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1. Heterogeneity in the Effect of Early Goal-Directed Therapy for Septic Shock: A Secondary Analysis of Two Multicenter International Trials

   https://doi.org/10.1097/CCM.0000000000006463  

   Shah, Faraaz Ali; Talisa, Victor B; Chang, Chung-Chou H; Triantafyllou, Sofia; Tang, Lu; Mayr, Florian B; Higgins, Alisa M; Peake, Sandra L; Mouncey, Paul; Harrison, David A; et al (January 2025, Critical Care Medicine)

   OBJECTIVES:The optimal approach for resuscitation in septic shock remains unclear despite multiple randomized controlled trials (RCTs). Our objective was to investigate whether previously uncharacterized variation across individuals in their response to resuscitation strategies may contribute to conflicting average treatment effects in prior RCTs. DESIGN:We randomly split study sites from the Australian Resuscitation of Sepsis Evaluation (ARISE) and Protocolized Care for Early Septic Shock (ProCESS) trials into derivation and validation cohorts. We trained machine learning models to predict individual absolute risk differences (iARDs) in 90-day mortality in derivation cohorts and tested for heterogeneity of treatment effect (HTE) in validation cohorts and swapped these cohorts in sensitivity analyses. We fit the best-performing model in a combined dataset to explore roles of patient characteristics and individual components of early goal-directed therapy (EGDT) to determine treatment responses. SETTING:Eighty-one sites in Australia, New Zealand, Hong Kong, Finland, Republic of Ireland, and the United States. PATIENTS:Adult patients presenting to the emergency department with severe sepsis or septic shock. INTERVENTIONS:EGDT vs. usual care. MEASUREMENTS AND MAIN RESULTS:A local-linear random forest model performed best in predicting iARDs. In the validation cohort, HTE was confirmed, evidenced by an interaction between iARD prediction and treatment (p< 0.001). When patients were grouped based on predicted iARDs, treatment response increased from the lowest to the highest quintiles (absolute risk difference [95% CI], –8% [–19% to 4%] and relative risk reduction, 1.34 [0.89–2.01] in quintile 1 suggesting harm from EGDT, and 12% [1–23%] and 0.64 [0.42–0.96] in quintile 5 suggesting benefit). Sensitivity analyses showed similar findings. Pre-intervention albumin contributed the most to HTE. Analyses of individual EGDT components were inconclusive. CONCLUSIONS:Treatment response to EGDT varied across patients in two multicenter RCTs with large benefits for some patients while others were harmed. Patient characteristics, including albumin, were most important in identifying HTE. 

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2. From Sleep Patterns to Heart Rhythms: Predicting Atrial Fibrillation from Overnight Polysomnograms

   https://doi.org/10.1101/2024.06.04.24308444  

   Koscova, Zuzana; Rad, Ali Bahrami; Nasiri, Samaneh; Reyna, Matthew A; Sameni, Reza; Trotti, Lynn M; Sun, Haoqi; Turley, Niels; Stone, Katie L; Thomas, Robert J; et al (June 2024, medRxiv)

   Abstract BackgroundAtrial fibrillation (AF) is often asymptomatic and thus under-observed. Given the high risks of stroke and heart failure among patients with AF, early prediction and effective management are crucial. Importantly, obstructive sleep apnea is highly prevalent among AF patients (60–90%); therefore, electrocardiogram (ECG) analysis from polysomnography (PSG), a standard diagnostic tool for subjects with suspected sleep apnea, presents a unique opportunity for the early prediction of AF. Our goal is to identify individuals at a high risk of developing AF in the future from a single-lead ECG recorded during standard PSGs. MethodsWe analyzed 18,782 single-lead ECG recordings from 13,609 subjects at Massachusetts General Hospital, identifying AF presence using ICD-9/10 codes in medical records. Our dataset comprises 15,913 recordings without a medical record for AF and 2,056 recordings from patients who were first diagnosed with AF between 1 day to 15 years after the PSG recording. The PSG data were partitioned into training, validation, and test cohorts. In the first phase, a signal quality index (SQI) was calculated in 30-second windows and those with SQI<0.95 were removed. From each remaining window, 150 hand-crafted features were extracted from time, frequency, time-frequency domains, and phase-space reconstructions of the ECG. A compilation of 12 statistical features summarized these window-specific features per recording, resulting in 1,800 features. We then updated a pre-trained deep neural network and data from the PhysioNet Challenge 2021 using transfer-learning to discriminate between recordings with and without AF using the same Challenge data. The model was applied to the PSG ECGs in 16-second windows to generate the probability of AF for each window. From the resultant probability sequence, 13 statistical features were extracted. Subsequently, we trained a shallow neural network to predict future AF using the extracted ECG and probability features. ResultsOn the test set, our model demonstrated a sensitivity of 0.67, specificity of 0.81, and precision of 0.3 for predicting AF. Further, survival analysis for AF outcomes, using the log-rank test, revealed a hazard ratio of 8.36 (p-value of 1.93 × 10⁻⁵²). ConclusionsOur proposed ECG analysis method, utilizing overnight PSG data, shows promise in AF prediction despite a modest precision indicating the presence of false positive cases. This approach could potentially enable low-cost screening and proactive treatment for high-risk patients. Ongoing refinement, such as integrating additional physiological parameters could significantly reduce false positives, enhancing its clinical utility and accuracy. 

