealed that 95.8% of the 543 risk intervals were not statistically significantly different. Construct validity assessed by death and survivor risk trajectories analyzed by mortality risk quartiles and 7 high and low risk diseases confirmed a priori clinical expectations about the trajectories of death and survivors.ConclusionsThe Criticality Index-Mortality computing mortality risk every 3 h for pediatric ICU patients has model performance that could enhance the clinical assessment of severity of illness. The overall Criticality Index-Mortality framework was effectively applied to develop an institutionally specific, and clinically relevant model for dynamic risk assessment of pediatric ICU patients.

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Section: Discussionmentioning

confidence: 69%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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The criticality Index-mortality: A dynamic machine learning prediction algorithm for mortality prediction in children cared for in an ICU

et al. 2022

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“… 24 , 25 , 26 , 27 The database has been used to study multiple aspects of pediatrics and pediatric medication practices. 1 , 3 , 28 , 29 , 30 , 31 eAppendix 1 in the Supplement provides a detailed description of the data cleaning process, definitions, and hospital characteristics.…”

Section: Methodsmentioning

confidence: 99%

Association of Intravenous Acetaminophen Administration With the Duration of Intravenous Opioid Use Among Hospitalized Pediatric Patients

et al. 2021

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IMPORTANCE Adoption of multimodal pain regimens that incorporate nonopioid analgesic medications to reduce inpatient opioid administration can prevent serious opioid-related adverse effects in children, including tolerance, withdrawal, delirium, and respiratory depression. Intravenous (IV) acetaminophen is in widespread pediatric use; however, its effectiveness as an opioid-sparing agent has not been evaluated in general pediatric inpatients. OBJECTIVE To determine if IV acetaminophen administered prior to IV opioids is associated with a reduction in the total duration of IV opioids administered compared with IV opioids administered without IV acetaminophen in general pediatric inpatients. DESIGN, SETTING, AND PARTICIPANTSThis comparative effectiveness research study included data on pediatric inpatients from 274 US hospitals between January 2011 and June 2016 collected from a national database. Outcomes were compared with a propensity score-matched analysis of pediatric inpatients administered IV opioids without IV acetaminophen (control) and those administered IV acetaminophen prior to IV opioids (intervention). Data were analyzed from January 2020 through October 2021. EXPOSURES Patients in the intervention group received IV acetaminophen prior to IV opioids. Patients in the control group received IV opioids without IV acetaminophen. MAIN OUTCOMES AND MEASURES Total duration of all IV opioids administered during a patient's hospitalization. RESULTS Of 893 293 pediatric inpatients, a total of 104 579 were included in analysis (median [IQR] age, 1.3 [0-14.7] years; 59 806 [57.2%] female; 21 485 [21.5%] African American, 56 309 [53.8%]White), of whom 18 197 (2.0%) received IV acetaminophen, and 287 504 (34.0%) received IV opioids. After applying exclusion criteria, among patients who received IV acetaminophen, 1739 (10.8%) received IV acetaminophen prior to IV opioids within a median (IQR) treatment time of 1.5 (0.02-7.3) hours. After propensity score matching produced comparable groups in the control and intervention groups (with 839 patients in each group), the multivariable model estimated a 15.5% shorter duration of IV opioid use in the intervention group, with an absolute IV opioid reduction of 7.5 hours (95% CI, 0.7-15.8 hours). CONCLUSIONS AND RELEVANCEIn this comparative effectiveness study, IV acetaminophen administered prior to IV opioids was associated with a reduction in IV opioid duration by 15.5%.Multimodal pain regimens that use IV acetaminophen prior to IV opioids could reduce IV opioid duration.

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“…Interestingly, the criticality index model estimates the probability of ICU care for a 6-h duration using a calibrated, deep neural network. The criticality index exhibited strong validity, which reflects the expected clinical course for five different patient groups (56). Additionally, a recurrent neural network was trained to continuously generate individual severity-of-illness scores from electronic medical record data by predicting the risk of ICU mortality.…”

Section: Future Impacts On Trajectories Of Picu Patients and Organiza...mentioning

confidence: 99%

Severity of illness and organ dysfunction scoring systems in pediatric critical care: The impacts on clinician's practices and the future

Recher¹,

Leteurtre²,

Canon³

et al. 2022

Front. Pediatr.

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Severity and organ dysfunction (OD) scores are increasingly used in pediatric intensive care units (PICU). Therefore, this review aims to provide 1/ an updated state-of-the-art of severity scoring systems and OD scores in pediatric critical care, which explains 2/ the performance measurement tools and the significance of each tool in clinical practice and provides 3/ the usefulness, limits, and impact on future scores in PICU. The following two pediatric systems have been proposed: the PRISMIV, is used to collect data between 2 h before PICU admission and the first 4 h after PICU admission; the PIM3, is used to collect data during the first hour after PICU admission. The PELOD-2 and SOFApediatric scores were the most common OD scores available. Scores used in the PICU should help clinicians answer the following three questions: 1/ Are the most severely ill patients dying in my service: a good discrimination allow us to interpret that there are the most severe patients who died in my service. 2/ Does the overall number of deaths observed in my department consistent with the severity of patients? The standard mortality ratio allow us to determine whether the total number of deaths observed in our service over a given period is in adequacy with the number of deaths predicted, by considering the severity of patients on admission? 3/ Does the number of deaths observed by severity level in my department consistent with the severity of patients? The calibration enabled us to determine whether the number of deaths observed according to the severity of patients at PICU admission in a department over a given period is in adequacy with the number of deaths predicted, according to the severity of the patients at PICU admission. These scoring systems are not interpretable at the patient level. Scoring systems are used to describe patients with PICU in research and evaluate the service's case mix and performance. Therefore, the prospect of automated data collection, which permits their calculation, facilitated by the computerization of services, is a necessity that manufacturers should consider.

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Severity Trajectories of Pediatric Inpatients Using the Criticality Index

Cited by 11 publications

References 35 publications

The criticality Index-mortality: A dynamic machine learning prediction algorithm for mortality prediction in children cared for in an ICU

The criticality Index-mortality: A dynamic machine learning prediction algorithm for mortality prediction in children cared for in an ICU

Association of Intravenous Acetaminophen Administration With the Duration of Intravenous Opioid Use Among Hospitalized Pediatric Patients

Severity of illness and organ dysfunction scoring systems in pediatric critical care: The impacts on clinician's practices and the future

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