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Competing Interests: Dr. Churpek disclosed that he is a named inventor on a patent for an early warning score algorithm (US11410777) and receives royalties for this intellectual property from the University of Chicago; he received support for article research from the National Institutes of Health. Dr. Spicer has disclosed that she does not have any potential conflicts of interest.

Purpose of Review: To date, most randomized clinical trials in critical care report neutral overall results. However, research as to whether heterogenous responses underlie these results and give opportunity for personalized care is gaining momentum but has yet to inform clinical practice guidance. Thus, we aim to provide an overview of methodological approaches to estimating heterogeneity of treatment effects in randomized trials and conjecture about future paths to application in patient care., Recent Findings: Despite their limitations, traditional subgroup analyses are still the most reported approach. More recent methods based on subphenotyping, risk modeling and effect modeling are still uncommonly embedded in primary reports of clinical trials but have provided useful insights in secondary analyses. However, further simulation studies and subsequent guidelines are needed to ascertain the most efficient and robust manner to validate these results for eventual use in practice., Summary: There is an increasing interest in approaches that can identify heterogeneity in treatment effects from randomized clinical trials, extending beyond traditional subgroup analyses. While prospective validation in further studies is still needed, these approaches are promising tools for design, interpretation, and implementation of clinical trial results., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Objective: A trial comparing extended-release naltrexone and sublingual buprenorphine-naloxone demonstrated higher relapse rates in individuals randomized to extended-release naltrexone. The effectiveness of treatment might vary based on patient characteristics. We hypothesized that causal machine learning would identify individualized treatment effects for each medication., Methods: This is a secondary analysis of a multicenter randomized trial that compared the effectiveness of extended-release naltrexone versus buprenorphine-naloxone for preventing relapse of opioid misuse. Three machine learning models were derived using all trial participants with 50% randomly selected for training (n = 285) and the remaining 50% for validation. Individualized treatment effect was measured by the Qini value and c-for-benefit, with the absence of relapse denoting treatment success. Patients were grouped into quartiles by predicted individualized treatment effect to examine differences in characteristics and the observed treatment effects., Results: The best-performing model had a Qini value of 4.45 (95% confidence interval, 1.02-7.83) and a c-for-benefit of 0.63 (95% confidence interval, 0.53-0.68). The quartile most likely to benefit from buprenorphine-naloxone had a 35% absolute benefit from this treatment, and at study entry, they had a high median opioid withdrawal score ( P < 0.001), used cocaine on more days over the prior 30 days than other quartiles ( P < 0.001), and had highest proportions with alcohol and cocaine use disorder ( P ≤ 0.02). Quartile 4 individuals were predicted to be most likely to benefit from extended-release naltrexone, with the greatest proportion having heroin drug preference ( P = 0.02) and all experiencing homelessness ( P < 0.001)., Conclusions: Causal machine learning identified differing individualized treatment effects between medications based on characteristics associated with preventing relapse., Competing Interests: Conflicts of Interest: None., (Copyright © 2024 American Society of Addiction Medicine.)

