Prognostic Enrichment Research Articles

Systematic analysis of prognostic significance, functional enrichment and immune implication of STK10 in acute myeloid leukemia

BackgroundDespite deeper understanding of the genetic landscape of acute myeloid leukemia (AML), the improvement of survival is still a great challenge. STK10 is overexpressed in several cancers with functions varying according to cancer types. But the functions of STK10 in AML has never been reported.MethodsWe analyzed the expression, prognosis and potential functions of STK10 utilizing public web servers. Metascape and the String database were used for functional and protein–protein interaction analyses.ResultsWe found STK10 was enriched in blood & immune cells and overexpressed in AML. High STK10 expression was associated with poor overall survival, which was also identified in the subgroups of patients ≤ 60 years old and patients with non-high-risk cytogenetics. We demonstrated genes associated with STK10 were enriched in blood, spleen and bone marrow, influencing the immune function and biological process of AML. ITGB2 and ITGAM might directly interact with STK10 and were associated with poor prognosis. Besides, STK10 was associated with the infiltration of immune cells and immune checkpoints, like HLA-E, CD274 and GAL-9.ConclusionsThe present study was the original description of STK10 in AML and set the stage for developing STK10 as a new prognostic marker or therapeutic target for AML.

Identifying Long-Term Morbidities and Health Trajectories After Prolonged Mechanical Ventilation in Children Using State All Payer Claims Data.

To identify postdischarge outcome phenotypes and risk factors for poor outcomes using insurance claims data. Retrospective cohort study. Single quaternary center. Children without preexisting tracheostomy who required greater than or equal to 3 days of invasive mechanical ventilation, survived the hospitalization, and had postdischarge insurance eligibility in Colorado's All Payer Claims Database. None. We used unsupervised machine learning to identify functional outcome phenotypes based on claims data representative of postdischarge morbidities. We assessed health trajectory by comparing change in the number of insurance claims between quarters 1 and 4 of the postdischarge year. Regression analyses identified variables associated with unfavorable outcomes. The 381 subjects had median age 3.3 years (interquartile range, 0.9-12 yr), and 147 (39%) had a complex chronic condition. Primary diagnoses were respiratory (41%), injury (23%), and neurologic (11%). We identified three phenotypes: lower morbidity (n = 300), higher morbidity (n = 62), and 1-year nonsurvivors (n = 19). Complex chronic conditions most strongly predicted the nonsurvivor phenotype. Longer PICU stays and tracheostomy placement most strongly predicted the higher morbidity phenotype. Patients with high but improving postdischarge resource use were differentiated by high illness severity and long PICU stays. Patients with persistently high or increasing resource use were differentiated by complex chronic conditions and tracheostomy placement. New morbidities are common after prolonged mechanical ventilation. Identifying phenotypes at high risk of postdischarge morbidity may facilitate prognostic enrichment in clinical trials.

Assessment of Negative Symptoms in Clinical Trials of Acute Schizophrenia: Test of a Novel Enrichment Strategy.

Drug trials for negative symptoms in schizophrenia select patients based on the severity and stability of negative symptoms, using criteria that are not suitable for trials of acute exacerbation of schizophrenia. Here we present a method to prognostically enrich subjects having a predefined factor structure in PANSS and apply it to the measurement of negative symptoms specifically in trials of acute schizophrenia. A vector of 1335 elements based on between- and within-item variances, covariances, and differences of PANSS items was created to calculate an index of heterogeneity and to enrich for a predetermined symptom construct in PANSS. Using prerandomization PANSS scores across N = 4876 subjects in 13 trials of acute schizophrenia, we demonstrate an ability to select for a subpopulation having the greatest amount of variance explained across the 7-items of the Marder PANSS negative symptom (MPNS) construct. Network analyses on subjects enriched for MPNS construct confirm that negative symptoms were most influential in overall psychopathology, distinct from subjects without the MPNS construct. As expected for D2 antagonists, drug-placebo differences on negative symptoms with lurasidone were not specific to the subpopulation having the MPNS construct. In contrast, the novel TAAR1 agonist ulotaront demonstrated specific improvements in negative symptoms which were greatest in the MPNS subpopulation. These results demonstrate the utility of a novel prognostic enrichment strategy that can address heterogeneity in clinical trials, where patients can be selected on the basis of a greater likelihood of having the measured symptom construct (negative symptoms) related to the disorder (schizophrenia). ClinicalTrials.gov Identifiers: NCT0296938, NCT00088634, NCT00549718, NCT00615433, NCT00790192.

