The findings of this study advocate for the use of music in cancer care. Treatment benefits may depend on patient characteristics such as outlook on life and readiness to explore emotions related to the cancer experience.

Introduction The Consortium for Clinical Characterization of COVID-19 by EHR (4CE) is an international collaboration addressing COVID-19 with federated analyses of electronic health record (EHR) data. Objective We sought to develop and validate a computable phenotype for COVID-19 severity. Methods Twelve 4CE sites participated. First we developed an EHR-based severity phenotype consisting of six code classes, and we validated it on patient hospitalization data from the 12 4CE clinical sites against the outcomes of ICU admission and/or death. We also piloted an alternative machine-learning approach and compared selected predictors of severity to the 4CE phenotype at one site. Results The full 4CE severity phenotype had pooled sensitivity of 0.73 and specificity 0.83 for the combined outcome of ICU admission and/or death. The sensitivity of individual code categories for acuity had high variability - up to 0.65 across sites. At one pilot site, the expert-derived phenotype had mean AUC 0.903 (95% CI: 0.886, 0.921), compared to AUC 0.956 (95% CI: 0.952, 0.959) for the machine-learning approach. Billing codes were poor proxies of ICU admission, with as low as 49% precision and recall compared to chart review. Discussion We developed a severity phenotype using 6 code classes that proved resilient to coding variability across international institutions. In contrast, machine-learning approaches may overfit hospital-specific orders. Manual chart review revealed discrepancies even in the gold-standard outcomes, possibly due to heterogeneous pandemic conditions. Conclusion We developed an EHR-based severity phenotype for COVID-19 in hospitalized patients and validated it at 12 international sites.

The majority of septic arthritis infections at our institution were culture negative. Among patients with culture-negative infection, empiric antibiotics failed for 9% and necessitated a change in therapy. More sensitive diagnostic testing should be implemented to elucidate the causes of culture-negative septic arthritis in children.

Background Biomarkers can facilitate safe antibiotic discontinuation in critically ill patients without bacterial infection. Methods We tested the ability of a biomarker-based algorithm to reduce excess antibiotic administration in patients with systemic inflammatory response syndrome (SIRS) without bacterial infections (uninfected) in our pediatric intensive care unit (PICU). The algorithm suggested that PICU clinicians stop antibiotics if (1) C-reactive protein <4 mg/dL and procalcitonin <1 ng/mL at SIRS onset and (2) no evidence of bacterial infection by exam/testing by 48 hours. We evaluated excess broad-spectrum antibiotic use, defined as administration on days 3–9 after SIRS onset in uninfected children. Incidence rate ratios (IRRs) compared unadjusted excess length of therapy (LOT) in the 34 months before (Period 1) and 12 months after (Period 2) implementation of this algorithm, stratified by biomarker values. Segmented linear regression evaluated excess LOT among all uninfected episodes over time and between the periods. Results We identified 457 eligible SIRS episodes without bacterial infection, 333 in Period 1 and 124 in Period 2. When both biomarkers were below the algorithm’s cut-points (n = 48 Period 1, n = 31 Period 2), unadjusted excess LOT was lower in Period 2 (IRR, 0.53; 95% confidence interval, 0.30–0.93). Among all 457 uninfected episodes, there were no significant differences in LOT (coefficient 0.9, P = .99) between the periods on segmented regression. Conclusions Implementation of a biomarker-based algorithm did not decrease overall antibiotic exposure among all uninfected patients in our PICU, although exposures were reduced in the subset of SIRS episodes where biomarkers were low.