Association between peripheral perfusion index and postoperative acute kidney injury in major noncardiac surgery patients receiving continuous vasopressors: a post hoc exploratory analysis of the VEGA-1 trial.

BACKGROUND: The peripheral perfusion index is the ratio of pulsatile to nonpulsatile static blood flow obtained by photoplethysmography and reflects peripheral tissue perfusion. We investigated the association between intraoperative perfusion index and postoperative acute kidney injury in patients undergoing major noncardiac surgery and receiving continuous vasopressor infusions. METHODS: In this exploratory post hoc analysis of a pragmatic, cluster-randomised, multicentre trial, we obtained areas and cumulative times under various thresholds of perfusion index and investigated their association with acute kidney injury in multivariable logistic regression analyses. In secondary analyses, we investigated the association of time-weighted average perfusion index with acute kidney injury. The 30-day mortality was a secondary outcome. RESULTS: Of 2534 cases included, 8.9% developed postoperative acute kidney injury. Areas and cumulative times under a perfusion index of 3% and 2% were associated with an increased risk of acute kidney injury; the strongest association was observed for area under a perfusion index of 1% (adjusted odds ratio [aOR] 1.32, 95% confidence interval [CI] 1.00-1.74, P=0.050, per 100%∗min increase). Additionally, time-weighted average perfusion index was associated with acute kidney injury (aOR 0.82, 95% CI 0.74-0.91, P<0.001) and 30-day mortality (aOR 0.68, 95% CI 0.49-0.95, P=0.024). CONCLUSIONS: Larger areas and longer cumulative times under thresholds of perfusion index and lower time-weighted average perfusion index were associated with postoperative acute kidney injury in patients undergoing major noncardiac surgery and receiving continuous vasopressor infusions. CLINICAL TRIAL REGISTRATION: NCT04789330.

Low-Dose Corticosteroids for Critically Ill Adults With Severe Pulmonary Infections: A Review.

Importance: Severe pulmonary infections, including COVID-19, community-acquired pneumonia, influenza, and Pneumocystis pneumonia, are a leading cause of death among adults worldwide. Pulmonary infections in critically ill patients may cause septic shock, acute respiratory distress syndrome, or both, which are associated with mortality rates ranging between 30% and 50%. Observations: Corticosteroids mitigate the immune response to infection and improve outcomes for patients with several types of severe pulmonary infections. Low-dose corticosteroids, defined as less than or equal to 400 mg hydrocortisone equivalent daily, can reduce mortality of patients with severe COVID-19, community-acquired pneumonia, and Pneumocystis pneumonia. A randomized clinical trial of 6425 patients hospitalized with COVID-19 who required supplemental oxygen or noninvasive or invasive mechanical ventilation reported that dexamethasone 6 mg daily for 10 days decreased 28-day mortality (23% vs 26%). A meta-analysis that included 7 randomized clinical trials of 1689 patients treated in the intensive care unit for severe bacterial community-acquired pneumonia reported that hydrocortisone equivalent less than or equal to 400 mg daily for 8 days or fewer was associated with lower 30-day mortality compared with placebo (10% vs 16%). In a meta-analysis of 6 randomized clinical trials, low-dose corticosteroids were associated with lower mortality rates compared with placebo for patients with HIV and moderate to severe Pneumocystis pneumonia (13% vs 25%). In a predefined subgroup analysis of a trial of low-dose steroid treatment for septic shock, patients with community-acquired pneumonia randomized to 7 days of intravenous hydrocortisone 50 mg every 6 hours and fludrocortisone 50 µg daily had decreased mortality compared with the placebo group (39% vs 51%). For patients with acute respiratory distress syndrome caused by various conditions, low-dose corticosteroids were associated with decreased in-hospital mortality (34% vs 45%) according to a meta-analysis of 8 studies that included 1091 patients. Adverse effects of low-dose corticosteroids may include hyperglycemia, gastrointestinal bleeding, neuropsychiatric disorders, muscle weakness, hypernatremia, and secondary infections. Conclusions and Relevance: Treatment with low-dose corticosteroids is associated with decreased mortality for patients with severe COVID-19 infection, severe community-acquired bacterial pneumonia, and moderate to severe Pneumocystis pneumonia (for patients with HIV). Low-dose corticosteroids may also benefit critically ill patients with respiratory infections who have septic shock, acute respiratory distress syndrome, or both.