PREDICTION OF DYSPNOEA FOLLOWING LUNG RESECTION SURGERY: POST-HOC ANALYSIS OF ‘PROFILES’ STUDY

Introduction Lung cancer is the leading cause of cancer death in Europe. Surgical resection is often the preferred treatment but is associated with morbidity and mortality. Survival with a meaningful quality of life is important; however, the prediction of post-operative dyspnoea (POD) is often difficult and innaccurate.1 The European Society of Thoracic Surgeons (ESTS) and the (UK) National Institute of Clinical Excellence (NICE) advocate studies concerning operative risk for surgical resection. Conventional prediction uses pulmonary function; predicted post-operative FEV1%(ppoFEV1%) and predicted post-operative DLCO%(ppoDLCO%) with Methods With informed consent and ethics approval, we prospectively recruited 250 patients undergoing lung resection in four UK centres. Dyspnoea was measured pre-operatively and 3 months post-operatively using the Medical Research Council (MRC) score. The primary outcome was patients with a post-operative MRC>2, excluding those with an MRC>2 pre-operatively. Two conventional models were derived (n=93, 1 site), before external validation (n=85, 3 sites) using the variables age, gender and ppoFEV1%/ppoDLCO%. Model 1(M1) incorporates ppoFEV1%/ ppoDLCO% with conventional cut offs and Model 2(M2) treats them continuously. Using similar internal derivation and external validation, two new models were explored. Univariate analysis identified risk predictors (p Results New models improved prediction within the internal dataset: M2 Vs M4 (AUROC comparison, p=0.03, NRI 0.26). (Fig.1) The best conventional and new models (M2 & M4) performed similarly within the external population: Sensitivity (55% vs 50%), Specificity (68% Vs 73%), PPV (38% Vs 39%), NPV (81% Vs 81%), respectively. Discussion This study demonstrates conventional risk prediction for POD using pulmonary function is poor. It also highlights challenges in creating new scoring tools: at external validation conventional models performed equally to new models with similar sensitivity/specificity/NPV and PPV. Using ppoFEV1%/ ppoDLCO% as continuous variables rather than dichotomised at 40%, may increase predictive strength. Future work should explore new variables to predict POD, such as pre-operative quality of life and biomarkers. For prognostic enrichment, models should have high sensitivity & high NPV, targeting those who would benefit most from low-risk interventions

Machine Learning Prediction of Death in Critically Ill Patients With Coronavirus Disease 2019.

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. 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. Sixty-eight U.S. ICUs. Adults with coronavirus disease 2019 admitted to 68 ICUs in the United States between March 4, 2020, and June 29, 2020. None. 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 Pao2/Fio2 ratio were the most important predictors in the eXtreme Gradient Boosting model. 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.

Biomarker-Based Classification of Patients With Acute Respiratory Failure Into Inflammatory Subphenotypes: A Single-Center Exploratory Study.

