Computerized Decision Support Systems for Mechanical Ventilation in Children

Sward, Katherine; Newth, Christopher J. L.

doi:10.1055/s-0035-1568161

Cited by 5 publications

(2 citation statements)

References 65 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This clinical decision support tool has the potential to deliver relevant higher-order information on patient stability. When incorporated into a robust clinical decision support protocol, ID o 2 may be able to inform earlier weaning decisions and therefore improve on the accuracy and efficiency of care de-escalation ( 12 , 13 , 21 , 23 , 24 ). Future studies should aim to assess the clinical relevance of risk analytic algorithms during the de-escalation phases of care.…”

Section: Discussionmentioning

confidence: 99%

“…By continuously analyzing real-time patient data to determine risk of underlying unstable physiology, these algorithms may conversely inform patient stability during de-escalation of care such as the weaning of vasoactive drugs and inotropes. By improving the efficiency of care de-escalation, patients may have shorter exposure to the ICU environment, shorter lengths of stay, and globally improved outcomes (12)(13)(14).…”

mentioning

confidence: 99%

See 1 more Smart Citation

Use of a Risk Analytic Algorithm to Inform Weaning From Vasoactive Medication in Patients Following Pediatric Cardiac Surgery

Goldsmith

Futterman

Gazit

et al. 2021

Critical Care Explorations

View full text Add to dashboard Cite

Advanced clinical decision support tools, such as real-time risk analytic algorithms, show promise in assisting clinicians in making more efficient and precise decisions. These algorithms, which calculate the likelihood of a given underlying physiology or future event, have predominantly been used to identify the risk of impending clinical decompensation. There may be broader clinical applications of these models. Using the inadequate delivery of oxygen index, a U.S. Food and Drug Administration-approved risk analytic algorithm predicting the likelihood of low cardiac output state, the primary objective was to evaluate the association of inadequate delivery of oxygen index with success or failure of weaning vasoactive support in postoperative cardiac surgery patients. DESIGN:Multicenter retrospective cohort study. SETTING: Three pediatric cardiac ICUs at tertiary academic children's hospitals. PATIENTS:Infants and children greater than 2 kg and less than 12 years following cardiac surgery, who required vasoactive infusions for greater than 6 hours in the postoperative period. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS:Postoperative patients were identified who successfully weaned off initial vasoactive infusions (n = 2,645) versus those who failed vasoactive wean (required reinitiation of vasoactive, required mechanical circulatory support, renal replacement therapy, suffered cardiac arrest, or died) (n = 516). Inadequate delivery of oxygen index for final 6 hours of vasoactive wean was captured. Inadequate delivery of oxygen index was significantly elevated in patients with failed versus successful weans (inadequate delivery of oxygen index 11.6 [sd 19.0] vs 6.4 [sd 12.6]; p < 0.001). Mean 6-hour inadequate delivery of oxygen index greater than 50 had strongest association with failed vasoactive wean (adjusted odds ratio, 4.0; 95% CI, 2.5-6.6). In patients who failed wean, reinitiation of vasoactive support was associated with concomitant fall in inadequate delivery of oxygen index (11.1 [sd 18] vs 8.9 [sd 16]; p = 0.007). CONCLUSIONS:During the de-escalation phase of postoperative cardiac ICU management, elevation of the real-time risk analytic model, inadequate delivery of oxygen index, was associated with failure to wean off vasoactive infusions. Future studies should prospectively evaluate utility of risk analytic models as clinical decision support tools in de-escalation practices in critically ill patients.KEY WORDS: clinical decision support systems; critical illness; low cardiac output; pediatric intensive care unit; physiologic monitoring; risk assessment C ritically ill pediatric patients with congenital and acquired cardiovascular disease have high rates of inhospital cardiac arrest and death (1). Clinical decision support tools aimed at identifying cardiac patients at high risk of clinical deterioration (2-6) have been described in the literature,

show abstract

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

Use of a Risk Analytic Algorithm to Inform Weaning From Vasoactive Medication in Patients Following Pediatric Cardiac Surgery

Goldsmith

Futterman

Gazit

et al. 2021

Critical Care Explorations

View full text Add to dashboard Cite

show abstract

Respiratory decision support systems

Chouvarda

Perantoni

Steiropoulos

2022

Wearable Sensing and Intelligent Data Analysis for Respiratory Management

View full text Add to dashboard Cite

Continuous noninvasive blood gas estimation in critically ill pediatric patients with respiratory failure

Dong

Xu-Wilson

Conroy

et al. 2022

Sci Rep

View full text Add to dashboard Cite

Patients supported by mechanical ventilation require frequent invasive blood gas samples to monitor and adjust the level of support. We developed a transparent and novel blood gas estimation model to provide continuous monitoring of blood pH and arterial CO2 in between gaps of blood draws, using only readily available noninvasive data sources in ventilated patients. The model was trained on a derivation dataset (1,883 patients, 12,344 samples) from a tertiary pediatric intensive care center, and tested on a validation dataset (286 patients, 4030 samples) from the same center obtained at a later time. The model uses pairwise non-linear interactions between predictors and provides point-estimates of blood gas pH and arterial CO2 along with a range of prediction uncertainty. The model predicted within Clinical Laboratory Improvement Amendments of 1988 (CLIA) acceptable blood gas machine equivalent in 74% of pH samples and 80% of PCO2 samples. Prediction uncertainty from the model improved estimation accuracy by 15% by identifying and abstaining on a minority of high-uncertainty samples. The proposed model estimates blood gas pH and CO2 accurately in a large percentage of samples. The model’s abstention recommendation coupled with ranked display of top predictors for each estimation lends itself to real-time monitoring of gaps between blood draws, and the model may help users determine when a new blood draw is required and delay blood draws when not needed.

show abstract

Computerized Decision Support Systems for Mechanical Ventilation in Children

Cited by 5 publications

References 65 publications

Use of a Risk Analytic Algorithm to Inform Weaning From Vasoactive Medication in Patients Following Pediatric Cardiac Surgery

Use of a Risk Analytic Algorithm to Inform Weaning From Vasoactive Medication in Patients Following Pediatric Cardiac Surgery

Respiratory decision support systems

Continuous noninvasive blood gas estimation in critically ill pediatric patients with respiratory failure

Contact Info

Product

Resources

About