IntraCranial pressure prediction AlgoRithm using machinE learning (I-CARE): Training and Validation Study.

OBJECTIVES: Elevated intracranial pressure (ICP) is a potentially devastating complication of neurologic injury. Developing an ICP prediction algorithm to help the clinician adjust treatments and potentially prevent elevated ICP episodes. DESIGN: Retrospective study. SETTING: Three hundred thirty-five ICUs at 208 hospitals in the United States. SUBJECTS: Adults patients from the electronic ICU (eICU) Collaborative Research Database was used to train an ensemble machine learning model to predict the ICP 30 minutes in the future. Predictive performance was evaluated using a left-out test dataset and externally evaluated on the Medical Information Mart for Intensive Care-III (MIMIC-III) Matched Waveform Database. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Predictors included age, assigned sex, laboratories, medications and infusions, input/output, Glasgow Coma Scale (GCS) components, and time-series vitals (heart rate, ICP, mean arterial pressure, respiratory rate, and temperature). Each patient ICU stay was divided into successive 95-minute timeblocks. For each timeblock, the model was trained on nontime-varying covariates as well as on 12 observations of time-varying covariates at 5-minute intervals and asked to predict the 5-minute median ICP 30 minutes after the last observed ICP value. Data from 931 patients with ICP monitoring in the eICU dataset were extracted (46,207 timeblocks). The root mean squared error was 4.51 mm Hg in the eICU test set and 3.56 mm Hg in the MIMIC-III dataset. The most important variables driving ICP prediction were previous ICP history, patients' temperature, weight, serum creatinine, age, GCS, and hemodynamic parameters. CONCLUSIONS: IntraCranial pressure prediction AlgoRithm using machinE learning, an ensemble machine learning model, trained to predict the ICP of a patient 30 minutes in the future based on baseline characteristics and vitals data from the past hour showed promising predictive performance including in an external validation dataset.

A deep learning approach for generating intracranial pressure waveforms from extracranial signals routinely measured in the intensive care unit.

Intracranial pressure (ICP) is commonly monitored to guide treatment in patients with serious brain disorders such as traumatic brain injury and stroke. Established methods to assess ICP are resource intensive and highly invasive. We hypothesized that ICP waveforms can be computed noninvasively from three extracranial physiological waveforms routinely acquired in the Intensive Care Unit (ICU): arterial blood pressure (ABP), photoplethysmography (PPG), and electrocardiography (ECG). We evaluated over 600 h of high-frequency (125 Hz) simultaneously acquired ICP, ABP, ECG, and PPG waveform data in 10 patients admitted to the ICU with critical brain disorders. The data were segmented in non-overlapping 10-s windows, and ABP, ECG, and PPG waveforms were used to train deep learning (DL) models to re-create concurrent ICP. The predictive performance of six different DL models was evaluated in single- and multi-patient iterations. The mean average error (MAE) ± SD of the best-performing models was 1.34 ± 0.59 mmHg in the single-patient and 5.10 ± 0.11 mmHg in the multi-patient analysis. Ablation analysis was conducted to compare contributions from single physiologic sources and demonstrated statistically indistinguishable performances across the top DL models for each waveform (MAE±SD 6.33 ± 0.73, 6.65 ± 0.96, and 7.30 ± 1.28 mmHg, respectively, for ECG, PPG, and ABP; p = 0.42). Results support the preliminary feasibility and accuracy of DL-enabled continuous noninvasive ICP waveform computation using extracranial physiological waveforms. With refinement and further validation, this method could represent a safer and more accessible alternative to invasive ICP, enabling assessment and treatment in low-resource settings.

Predicting Long COVID in the National COVID Cohort Collaborative Using Super Learner: Cohort Study.