Hyper- and hypoinflammatory subphenotypes discovered in patients with acute respiratory distress syndrome predict clinical outcomes and therapeutic responses. These subphenotypes may be important in broader critically ill patient populations with acute respiratory failure regardless of clinical diagnosis. We investigated subphenotyping with latent class analysis in an inclusive population of acute respiratory failure, derived a parsimonious model for subphenotypic predictions based on a small set of variables, and examined associations with clinical outcomes. Prospective, observational cohort study. Single-center, academic medical ICU. Mechanically ventilated patients with acute respiratory failure. We included 498 patients with acute respiratory failure (acute respiratory distress syndrome: 143, at-risk for acute respiratory distress syndrome: 198, congestive heart failure: 37, acute on chronic respiratory failure: 23, airway protection: 61, and multifactorial: 35) in our derivation cohort and measured 10 baseline plasma biomarkers. Latent class analysis considering clinical variables and biomarkers determined that a two-class model offered optimal fit (23% hyperinflammatory subphenotype). Distribution of hyperinflammatory subphenotype varied among acute respiratory failure etiologies (acute respiratory distress syndrome: 31%, at-risk for acute respiratory distress syndrome: 27%, congestive heart failure: 22%, acute on chronic respiratory failure 0%, airway protection: 5%, and multifactorial: 14%). Hyperinflammatory patients had higher Sequential Organ Failure Assessment scores, fewer ventilator-free days, and higher 30- and 90-day mortality (all p < 0.001). We derived a parsimonious model consisting of angiopoietin-2, soluble tumor necrosis factor receptor-1, procalcitonin, and bicarbonate and classified subphenotypes in a validation cohort (n = 139). Hyperinflammatory patients (19%) demonstrated higher levels of inflammatory biomarkers not included in the model (p < 0.01) and worse outcomes. Host-response subphenotypes are observable in a heterogeneous population with acute respiratory failure and predict clinical outcomes. Simple, biomarker-based models can offer prognostic enrichment in patients with acute respiratory failure. The differential distribution of subphenotypes by specific etiologies of acute respiratory failure indicates that subphenotyping may be more relevant in patients with hypoxemic causes of acute respiratory failure and not in patients intubated for airway protection or acute on chronic decompensation.

Thrombomodulin is associated with increased mortality and organ failure in mechanically ventilated children with acute respiratory failure: biomarker analysis from a multicenter randomized controlled trial

BackgroundAcute respiratory failure (ARF) can progress to acute respiratory distress syndrome and death. Biomarkers may allow for risk stratification and prognostic enrichment in ARF. Thrombomodulin (TM) is a transmembrane antithrombotic mediator expressed in endothelial cells. It is cleaved into its soluble form (sTM) during inflammation and vascular injury. Levels of sTM correlate with inflammation and end organ dysfunction.MethodsThis was a prospective observational study of 432 patients aged 2 weeks—17 years requiring invasive mechanical ventilation. It was ancillary to the multicenter clinical trial, Randomized Evaluation of Sedation Titration for Respiratory Failure (RESTORE). After consent, patients had up to 3 plasma samples collected at 24-h intervals within 5 days after intubation. sTM was assayed by ELISA. The Hazard ratio (HR) for 90-day mortality was determined by Cox regression. Mixed effect models (MEM) were used to test for association with extrapulmonary multiorgan failure (MOF) and oxygenation index (OI). Age, race, sex and PRISM-III scores were used as confounding variables for multivariable analyses.ResultssTM values ranged from 16.6 to 670.9 ng/ml within 5 days after intubation. Higher sTM was associated with increased 90-day mortality (n = 432, adjusted HR = 1.003, p = 0.02) and worse OI in the first 5 days after intubation (n = 252, Estimate = 0.02, p < 0.01). Both initial and slope of sTM were associated with increased extrapulmonary MOF in unadjusted and adjusted analyses (Intercept, Estimate = 0.003, p < 0.0001; and slope, Estimate = 0.01, p = 0.0009, n = 386).ConclusionsPlasma sTM is associated with mortality, severity of hypoxic respiratory failure and worsening extrapulmonary MOF in children with ARF. This suggests a role of vascular injury in the pathogenesis of ARF and provides potential applicability towards targeted therapies.Trial registration: https://clinicaltrials.gov/ct2/show/NCT00814099.In healthy lung endothelium, thrombomodulin (TM) recruits thrombin to activate Protein-C (PC/APC), that inhibits plasminogen activator-1 (PAI-1) and thrombosis. In inflamed and damaged endothelium, TM is cleaved into its soluble form (sTM), precluding its usual regulation of thrombosis. In this study, we measured plasma sTM levels in pediatric patients with respiratory failure and found that sTM correlated with mortality and other clinical markers of poor outcomes.

Diuretic therapy as prognostic enrichment factor for clinical trials in patients with heart failure with reduced ejection fraction.