Importance: Among critically ill adults, randomized trials have not found oxygenation targets to affect outcomes overall. Whether the effects of oxygenation targets differ based on an individual's characteristics is unknown., Objective: To determine whether an individual's characteristics modify the effect of lower vs higher peripheral oxygenation-saturation (Spo2) targets on mortality., Design, Setting, and Participants: A machine learning model to predict the effect of treatment with a lower vs higher Spo2 target on mortality for individual patients was derived in the Pragmatic Investigation of Optimal Oxygen Targets (PILOT) trial and externally validated in the Intensive Care Unit Randomized Trial Comparing Two Approaches to Oxygen Therapy (ICU-ROX) trial. Critically ill adults received invasive mechanical ventilation in an intensive care unit (ICU) in the United States between July 2018 and August 2021 for PILOT (n = 1682) and in 21 ICUs in Australia and New Zealand between September 2015 and May 2018 for ICU-ROX (n = 965)., Exposures: Randomization to a lower vs higher Spo2 target group., Main Outcome and Measure: 28-Day mortality., Results: In the ICU-ROX validation cohort, the predicted effect of treatment with a lower vs higher Spo2 target for individual patients ranged from a 27.2% absolute reduction to a 34.4% absolute increase in 28-day mortality. For example, patients predicted to benefit from a lower Spo2 target had a higher prevalence of acute brain injury, whereas patients predicted to benefit from a higher Spo2 target had a higher prevalence of sepsis and abnormally elevated vital signs. Patients predicted to benefit from a lower Spo2 target experienced lower mortality when randomized to the lower Spo2 group, whereas patients predicted to benefit from a higher Spo2 target experienced lower mortality when randomized to the higher Spo2 group (likelihood ratio test for effect modification P = .02). The use of a Spo2 target predicted to be best for each patient, instead of the randomized Spo2 target, would have reduced the absolute overall mortality by 6.4% (95% CI, 1.9%-10.9%)., Conclusion and Relevance: Oxygenation targets that are individualized using machine learning analyses of randomized trials may reduce mortality for critically ill adults. A prospective trial evaluating the use of individualized oxygenation targets is needed.

Objectives: To develop and externally validate machine learning models using structured and unstructured electronic health record data to predict postoperative acute kidney injury (AKI) across inpatient settings., Materials and Methods: Data for adult postoperative admissions to the Loyola University Medical Center (2009-2017) were used for model development and admissions to the University of Wisconsin-Madison (2009-2020) were used for validation. Structured features included demographics, vital signs, laboratory results, and nurse-documented scores. Unstructured text from clinical notes were converted into concept unique identifiers (CUIs) using the clinical Text Analysis and Knowledge Extraction System. The primary outcome was the development of Kidney Disease Improvement Global Outcomes stage 2 AKI within 7 days after leaving the operating room. We derived unimodal extreme gradient boosting machines (XGBoost) and elastic net logistic regression (GLMNET) models using structured-only data and multimodal models combining structured data with CUI features. Model comparison was performed using the receiver operating characteristic curve (AUROC), with Delong's test for statistical differences., Results: The study cohort included 138 389 adult patient admissions (mean [SD] age 58 [16] years; 11 506 [8%] African-American; and 70 826 [51%] female) across the 2 sites. Of those, 2959 (2.1%) developed stage 2 AKI or higher. Across all data types, XGBoost outperformed GLMNET (mean AUROC 0.81 [95% confidence interval (CI), 0.80-0.82] vs 0.78 [95% CI, 0.77-0.79]). The multimodal XGBoost model incorporating CUIs parameterized as term frequency-inverse document frequency (TF-IDF) showed the highest discrimination performance (AUROC 0.82 [95% CI, 0.81-0.83]) over unimodal models (AUROC 0.79 [95% CI, 0.78-0.80])., Discussion: A multimodality approach with structured data and TF-IDF weighting of CUIs increased model performance over structured data-only models., Conclusion: These findings highlight the predictive power of CUIs when merged with structured data for clinical prediction models, which may improve the detection of postoperative AKI., Competing Interests: Dr Churpek is a named inventor on a patent for a risk stratification algorithm for hospitalized patients (U.S. patent # 11410777). The remaining authors have disclosed that they do not have any potential conflicts of interest., (© The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Objectives: To identify and validate novel COVID-19 subphenotypes with potential