Applying Artificial Intelligence to Identify Physiomarkers Predicting Severe Sepsis in the PICU*

Kamaleswaran, Rishikesan; Akbilgiç, Oğuz; Hallman, Madhura; West, Alina Nico; Davis, Robert L.; Shah, Samir

doi:10.1097/pcc.0000000000001666

Cited by 105 publications

(55 citation statements)

References 28 publications

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“…In parallel, we designed a data-driven approach. Different AI methods were available; we selected the Random Forest method because it is one of the most efficient strategies for providing a predictive algorithm in this context [18][19][20][21]. Importantly, the final algorithm was tasked with providing predictions for a novel population independent of the dataset used for the algorithm construction.…”

Section: Discussionmentioning

confidence: 99%

“…Step 1: patient data collection Prospective data collection was conducted in a single center over an 18-month period. The study complied with French law for observational studies, was approved by the ethics committee of the French Intensive Care Society (CE SRLF [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28], was approved by the Commission Nationale de l'Informatique et des Libertés (CNIL) for the treatment of personal health data. We gave written and oral information to patients or next-of-kin.…”

Section: Methodsmentioning

confidence: 99%

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Predicting the microbial cause of community-acquired pneumonia: can physicians or a data-driven method differentiate viral from bacterial pneumonia at patient presentation?

et al. 2020

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Background: Community-acquired pneumonia (CAP) requires urgent and specific antimicrobial therapy. However, the causal pathogen is typically unknown at the point when anti-infective therapeutics must be initiated. Physicians synthesize information from diverse data streams to make appropriate decisions. Artificial intelligence (AI) excels at finding complex relationships in large volumes of data. We aimed to evaluate the abilities of experienced physicians and AI to answer this question at patient admission: is it a viral or a bacterial pneumonia?Methods: We included patients hospitalized for CAP and recorded all data available in the first 3-h period of care (clinical, biological and radiological information). For this proof-of-concept investigation, we decided to study only CAP caused by a singular and identified pathogen. We built a machine learning model prediction using all collected data. Finally, an independent validation set of samples was used to test the pathogen prediction performance of: (i) a panel of three experts and (ii) the AI algorithm. Both were blinded regarding the final microbial diagnosis. Positive likelihood ratio (LR) values > 10 and negative LR values < 0.1 were considered clinically relevant. Results: We included 153 patients with CAP (70.6% men; 62 [51-73] years old; mean SAPSII, 37 [27-47]), 37% had viral pneumonia, 24% had bacterial pneumonia, 20% had a co-infection and 19% had no identified respiratory pathogen. We performed the analysis on 93 patients as co-pathogen and no-pathogen cases were excluded. The discriminant abilities of the AI approach were low to moderate (LR+ = 2.12 for viral and 6.29 for bacterial pneumonia), and the discriminant abilities of the experts were very low to low (LR+ = 3.81 for viral and 1.89 for bacterial pneumonia).

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Predicting the microbial cause of community-acquired pneumonia: can physicians or a data-driven method differentiate viral from bacterial pneumonia at patient presentation?

et al. 2020

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“…Complex prediction models, including promising artificial intelligence and machine learning approaches, have been developed for sepsis prediction. [20][21][22] However, despite the promise of these more sophisticated tools, there lie many challenges in their implementation including provider trust, algorithm and system maintenance, and cost. Alternatively, prior successful CDS interventions that relied only on standard EHR functionality without significant additional documentation have shown promise, 14,23 and we sought to emulate their success within the PICU setting.…”

Section: Background and Significancementioning

confidence: 99%

Design, Implementation, and Validation of a Pediatric ICU Sepsis Prediction Tool as Clinical Decision Support