Loop diuretics are the mainstay of congestion treatment in patients with heart failure (HF). We assessed the association between baseline loop diuretic use and outcome. We also compared the increment in risk related to diuretic dose with conventional prognostic enrichment criteria used in the EMPHASIS-HF trial, which included patients with systolic HF and mild symptoms, such as prior hospitalization and elevated natriuretic peptides. Individual analyses were performed according to baseline loop diuretic usage (furosemide-equivalent dose > 40mg, 1-40mg, and no furosemide), and according to enrichment criteria adopted in the trial [i.e. recent hospitalization (< 30days or 30 to 180days prior to randomization) due to HF or other cardiovascular cause, or elevated natriuretic peptides]. The primary endpoint was a composite of cardiovascular death or HF hospitalization. Loop diuretic usage at baseline (HR for > 40mg furosemide-equivalent dose = 3.16, 95% CI 2.43-4.11; HR for 1-40mg = 2.06, 95% CI 1.60-2.65) was significantly associated with a higher risk for the primary endpoint in a stepwise manner when compared to no baseline loop diuretic usage. In contrast, the differences in outcome rates were more modest when using history of hospitalization and/or BNP: all HR ranged from 1 (reference, non-HF related CV hospitalization > 30days) to 2.04 (HF hospitalization < 30days). The effect of eplerenone on the primary endpoint was consistent across subgroups in both analyses (P for interaction ≥ 0.2 for all). Loop diuretic usage (especially at doses > 40mg) identified patients at higher risk than history of HF hospitalization and/or high BNP blood concentrations.

Novel Claims-Based Outcome Phenotypes in Survivors of Pediatric Traumatic Brain Injury.

For children hospitalized with acute traumatic brain injury (TBI), to use postdischarge insurance claims to identify: (1) healthcare utilization patterns representative of functional outcome phenotypes and (2) patient and hospitalization characteristics that predict outcome phenotype. Two pediatric trauma centers and a state-level insurance claim aggregator. A total of 289 children, who survived a hospitalization after TBI between 2009 and 2014, were in the hospital trauma registry, and had postdischarge insurance eligibility. Retrospective cohort study. Unsupervised machine learning to identify phenotypes based on postdischarge insurance claims. Regression analyses to identify predictors of phenotype. Median age 5 years (interquartile range 2-12), 29% (84/289) female. TBI severity: 30% severe, 14% moderate, and 60% mild. We identified 4 functional outcome phenotypes. Phenotypes 3 and 4 were the highest utilizers of resources. Morbidity burden was highest during the first 4 postdischarge months and subsequently decreased in all domains except respiratory. Severity and mechanism of injury, intracranial pressure monitor placement, seizures, and hospital and intensive care unit lengths of stay were phenotype predictors. Unsupervised machine learning identified postdischarge phenotypes at high risk for morbidities. Most phenotype predictors are available early in the hospitalization and can be used for prognostic enrichment of clinical trials targeting mitigation or treatment of domain-specific morbidities.

Clinical phenotypes of acute kidney injury are associated with unique outcomes in critically ill septic children.

BackgroundAssessment of acute kidney injury (AKI) in septic patients remains imprecise. In adults, the classification of septic patients by clinical AKI phenotypes (severity and timing) demonstrates unique associations with patient outcome vs. broadly defined AKI.MethodsIn a multinational prospective observational study, AKI diagnosis in critically ill septic children was stratified by duration (transient vs. persistent) and severity (mild vs. severe by creatinine change and urine output). The outcomes of interest were mortality and intensive care unit resource complexity at 28 days.ResultsSeven hundred and fifty-seven septic children were studied (male 52.7%, age 4.6 years (1.5–11.9)). Mortality (overall 12.1%) was different between severe AKI and mild AKI (18.3 vs. 4.4%, p < 0.001) as well as intensive care unit (ICU) complexity (overall 34.5%, 45 vs. 21.7%, p < 0.001). Patients with Persistent AKI had fewer ICU-free days (17 (7, 21) vs. 24 (17, 26), p < 0.001) and higher ICU complexity (52.8 vs. 22.9%, p = 0.002) than transient AKI, even after exclusion of patients with early mortality. AKI phenotypes incorporating temporal and severity data correlate with unique survival (range 4.4–21.6%) and ICU-free days (range of 15–25 days)ConclusionsThe outcome of septic children with AKI changes by clinical phenotype. Our findings underscore the importance of prognostic enrichment in sepsis and AKI for the purpose of trial design and patient management.Impact Although AKI occurs commonly in patients with sepsis (S-AKI), outcomes for children with S-AKI varies based on the severity and timing of the AKI.Existing S-AKI pediatric data utilize a broad singular definition of kidney injury. Increasing the precision of AKI classification results in a new understanding of how S-AKI associates with patient outcome.A refined classification of S-AKI identifies subgroups of children, making possible a targeted and a personalized medicine approach to S-AKI study and management.