heterogenous treatment effects (HTEs) using electronic health record (EHR) data and 33 unique biomarkers., Design: Retrospective cohort study of adults presenting for acute care, with analysis of biomarkers from residual blood collected during routine clinical care. Latent profile analysis (LPA) of biomarker and EHR data identified subphenotypes of COVID-19 inpatients, which were validated using a separate cohort of patients. HTE for glucocorticoid use among subphenotypes was evaluated using both an adjusted logistic regression model and propensity matching analysis for in-hospital mortality., Setting: Emergency departments from four medical centers., Patients: Patients diagnosed with COVID-19 based on International Classification of Diseases , 10th Revision codes and laboratory test results., Interventions: None., Measurements and Main Results: Biomarker levels generally paralleled illness severity, with higher levels among more severely ill patients. LPA of 522 COVID-19 inpatients from three sites identified two profiles: profile 1 ( n = 332), with higher levels of albumin and bicarbonate, and profile 2 ( n = 190), with higher inflammatory markers. Profile 2 patients had higher median length of stay (7.4 vs 4.1 d; p < 0.001) and in-hospital mortality compared with profile 1 patients (25.8% vs 4.8%; p < 0.001). These were validated in a separate, single-site cohort ( n = 192), which demonstrated similar outcome differences. HTE was observed ( p = 0.03), with glucocorticoid treatment associated with increased mortality for profile 1 patients (odds ratio = 4.54)., Conclusions: In this multicenter study combining EHR data with research biomarker analysis of patients with COVID-19, we identified novel profiles with divergent clinical outcomes and differential treatment responses., Competing Interests: Drs. Verhoef, Lopez-Espina, Reddy, and Sinha received support for article research from the National Institutes of Health (NIH). Drs. Spicer and Churpek disclosed grant funding from NIH/National Institute of General Medical Sciences, R01 GM123193; Department of Defense/Peer-reviewed medical research program, W81XWH-21-1-0009l; and NIH/National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), R01-DK126933-A1. Drs. Lopez-Espina’s and Reddy’s institutions received funding from the NIH. Drs. Lopez-Espina, Bhargava, Schmalz, and Reddy disclosed they are employees of Presnosis. Dr. Sims’s institution received funding from Prenosis. Dr. Churpek’s institution received funding from National Heart, Lung, and Blood Institute R01-HL157262-01, NIH/National Institute on Aging R21 AG068720-01, NIH/National Institute on Drug Abuse R01 DA051464-01, NIH/NIDDK R21DK113420-01A1, and he disclosed there is a patent pending (ARCD. P0535US.P2) for risk stratification algorithms for hospitalized patients. The remaining authors have disclosed that they do not have any potential conflicts of interest., (Copyright © 2023 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved.)

Rationale: A recent randomized trial found that using a bougie did not increase the incidence of successful intubation on first attempt in critically ill adults. The average effect of treatment in a trial population, however, may differ from effects for individuals. Objective: We hypothesized that application of a machine learning model to data from a clinical trial could estimate the effect of treatment (bougie vs. stylet) for individual patients based on their baseline characteristics ("individualized treatment effects"). Methods: This was a secondary analysis of the BOUGIE (Bougie or Stylet in Patients Undergoing Intubation Emergently) trial. A causal forest algorithm was used to model differences in outcome probabilities by randomized group assignment (bougie vs. stylet) for each patient in the first half of the trial (training cohort). This model was used to predict individualized treatment effects for each patient in the second half (validation cohort). Measurements and Main Results: Of 1,102 patients in the BOUGIE trial, 558 (50.6%) were the training cohort, and 544 (49.4%) were the validation cohort. In the validation cohort, individualized treatment effects predicted by the model significantly modified the effect of trial group assignment on the primary outcome ( P value for interaction = 0.02; adjusted qini coefficient, 2.46). The most important model variables were difficult airway characteristics, body mass index, and Acute Physiology and Chronic Health Evaluation II score. Conclusions: In this hypothesis-generating secondary analysis of a randomized trial with no average treatment effect and no treatment effect in any prespecified subgroups, a causal forest machine learning algorithm identified patients who appeared to benefit from the use of a bougie over a stylet and from the use of a stylet over a bougie using complex interactions between baseline patient and operator characteristics.