BACKGROUND: Postacute sequelae of COVID-19 (PASC), also known as long COVID, is a broad grouping of a range of long-term symptoms following acute COVID-19. These symptoms can occur across a range of biological systems, leading to challenges in determining risk factors for PASC and the causal etiology of this disorder. An understanding of characteristics that are predictive of future PASC is valuable, as this can inform the identification of high-risk individuals and future preventative efforts. However, current knowledge regarding PASC risk factors is limited. OBJECTIVE: Using a sample of 55,257 patients (at a ratio of 1 patient with PASC to 4 matched controls) from the National COVID Cohort Collaborative, as part of the National Institutes of Health Long COVID Computational Challenge, we sought to predict individual risk of PASC diagnosis from a curated set of clinically informed covariates. The National COVID Cohort Collaborative includes electronic health records for more than 22 million patients from 84 sites across the United States. METHODS: We predicted individual PASC status, given covariate information, using Super Learner (an ensemble machine learning algorithm also known as stacking) to learn the optimal combination of gradient boosting and random forest algorithms to maximize the area under the receiver operator curve. We evaluated variable importance (Shapley values) based on 3 levels: individual features, temporal windows, and clinical domains. We externally validated these findings using a holdout set of randomly selected study sites. RESULTS: We were able to predict individual PASC diagnoses accurately (area under the curve 0.874). The individual features of the length of observation period, number of health care interactions during acute COVID-19, and viral lower respiratory infection were the most predictive of subsequent PASC diagnosis. Temporally, we found that baseline characteristics were the most predictive of future PASC diagnosis, compared with characteristics immediately before, during, or after acute COVID-19. We found that the clinical domains of health care use, demographics or anthropometry, and respiratory factors were the most predictive of PASC diagnosis. CONCLUSIONS: The methods outlined here provide an open-source, applied example of using Super Learner to predict PASC status using electronic health record data, which can be replicated across a variety of settings. Across individual predictors and clinical domains, we consistently found that factors related to health care use were the strongest predictors of PASC diagnosis. This indicates that any observational studies using PASC diagnosis as a primary outcome must rigorously account for heterogeneous health care use. Our temporal findings support the hypothesis that clinicians may be able to accurately assess the risk of PASC in patients before acute COVID-19 diagnosis, which could improve early interventions and preventive care. Our findings also highlight the importance of respiratory characteristics in PASC risk assessment. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1101/2023.07.27.23293272.

Patient-Level Meta-Analysis of Low-Dose Hydrocortisone in Adults with Septic Shock.

BACKGROUND: Trials and study-level meta-analyses have failed to resolve the role of corticosteroids in the management of patients with septic shock. Patient-level meta-analyses may provide more precise estimates of treatment effects, particularly subgroup effects. METHODS: We pooled individual patient data from septic shock trials investigating the adjunctive use of intravenous hydrocortisone. The primary outcome was 90-day all-cause mortality, and it was also analyzed across predefined subgroups. Secondary outcomes included mortality at intensive care unit and hospital discharge, at 28 and 180 days, and vasopressor-, ventilator-, and organ failure­free days. Adverse events included superinfection, muscle weakness, hyperglycemia, hypernatremia, and gastroduodenal bleeding. RESULTS: Of 24 eligible trials (n=8528), 17 (n=7882) provided individual patient data, and 7 (n=5929) provided 90-day mortality. The marginal relative risk (RR) for 90-day mortality of hydrocortisone versus placebo was 0.93 (95% confidence interval [CI], 0.82 to 1.04; P=0.22; moderate certainty). It was 0.86 (95% CI, 0.79 to 0.92) for hydrocortisone with fludrocortisone and 0.96 (95% CI, 0.82 to 1.12) without fludrocortisone. There was no significant differential treatment effect across subgroups. Hydrocortisone was associated with little to no difference in any of the secondary outcomes except vasopressor-free days (mean difference, 1.24 days; 95% CI, 0.74 to 1.73; high certainty). Hydrocortisone may not be associated with an increase in the risk of superinfection (RR, 1.04; 95% CI, 0.95 to 1.15; low certainty), hyperglycemia (RR, 1.05; 95% CI, 0.98 to 1.12; low certainty), or gastroduodenal bleeding (RR, 1.11; 95% CI, 0.83 to 1.48; low certainty). Hydrocortisone may be associated with an increase in the risk of hypernatremia (RR, 2.01; 95% CI, 1.56 to 2.60; low certainty) and muscle weakness (n=2647; RR, 1.73; 95% CI, 1.49 to 1.99; low certainty). CONCLUSIONS: In this patient-level meta-analysis, hydrocortisone compared with placebo was not associated with reduced mortality for patients with septic shock. (Funded by "Programme d'Investissements d'Avenir," a research Professorship from the National Institute of Health and Care Research, Leadership Fellowships from the National Health and Medical Research Council of Australia, and Emerging Leaders Fellowship from the National Health and Medical Research Council of Australia; PROSPERO registration number, CRD42017062198.)