Severity of illness scores at presentation predict ICU admission and mortality in COVID-19.

Background:The COVID-19 pandemic has overwhelmed hospital systems in multiple countries and necessitated caring for patients in atypical healthcare settings. The goal of this study was to ascertain if the conventional critical care severity scores qSOFA, SOFA, APACHE-II, and SAPS-II could predict which patients admitted to the hospital from an emergency department would eventually require intensive care.Methods:This single-center, retrospective cohort study enrolled patients admitted to Vanderbilt University Hospital from the emergency room with symptomatic, confirmed COVID-19 infection between March 8, 2020 through May 15, 2020. Clinical phenotyping was performed by chart abstraction, and the correlation of the qSOFA, SOFA, APACHE-II, and SAPS-II scores for the primary endpoint of ICU admission and secondary endpoint of in-hospital mortality was evaluated.Results:During the study period, 128 patients were admitted to Vanderbilt University Hospital from the emergency room with COVID-19. Of these, 39 patients eventually required intensive care; the remaining 89 were discharged from the medical ward. All severity of illness scores demonstrated at least moderate ability to identify patients who would die or require ICU admission. Of the three severity of illness scores assessed, the APACHE-II score performed best with an AUC of 0.851 (95% CI: 0.786 to 0.917) for identifying patient that would require ICU admission. No patient with an APACHE-II score at the time of presentation less than 8 or qSOFA of 0 required intensive care unit (ICU) admission. All patients with an APACHE-II score less than 10 or qSOFA score of 0 survived to hospital discharge.Conclusions:The APACHE-II score accurately predicts the eventual need for ICU admission. This may allow for risk-stratification of patients safe to treat in alternative health care settings and prognostic enrichment to accelerate clinical trials of COVID-19 therapies.

Prognostic Biomarkers for Thrombotic Microangiopathy after Acute Graft-versus-Host Disease: A Nested Case-Control Study

Transplantation-associated thrombotic microangiopathy (TA-TMA) is a complication of allogeneic hematopoietic cell transplantation (HCT) that often occurs following the development of acute graft-versus-host disease (aGVHD). In this study, we aimed to identify early TMA biomarkers among patients with aGVHD. We performed a nested-case-control study from a prospective cohort of allogeneic HCT recipients, matching on the timing and severity of antecedent aGVHD. We identified 13 TMA cases and 25 non-TMA controls from 208 patients in the cohort. Using multivariable conditional logistic regression, the odds ratio for TMA compared with non-TMA was 2.65 (95% confidence interval [CI], 1.00 to 7.04) for every 100 ng/mL increase in terminal complement complex sC5b9 and 2.62 (95% CI, 1.56 to 4.38) for every 1000 pg/mL increase in angiopoietin-2 (ANG2) at the onset of aGVHD. ADAMTS13 and von Willebrand factor (VWF) antigens were not appreciably associated with TMA. Using a Cox regression model incorporating sC5b9 >300 ng/mL and ANG2 >3000 pg/mL at the onset of aGVHD, the adjusted hazard ratio for mortality was 5.33 (95% CI, 1.57 to 18.03) for the high-risk group (both elevated) and 4.40 (95% CI, 1.60 to 12.07) for the intermediate-risk group (one elevated) compared with the low-risk group (neither elevated). In conclusion, we found that elevated sC5b9 and ANG2 levels at the onset of aGVHD were associated with the development of TMA and possibly mortality after accounting for the timing and severity of aGVHD. The results suggest important roles of complement activation and endothelial dysfunction in the pathogenesis of TMA. Measurement of these biomarkers at the onset of aGVHD may inform prognostic enrichment for preventive trials and improve clinical care.

Validating Measures of Disease Severity in Acute Respiratory Distress Syndrome.