Low-protein (LP) diets are associated with a decreased risk of diabetes in humans, and promote leanness and glycaemic control in both rodents and humans. While the effects of an LP diet on glycaemic control are mediated by reduced levels of the branched-chain amino acids, we have observed that reducing dietary levels of the other six essential amino acids leads to changes in body composition. Here, we find that dietary histidine plays a key role in the response to an LP diet in male C57BL/6J mice. Specifically reducing dietary levels of histidine by 67% reduces the weight gain of young, lean male mice, reducing both adipose and lean mass without altering glucose metabolism, and rapidly reverses diet-induced obesity and hepatic steatosis in diet-induced obese male mice, increasing insulin sensitivity. This normalization of metabolic health was associated not with caloric restriction or increased activity, but with increased energy expenditure. Surprisingly, the effects of histidine restriction do not require the energy balance hormone Fgf21. Histidine restriction that was started in midlife promoted leanness and glucose tolerance in aged males but not females, but did not affect frailty or lifespan in either sex. Finally, we demonstrate that variation in dietary histidine levels helps to explain body mass index differences in humans. Overall, our findings demonstrate that dietary histidine is a key regulator of weight and body composition in male mice and in humans, and suggest that reducing dietary histidine may be a translatable option for the treatment of obesity. KEY POINTS: Protein restriction (PR) promotes metabolic health in rodents and humans and extends rodent lifespan. Restriction of specific individual essential amino acids can recapitulate the benefits of PR. Reduced histidine promotes leanness and increased energy expenditure in male mice. Reduced histidine does not extend the lifespan of mice when begun in midlife. Dietary levels of histidine are positively associated with body mass index in humans., (© 2022 The Authors. The Journal of Physiology published by John Wiley & Sons Ltd on behalf of The Physiological Society.)

Objectives: Critically ill patients with coronavirus disease 2019 have variable mortality. Risk scores could improve care and be used for prognostic enrichment in trials. We aimed to compare machine learning algorithms and develop a simple tool for predicting 28-day mortality in ICU patients with coronavirus disease 2019., Design: This was an observational study of adult patients with coronavirus disease 2019. The primary outcome was 28-day inhospital mortality. Machine learning models and a simple tool were derived using variables from the first 48 hours of ICU admission and validated externally in independent sites and temporally with more recent admissions. Models were compared with a modified Sequential Organ Failure Assessment score, National Early Warning Score, and CURB-65 using the area under the receiver operating characteristic curve and calibration., Setting: Sixty-eight U.S. ICUs., Patients: Adults with coronavirus disease 2019 admitted to 68 ICUs in the United States between March 4, 2020, and June 29, 2020., Interventions: None., Measurements and Main Results: The study included 5,075 patients, 1,846 (36.4%) of whom died by day 28. eXtreme Gradient Boosting had the highest area under the receiver operating characteristic curve in external validation (0.81) and was well-calibrated, while k-nearest neighbors were the lowest performing machine learning algorithm (area under the receiver operating characteristic curve 0.69). Findings were similar with temporal validation. The simple tool, which was created using the most important features from the eXtreme Gradient Boosting model, had a significantly higher area under the receiver operating characteristic curve in external validation (0.78) than the Sequential Organ Failure Assessment score (0.69), National Early Warning Score (0.60), and CURB-65 (0.65; p < 0.05 for all comparisons). Age, number of ICU beds, creatinine, lactate, arterial pH, and Pao 2 /Fio 2 ratio were the most important predictors in the eXtreme Gradient Boosting model., Conclusions: eXtreme Gradient Boosting had the highest discrimination overall, and our simple tool had higher discrimination than a modified Sequential Organ Failure Assessment score, National Early Warning Score, and CURB-65 on external validation. These models could be used to improve triage decisions and clinical trial enrichment., Competing Interests: Dr. Churpek is supported by an R01 from National Institute of General Medical Sciences (NIGMS) (R01 GM123193), has a patent pending (ARCD. P0535US.P2) for risk stratification algorithms for hospitalized patients, and has received research support from EarlySense (Tel Aviv, Israel). Dr. Gupta is a scientific coordinator for the A Study of Cardiovascular Events in Diabetes trial (GlaxoSmithKline). Dr. Shaefi is supported by a K08 from NIGMS (K08GM134220) and an R03 from National Institute of Aging (R03AG060179). Dr. Leaf is supported by an R01 from National Heart, Lung, and Blood Institute (R01HL144566). The remaining authors have disclosed that they do not have any potential conflicts of interest., (Copyright © 2021 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of the Society of Critical Care Medicine.)