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3. Comparative oncology chemosensitivity assay for personalized medicine using low-coherence digital holography of dynamic light scattering from cancer biopsies

   https://doi.org/10.1038/s41598-024-52404-w  

   Hua, Zhen; Li, Zhe; Lim, Dawith; Ajrouch, Ali; Karkash, Ahmad; Jalal, Shadia; Childress, Michael; Turek, John; Nolte, David (February 2024, Scientific Reports)

   Abstract Nearly half of cancer patients who receive standard-of-care treatments fail to respond to their first-line chemotherapy, demonstrating the pressing need for improved methods to select personalized cancer therapies. Low-coherence digital holography has the potential to fill this need by performing dynamic contrast OCT on living cancer biopsies treated ex vivo with anti-cancer therapeutics. Fluctuation spectroscopy of dynamic light scattering under conditions of holographic phase stability captures ultra-low Doppler frequency shifts down to 10 mHz caused by light scattering from intracellular motions. In the comparative preclinical/clinical trials presented here, a two-species (human and canine) and two-cancer (esophageal carcinoma and B-cell lymphoma) analysis of spectral phenotypes identifies a set of drug response characteristics that span species and cancer type. Spatial heterogeneity across a centimeter-scale patient biopsy sample is assessed by measuring multiple millimeter-scale sub-samples. Improved predictive performance is achieved for chemoresistance profiling by identifying red-shifted sub-samples that may indicate impaired metabolism and removing them from the prediction analysis. These results show potential for using biodynamic imaging for personalized selection of cancer therapy. 

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4. A supervised machine learning model for identifying predictive factors for recommending head and neck cancer surgery

   https://doi.org/10.1002/hed.27674  

   Jiam, Max L; Xin, Kevin Z; Ha, Patrick K; Jiam, Nicole T (May 2024, Head & Neck)

   Abstract BackgroundNew patient referrals are often processed by practice coordinators with little‐to‐no medical background. Treatment delays due to incorrect referral processing, however, have detrimental consequences. Identifying variables that are associated with a higher likelihood of surgical oncological resection may improve patient referral processing and expedite the time to treatment. The study objective is to develop a supervised machine learning (ML) platform that identifies relevant variables associated with head and neck surgical resection. MethodsA retrospective cohort study was conducted on 64 222 patient datapoints from the SEER database. ResultsThe random forest ML model correctly classified patients who were offered head and neck surgery with an 81% accuracy rate. The sensitivity and specificity rates were 86% and 71%. The positive and negative predictive values were 85% and 73%. ConclusionsML modeling accurately predicts head and neck cancer surgery recommendations based on patient and cancer information from a large population‐based dataset. ML adjuncts for referral processing may decrease the time to treatment for patients with cancer. 

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5. Evidence for machine learning guided early prediction of acute outcomes in the treatment of depressed children and adolescents with antidepressants

   https://doi.org/10.1111/jcpp.13580  

   Athreya, Arjun P.; Vande Voort, Jennifer L.; Shekunov, Julia; Rackley, Sandra J.; Leffler, Jarrod M.; McKean, Alastair J.; Romanowicz, Magdalena; Kennard, Betsy D.; Emslie, Graham J.; Mayes, Taryn; et al (March 2022, Journal of Child Psychology and Psychiatry)

   BackgroundThe treatment of depression in children and adolescents is a substantial public health challenge. This study examined artificial intelligence tools for the prediction of early outcomes in depressed children and adolescents treated with fluoxetine, duloxetine, or placebo. MethodsThe study samples included training datasets (N = 271) from patients with major depressive disorder (MDD) treated with fluoxetine and testing datasets from patients with MDD treated with duloxetine (N = 255) or placebo (N = 265). Treatment trajectories were generated using probabilistic graphical models (PGMs). Unsupervised machine learning identified specific depressive symptom profiles and related thresholds of improvement during acute treatment. ResultsVariation in six depressive symptoms (difficulty having fun, social withdrawal, excessive fatigue, irritability, low self‐esteem, and depressed feelings) assessed with the Children’s Depression Rating Scale‐Revised at 4–6 weeks predicted treatment outcomes with fluoxetine at 10–12 weeks with an average accuracy of 73% in the training dataset. The same six symptoms predicted 10–12 week outcomes at 4–6 weeks in (a) duloxetine testing datasets with an average accuracy of 76% and (b) placebo‐treated patients with accuracies of 67%. In placebo‐treated patients, the accuracies of predicting response and remission were similar to antidepressants. Accuracies for predicting nonresponse to placebo treatment were significantly lower than antidepressants. ConclusionsPGMs provided clinically meaningful predictions in samples of depressed children and adolescents treated with fluoxetine or duloxetine. Future work should augment PGMs with biological data for refined predictions to guide the selection of pharmacological and psychotherapeutic treatment in children and adolescents with depression. 

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