Braun, Thomas

Abstract Introduction Distinct temperature patterns have long been recognized to correlate with fevers of differing etiologies. While the use of wearable sensors for high-frequency temperature monitoring (HFTM) on a near minute-by-minute basis has been shown to detect fevers earlier than standard-of-care nursing vital sign assessments in hospitalized patients, leveraging these high-resolution datasets to computationally identify unique digital signatures for real-time diagnosis of underlying fever etiology has not been widely explored. Diagnostic uncertainty is common in patients undergoing hematopoietic stem cell transplantation (HCT), with only 20–30% of febrile neutropenic episodes being microbiologically documented. We hypothesized that unique temperature patterns extracted from HFTM data collected during episodes of febrile neutropenia could be used to develop a supervised machine learning classifier capable of accurately predicting underlying fever etiology in HCT patients. Methods We analyzed 68 clinically independent fever episodes recorded in HCT patients (n=90) outfitted with an FDA-cleared wireless temperature sensor (TempTraq®, BlueSpark Technologies) that measured axillary temperature every 2 minutes throughout hospitalization. Time-series features were extracted from temperature traces spanning 1 hour before to 3 hours after fever onset and used to train a suite of machine-learning models to distinguish engraftment fevers from other fever etiologies. Model training and evaluation were performed using repeated stratified 5-fold patient-level cross-validation, yielding 100 train-test evaluations. Results Among all classification models, the logistic regression classifier provided the best overall performance and interpretability, achieving 94% specificity (95% CI, 0.84–1.0) for identifying engraftment fevers with a mean AUROC of 0.88 ± 0.10. Feature importance analysis demonstrated that both clinical variables and HFTM-derived temperature dynamics contributed to model performance, with a strong reliance on time-series features captured within the first 4 hours of fever onset. Conclusion Our study provides a demonstration that continuous temperature data collected from patients outfitted with wearable sensors can be leveraged not only for early fever detection but also for machine learning–based diagnosis of fever etiology. These findings suggest that dynamic temperature patterns contain clinically meaningful physiologic information that with further studies could support real-time diagnostic decision-making and guide safe de-escalation of empiric antibiotics during febrile neutropenia in patients undergoing intensive cancer therapy.

PURPOSE Preclinical studies suggest the combination of the poly(adenosine diphosphate[ADP]-ribose) polymerase (PARP) inhibitor olaparib and the ataxia telangiectasia–mutated and Rad3-related (ATR) inhibitor ceralasertib has synergistic antitumor activity in homologous recombination proficient (HRP) and homologous recombination repair gene mutation (HRRm) patients. This study aims to determine the efficacy of olaparib plus ceralasertib in metastatic castration-resistant prostate cancer (mCRPC) with and without HRRm. PATIENTS AND METHODS Thirty-five HRP and 12 HRRm PARP inhibitor–naïve patients with mCRPC progressing on ≥1 line of therapy or previous androgen receptor pathway inhibitor were enrolled. Patients received olaparib (300 mg twice daily) and ceralasertib (160 mg once daily, days 1-7) in 28-day cycles. HRRm was defined as biallelic inactivation of BRCA2 or BRCA1 , monoallelic inactivation of ATM , or germline inactivation of any of these genes. The primary end point was disease response rate (dRR; confirmed prostate-specific antigen decline ≥50% and/or complete/partial radiographic response by RECIST v1.1) in HRP patients. Secondary end points included dRR in HRRm patients, progression-free survival (PFS), and safety/toxicity. Two-stage designs were used. RESULTS Four of 35 (11%; 95% CI, 3.2 to 26.7) HRP and four of 12 HRRm patients responded. Median PFS was 8.2 months (95% CI, 5.3 to not reached) for HRP patients. Thirty-six percent of patients had ≥grade 3 toxicity, most commonly from anemia. Limitations include small, single-arm, nonrandomized trial design. CONCLUSION Combining ceralasertib with olaparib had limited activity in patients with HRP mCRPC. HRRm response rate was not greater than previous single-agent PARP inhibitor clinical trials.