Rationale: Variation in hospital mortality has been described for coronavirus disease 2019 (COVID-19), but the factors that explain these differences remain unclear., Objective: Our objective was to utilize a large, nationally representative dataset of critically ill adults with COVID-19 to determine which factors explain mortality variability., Methods: In this multicenter cohort study, we examined adults hospitalized in intensive care units with COVID-19 at 70 United States hospitals between March and June 2020. The primary outcome was 28-day mortality. We examined patient-level and hospital-level variables. Mixed-effects logistic regression was used to identify factors associated with interhospital variation. The median odds ratio (OR) was calculated to compare outcomes in higher- vs. lower-mortality hospitals. A gradient boosted machine algorithm was developed for individual-level mortality models., Measurements and Main Results: A total of 4,019 patients were included, 1537 (38%) of whom died by 28 days. Mortality varied considerably across hospitals (0-82%). After adjustment for patient- and hospital-level domains, interhospital variation was attenuated (OR decline from 2.06 [95% CI, 1.73-2.37] to 1.22 [95% CI, 1.00-1.38]), with the greatest changes occurring with adjustment for acute physiology, socioeconomic status, and strain. For individual patients, the relative contribution of each domain to mortality risk was: acute physiology (49%), demographics and comorbidities (20%), socioeconomic status (12%), strain (9%), hospital quality (8%), and treatments (3%)., Conclusion: There is considerable interhospital variation in mortality for critically ill patients with COVID-19, which is mostly explained by hospital-level socioeconomic status, strain, and acute physiologic differences. Individual mortality is driven mostly by patient-level factors. This article is open access and distributed under the terms of the Creative Commons Attribution Non-Commercial No Derivatives License 4.0 (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Low-protein diets promote metabolic health in rodents and humans, and the benefits of low-protein diets are recapitulated by specifically reducing dietary levels of the three branched-chain amino acids (BCAAs), leucine, isoleucine, and valine. Here, we demonstrate that each BCAA has distinct metabolic effects. A low isoleucine diet reprograms liver and adipose metabolism, increasing hepatic insulin sensitivity and ketogenesis and increasing energy expenditure, activating the FGF21-UCP1 axis. Reducing valine induces similar but more modest metabolic effects, whereas these effects are absent with low leucine. Reducing isoleucine or valine rapidly restores metabolic health to diet-induced obese mice. Finally, we demonstrate that variation in dietary isoleucine levels helps explain body mass index differences in humans. Our results reveal isoleucine as a key regulator of metabolic health and the adverse metabolic response to dietary BCAAs and suggest reducing dietary isoleucine as a new approach to treating and preventing obesity and diabetes., Competing Interests: Declaration of interests D.W.L. has received funding from and is a scientific advisory board member of Aeovian Pharmaceuticals, which seeks to develop novel, selective mTOR inhibitors for the treatment of various diseases. UW-Madison has applied for a patent based in part on the findings reported here, for which N.E.R. and D.W.L. are inventors., (Published by Elsevier Inc.)

Previous demonstrations of the irreversibility of dihydrostreptomycin transport across the cytoplasmic membrane of Escherichia coli were not due to decreases in the magnitude of the cytoplasmic membrane potential (delta psi). Irreversibility was probably not due to ATP hydrolysis being coupled to transport, because the rate of energy-dependent dihydrostreptomycin uptake was unaffected by 10-fold reduction in the cellular ATP level.