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February 1, 2023Wireless Fetal Monitoring
February 1, 2023Article of the Month – February 2023
Development and Validation of a Machine Learning Model to Estimate Bacterial Sepsis Among Immunocompromised Recipients...
Development and Validation of a Machine Learning Model to estimate Bacterial Sepsis among Inmunocompromised Recipients of Stem Cell Transplant
Lind ML, Mooney SJ, Carone M, et al.
Importance
Sepsis disproportionately affects recipients of allogeneic hematopoietic cell transplant (allo-HCT), and timely detection is crucial. However, the atypical presentation of sepsis within this population makes detection challenging, and existing clinical sepsis tools have limited prognostic value among this high-risk population.
Objectives
To develop a full risk factor (demographic, transplant, clinical, and laboratory factors) and clinical factor–specific automated bacterial sepsis decision support tool for recipients of allo-HCT with potential bloodstream infections (PBIs).
Design, Setting, and Participants
This prognostic study used data from adult recipients of allo-HCT transplanted at the Fred Hutchinson Cancer Research Center, Seattle, Washington, between June 2010 and June 2019, randomly divided into 70% modeling and 30% validation data sets. Tools were developed using the area under the curve (AUC) optimized SuperLearner, and their performance was compared with existing clinical sepsis tools: National Early Warning Score (NEWS), quick Sequential Organ Failure Assessment (qSOFA), and Systemic Inflammatory Response Syndrome (SIRS), using the validation data set. Data were analyzed between January and October 2020.
Main Outcomes and Measures
The primary outcome was high–sepsis risk bacteremia (culture-confirmed gram-negative species, Staphylococcus aureus, or Streptococcus spp bacteremia), and the secondary outcomes were 10- and 28-day mortality. Tool discrimination and calibration were examined using accuracy metrics and expected vs. observed probabilities.
Results
Between June 2010 and June 2019, 1943 recipients of allo-HCT received their first transplant, and 1594 recipients (median [interquartile range] age at transplant, 54 [43-63] years; 911 [57.2%] men; 1242 individuals [77.9%] identifying as White) experienced at least 1 PBI. Of 8131 observed PBIs, 238 (2.9%) were high–sepsis risk bacteremia. Compared with high–sepsis risk bacteremia, the full decision support tool had the highest AUC (0.85; 95% CI, 0.81-0.89), followed by the clinical factor–specific tool (0.72; 95% CI, 0.66-0.78). SIRS had the highest AUC of existing tools (0.64; 95% CI, 0.57-0.71). The full decision support tool had the highest AUCs for PBIs identified in inpatient (0.82; 95% CI, 0.76-0.89) and outpatient (0.82; 95% CI, 0.75-0.89) settings and for 10-day (0.85; 95% CI, 0.79-0.91) and 28-day (0.80; 95% CI, 0.75-0.84) mortality.
Conclusions and Relevance
These findings suggest that compared with existing tools and the clinical factor–specific tool, the full decision support tool had superior prognostic accuracy for the primary (high–sepsis risk bacteremia) and secondary (short-term mortality) outcomes in inpatient and outpatient settings. If used at the time of culture collection, the full decision support tool may inform more timely sepsis detection among recipients of allo-HCT.
Relevance to Healthcare Field
Immunocompromised patients are at high risk of developing and dying from sepsis. This study developed two automated bacterial sepsis tools using a convening technique, the Super Learner, for recipients of allo-HCT with possible bloodstream infections (PBIs): The super HCT bacterial sepsis learner (SHBSL) and the clinical factor-specific super HCT bacterial sepsis Learner (C-SHBSL). They can be integrated into the Electronic Medical Record (EMR) and provide adequate management based on the patient-specific sepsis risk probability estimates or patients at high risk at the time of the blood culture collection.
These models were adjusted with respect to the area under the receiver characteristic curve (AUROC). When comparing AUROC and predictive accuracy metrics (sensitivity, specificity) to existing sepsis assessment tools (the National Early Warning Response (NEWS), quick Sequential Organ Failure Assessment (qSOFA), and Systemic Inflammatory Response Syndrome (SIRS)), SHBSL and C-SHBSL had the highest and second highest AUROC with higher predictive accuracy as well. Although this study was based on transplanted patients only, this technique could be used in different immunocompromised patients, adding or removing some parameters depending on the type of immunocompromise. In this study, SHBSL proved to better estimate sepsis risk and high-risk bacteremia among allo-HCT patients with PBIs. With this tool, we can now help and prevent unfavorable outcomes, which will increase the survival rate of the patients.