et al. 2020

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Background Sepsis is an uncontrolled inflammatory reaction caused by infection. Clinicians in the pediatric intensive care unit (PICU) developed a paper-based tool to identify patients at risk of sepsis. To improve the utilization of the tool, the PICU team integrated the paper-based tool as a real-time clinical decision support (CDS) intervention in the electronic health record (EHR). Objective This study aimed to improve identification of PICU patients with sepsis through an automated EHR-based CDS intervention. Methods A prospective cohort study of all patients admitted to the PICU from May 2017 to May 2019. A CDS intervention was implemented in May 2018. The CDS intervention screened patients for nonspecific sepsis criteria, temperature dysregulation and a blood culture within 6 hours. Following the screening, an interruptive alert prompted nursing staff to complete a perfusion screen to assess for clinical signs of sepsis. The primary alert performance outcomes included sensitivity, specificity, and positive and negative predictive value. The secondary clinical outcome was completion of sepsis management tasks. Results During the 1-year post implementation period, there were 45.0 sepsis events per 1,000 patient days over 10,805 patient days. The sepsis alert identified 392 of the 436 sepsis episodes accurately with sensitivity of 92.5%, specificity of 95.6%, positive predictive value of 46.0%, and negative predictive value of 99.7%. Examining only patients with severe sepsis confirmed by chart review, test characteristics fell to a sensitivity of 73.3%, a specificity of 92.5%. Prior to the initiation of the alert, 18.6% (13/70) of severe sepsis patients received recommended sepsis interventions. Following the implementation, 34% (27/80) received these interventions in the time recommended, p = 0.04. Conclusion An EHR CDS intervention demonstrated strong performance characteristics and improved completion of recommended sepsis interventions.

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“…Prospective data collection was conducted in a single center over an 18-month period. The study complied with French law for observational studies, was approved by the ethics committee of the French Intensive Care Society (CE SRLF [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28], was approved by the Commission Nationale de l'Informatique et des Libertés (CNIL) for the treatment of personal health data. We gave written and oral information to patients or next-of-kin.…”

Section: Step 1: Patient Data Collectionmentioning

confidence: 99%

Predicting the microbial cause of community-acquired pneumonia: can physicians or a data-driven method differentiate viral from bacterial pneumonia at patient presentation?

Lhommet

Garot

Grammatico‐Guillon

et al. 2019

Preprint

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Background Severe community-acquired pneumonia (sCAP) requires urgent and specific antimicrobial therapy. However, the causal pathogen is typically unknown at the point when anti-infective therapeutics must be initiated. Physicians synthesize information from diverse data streams to make appropriate decisions. Artificial intelligence (AI) excels at finding complex relationships in large volumes of data. We aimed to evaluate the abilities of experienced physicians and AI to answer this question at patient admission: is it a viral or a bacterial pneumonia? Methods We included patients hospitalized for sCAP and recorded clinical/paraclinical data available in the first 3-hour period of care. We built a machine learning model prediction using all collected data. Finally, an independent validation set of samples was used to test the pathogen prediction performance of: ( i ) a panel of three experts and ( ii ) the AI algorithm. Both were blinded regarding the final microbial diagnosis. Positive likelihood ratio (LR) values >10 and negative LR values <0.1 were considered clinically relevant.Results We included 153 patients with sCAP (70.6% men; 62 [51-73] years old; mean SAPSII, 37 [27-47]). The discriminant abilities of the AI approach were low to moderate (LR+ = 2.12 for viral and 6.29 for bacterial pneumonia), and the discriminant abilities of the experts were very low to low (LR+ = 3.81 for viral and 1.89 for bacterial pneumonia).Conclusion Neither experts nor an AI algorithm can predict the microbial etiology of sCAP within the first hours of hospitalization when there is an urgent need to define the anti-infective therapeutic strategy.

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Applying Artificial Intelligence to Identify Physiomarkers Predicting Severe Sepsis in the PICU*

Abstract: Artificial intelligence can be used to predict the onset of severe sepsis using physiomarkers in critically ill children. Further, it may detect severe sepsis as early as 8 hours prior to a real-time electronic severe sepsis screening algorithm.

Cited by 105 publications

References 28 publications

Predicting the microbial cause of community-acquired pneumonia: can physicians or a data-driven method differentiate viral from bacterial pneumonia at patient presentation?

Predicting the microbial cause of community-acquired pneumonia: can physicians or a data-driven method differentiate viral from bacterial pneumonia at patient presentation?

Design, Implementation, and Validation of a Pediatric ICU Sepsis Prediction Tool as Clinical Decision Support

Predicting the microbial cause of community-acquired pneumonia: can physicians or a data-driven method differentiate viral from bacterial pneumonia at patient presentation?

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