Theresa L. Walunas

ObjectiveTo identify clinical and laboratory features associated with severe outcomes in multisystem inflammatory syndrome in children (MIS-C) and to characterize latent clinical subphenotypes.Study DesignThis retrospective multicenter cohort study analyzed 147 patients diagnosed with MIS-C between May 2020 and October 2023 across three Midwestern hospital systems. Cases were manually adjudicated. Associations between symptoms and laboratory features were evaluated against eight outcomes, including vasoactive agent use, external oxygenation, ICU admission, cardiac abnormalities, and lengths of stay. Univariate screening adjusting for demographic covariates was followed by multivariable regression to identify independent associations. Spectral clustering with consensus validation identified clinical subphenotypes.ResultsNeutrophilia was independently associated with ICU admission (adjusted OR (aOR) 6.0, p<0.001), vasoactive agent use (aOR 5.3, p=0.001), external oxygenation (aOR 7.4, p=0.002), and ejection fraction <55% (aOR 3.2, p=0.026). Altered mental status was associated with vasoactive agent use (aOR 10.0, p=0.007) and external oxygenation (aOR 4.7, p=0.05). Dyspnea (aOR 3.9, p=0.027) was associated with coronary abnormalities. Clustering identified three subphenotypes. Cluster 3 showed widespread laboratory abnormalities and the highest rates of severe management and cardiac outcomes. Cluster 2 was younger with milder abnormalities and a Kawasaki-like profile. Cluster 1 was older with moderate abnormalities but had outcomes similar to Cluster 3.ConclusionsNeutrophilia has strong, underrecognized associations with severe MIS-C. Subphenotyping suggests older patients have distinct clinical trajectories with divergent risk. Phenotype-based risk stratification may outperform single risk factors. These findings may inform approaches to future post-infectious inflammatory syndromes.

Pneumonia is the leading cause of death from infectious disease worldwide. The diagnosis and treatment of patients with pneumonia lag behind other major conditions, relying on syndromic definitions that lack molecular resolution and ignore underlying endotypes. We sought to test the hypothesis that dynamic pathogen-specific host responses in the alveolar space represent distinct pneumonia endotypes linked to different clinical features and outcomes. We prospectively enrolled a cohort of 690 patients (including immunocompromised patients) with known or suspected pneumonia receiving mechanical ventilation in whom the etiology of pneumonia was determined by gold-standard analysis of distal lung fluid obtained by bronchoalveolar lavage (BAL) combined with clinical adjudication. From these patients, we analyzed 792 BAL fluid samples, including 310 serial samples, using flow cytometry (482 patients) and single-cell RNA-sequencing (170 patients; 263 samples, complemented by 9 healthy controls and 25 post-COVID-19 patients, yielding ~2.4 million single cells across 28 cell types), and extracted daily clinical data from the electronic health record (>15,000 patient-days). We used machine learning models to identify pathogen-specific host responses in the transcriptome of alveolar immune cells that were associated with changes in alveolar cell abundance and clinical features. Our results suggest that therapeutic strategies for pneumonia should be individualized to specific host-pathogen interactions.

Many analyses of critically ill patients focus on admission features and discharge outcomes, overlooking daily events and intercurrent complications. Pneumonia is a syndromic entity that is difficult to adjudicate outcomes without clinical expertise. Here, we examine clinical features on a day-by-day basis, mirroring the practice of daily multidisciplinary rounds, with annotated pneumonia episodes and outcomes. The Successful Clinical Response in Pneumonia Therapy (SCRIPT) CarpeDiem Dataset includes 21,931 patient-hospital-days from 704 patients enrolled in the SCRIPT study between June 2018 and May 2023. These patients were all receiving mechanical ventilation and underwent bronchoalveolar lavage to diagnose and establish the etiology of their pneumonia or an alternative diagnosis. The dataset captures patient demographics, daily clinical parameters, including vital signs, laboratory results, and mechanical support data, detailed pneumonia episode results adjudicated by critical care physicians, and outcomes. All data are de-identified per HIPAA Safe Harbor guidelines. This dataset offers a unique resource for analyzing the clinical trajectories of patients with severe pneumonia.

Purpose: Ventilator-associated pneumonia (VAP) remains one of the most serious hospital-acquired infections in the intensive care unit (ICU), with high morbidity and mortality. Early identification of patients at risk for developing VAP could enable timely diagnostics and intervention. However, current clinical tools are limited in their ability to detect early physiologic signals preceding VAP onset. We aimed to build supervised machine learning models to predict short term onset of VAP. Methods: We analyzed electronic health record data from a prospective observational cohort of ICU patients, where VAP was adjudicated using a standardized published protocol by a panel of critical care physicians. Clinical features (including vital signs, ventilator settings, laboratory values, and support devices) were extracted for each patient-ICU-day. We explored unsupervised clustering to characterize feature dynamics associated with VAP onset. We built multiple machine learning models across different prediction windows (3, 5, 7 days before VAP). We examined model performance in two external cohorts, MIMIC-IV and secondary analysis of the AMIKINHAL trial. Results were evaluated with discrimination metrics such as AUROC. Results: The internal cohort included 507 patients with BAL-confirmed diagnoses: 261 developed VAP and 246 did not have VAP. Visualization using clustering identified distinct physiologic states enriched for VAP-labeled days. The best-performing model achieved an AUROC of 0.866 in predicting VAP up to seven days before clinical diagnosis. Temporal model probability trajectories showed rising model confidence in the days leading up to VAP. On external validation in MIMIC-IV, the best model achieved an AUROC of 0.817 for forecasting VAP within five days. There was low feature overlap with the AMIKINHAL trial data, leading to poor model performance. Feature analysis revealed that platelet count, positive end-expiratory pressure (PEEP), ventilator duration, and inflammatory markers were key drivers of model predictions. Conclusions: Machine learning models trained on routinely collected ICU data with careful labeling can anticipate VAP onset up to a week in advance with strong predictive performance. Model performance generalized to data from an entirely different hospital system despite differences in practice and labeling patterns, but did not perform well when there was poor feature overlap. Future work should focus on real-time prospective evaluation.

Machine learning (ML)—a field of study dedicated to the principled extraction of knowledge from complex data—can benefit implementation science

Background: Computational phenotyping from electronic health records (EHRs) is essential for clinical research, decision support, and quality/population health assessment, but the proliferation of algorithms for the same conditions makes it difficult to identify which algorithm is most appropriate for reuse. Objective: To develop a framework for assessing phenotyping algorithm fitness for purpose and reuse. Fitness for Purpose: Phenotyping algorithms are fit for purpose when they identify the intended population with performance characteristics appropriate for the intended application. Fitness for Reuse: Phenotyping algorithms are fit for reuse when the algorithm is implementable and generalizable—that is, it identifies the same intended population with similar performance characteristics when applied to a new setting. Conclusions: The PhenoFit framework provides a structured approach to evaluate and adapt phenotyping algorithms for new contexts increasing efficiency and consistency of identifying patient populations from EHRs.