Rationale: Quantifying acute respiratory disease syndrome (ARDS) severity is essential for prognostic enrichment to stratify patients for invasive or higher-risk treatments; however, the comparative performance of many ARDS severity measures is unknown.Objectives: To validate ARDS severity measures for their ability to predict hospital mortality and an ARDS-specific outcome (defined as death from pulmonary dysfunction or the need for extracorporeal membrane oxygenation [ECMO] therapy).Methods: We compared five individual ARDS severity measures including the ratio of arterial oxygen tension/pressure to fraction of inspired oxygen (PaO2/FiO2 ratio), oxygenation index, ventilatory ratio, lung compliance, and radiologic assessment of lung edema (RALE); two ARDS composite severity scores including the Murray Lung Injury Score, and a novel score combining RALE, PaO2/FiO2 ratio, and ventilatory ratio; and the Acute Physiology and Chronic Health Evaluation IV score, using data collected at ARDS onset in patients hospitalized at a single center in 2016 and 2017. Discrimination of hospital mortality and the ARDS-specific outcome was evaluated using the area under the receiver operator characteristic curve (AUROC). Measure calibration was also evaluated.Results: Among 340 patients with ARDS, 125 (37%) died during hospitalization and 36 (10.6%) had the ARDS-specific outcome, including one who received ECMO. Among the five individual ARDS severity measures, the RALE score had the highest discrimination of the ARDS-specific outcome (AUROC = 0.67; 95% confidence interval [CI], 0.58-0.77), although other ARDS severity measures had similar performance. However, their ability to discriminate overall mortality was low. In contrast, the Acute Physiology and Chronic Health Evaluation IV score best discriminated overall mortality (AUROC = 0.73; 95% CI, 0.67-0.79) but was unable to discriminate the ARDS-specific outcome (AUROC = 0.54; 95% CI, 0.44-0.65). Among ARDS composite severity scores, the lung injury score had an AUROC = 0.67 (95% CI, 0.58-0.75) for the ARDS-specific outcome whereas the novel score had an AUROC = 0.70 (95% CI, 0.61-0.79). Patients grouped by quartile of the novel score had a 6%, 2%, 10%, and 24% rate of the ARDS-specific outcome.Conclusions: Although most ARDS severity measures had poor discrimination of hospital mortality, they performed better at predicting death from severe pulmonary dysfunction or ECMO needs. A novel composite score had the highest discrimination of this outcome.

Designing an ARDS trial for 2020 and beyond: focus on enrichment strategies.

With the exception of a few successes in trials of supportive care, the majority of interventional clinical trials for acute respiratory distress syndrome (ARDS) have not led to new therapies. To improve the likelihood of benefit from clinical trial interventions in ARDS, clinical trial design must be improved. To optimize trial design, many factors need to be considered including the type of therapy to be tested, the type of trial (phase 2 or 3), how patients will be selected, primary and secondary end-points, and strategy for conduct of the trial, including potential newer trial designs such as platform or adaptive trials. Of these, optimization of patient selection is central to the likelihood of success and is particularly relevant in ARDS, which is a heterogeneous clinical syndrome, not a homogeneous disease. Recent advances including improved understanding of pathophysiologic mechanisms and better tools for outcome prediction in ARDS should facilitate both predictive and prognostic enrichment. This commentary focuses on new information and novel methods for prognostic and predictive enrichment that may be useful to optimize patient selection and increase the likelihood of positive clinical trials in ARDS.

COVID-19 recovery: potential treatments for post-intensive care syndrome

COVID-19 recovery: potential treatments for post-intensive care syndrome

BioPETsurv: Methodology and open source software to evaluate biomarkers for prognostic enrichment of time-to-event clinical trials.

Biomarkers can be used to enrich a clinical trial for patients at higher risk for an outcome, a strategy termed "prognostic enrichment." Methodology is needed to evaluate biomarkers for prognostic enrichment of trials with time-to-event endpoints such as survival. Key considerations when considering prognostic enrichment include: clinical trial sample size; the number of patients one must screen to enroll the trial; and total patient screening costs and total per-patient trial costs. The Biomarker Prognostic Enrichment Tool for Survival Outcomes (BioPETsurv) is a suite of methods for estimating these elements to evaluate a prognostic enrichment biomarker and/or plan a prognostically enriched clinical trial with a time-to-event primary endpoint. BioPETsurv allows investigators to analyze data on a candidate biomarker and potentially censored survival times. Alternatively, BioPETsurv can simulate data to match a particular clinical setting. BioPETsurv's data simulator enables investigators to explore the potential utility of a prognostic enrichment biomarker for their clinical setting. Results demonstrate that both modestly prognostic and strongly prognostic biomarkers can improve trial metrics such as reducing sample size or trial costs. In addition to the quantitative analysis provided by BioPETsurv, investigators should consider the generalizability of trial results and evaluate the ethics of trial eligibility criteria. BioPETsurv is freely available as a package for the R statistical computing platform, and as a webtool at www.prognosticenrichment.com/surv.

Association Between Early Recovery of Kidney Function After Acute Kidney Injury and Long-term Clinical Outcomes

The severity of acute kidney injury (AKI) is usually determined based on the maximum serum creatinine concentration. However, the trajectory of kidney function recovery could be an additional important dimension of AKI severity. To assess whether the trajectory of kidney function recovery within 72 hours after AKI is associated with long-term risk of clinical outcomes. This prospective, multicenter cohort study enrolled 1538 adults with or without AKI 3 months after hospital discharge between December 1, 2009, and February 28, 2015. Statistical analyses were completed November 1, 2018. Participants with or without AKI were matched based on demographic characteristics, site, comorbidities, and prehospitalization estimated glomerular filtration rate. Participants with AKI were classified as having resolving or nonresolving AKI based on previously published definitions. Resolving AKI was defined as a decrease in serum creatinine concentration of 0.3 mg/dL or more or 25% or more from maximum in the first 72 hours after AKI diagnosis. Nonresolving AKI was defined as AKI not meeting the definition for resolving AKI. The primary outcome was a composite of major adverse kidney events (MAKE), defined as incident or progressive chronic kidney disease, long-term dialysis, or all-cause death during study follow-up. Among 1538 participants (964 men; mean [SD] age, 64.6 [12.7] years), 769 (50%) had no AKI, 475 (31%) had a resolving AKI pattern, and 294 (19%) had a nonresolving AKI pattern. After a median follow-up of 4.7 years, the outcome of MAKE occurred in 550 (36%) of all participants. The adjusted hazard ratio for MAKE was higher for patients with resolving AKI (adjusted hazard ratio, 1.52; 95% CI, 1.01-2.29; P = .04) and those with nonresolving AKI (adjusted hazard ratio 2.30; 95% CI, 1.52-3.48; P < .001) compared with participants without AKI. Within the population of patients with AKI, nonresolving AKI was associated with a 51% greater risk of MAKE (95% CI, 22%-88%; P < .001) compared with resolving AKI. The higher risk of MAKE among patients with nonresolving AKI was explained by a higher risk of incident and progressive chronic kidney disease. This study suggests that the 72-hour period immediately after AKI distinguishes the risk of clinically important kidney-specific long-term outcomes. The identification of different AKI recovery patterns may improve patient risk stratification, facilitate prognostic enrichment in clinical trials, and enable recognition of patients who may benefit from nephrology consultation.

PERSEVERE Biomarkers Predict Severe Acute Kidney Injury and Renal Recovery in Pediatric Septic Shock.

Rationale: Acute kidney injury (AKI), a common complication of sepsis, is associated with substantial morbidity and mortality and lacks definitive disease-modifying therapy. Early, reliable identification of at-risk patients is important for targeted implementation of renal protective measures. The updated Pediatric Sepsis Biomarker Risk Model (PERSEVERE-II) is a validated, multibiomarker prognostic enrichment strategy to estimate baseline mortality risk in pediatric septic shock.Objectives: To assess the association between PERSEVERE-II mortality probability and the development of severe, sepsis-associated AKI on Day 3 (D3 SA-AKI) in pediatric septic shock.Methods: We performed secondary analysis of a prospective observational study of children with septic shock in whom the PERSEVERE biomarkers were measured to assign a PERSEVERE-II baseline mortality risk.Measurements and Main Results: Among 379 patients, 65 (17%) developed severe D3 SA-AKI. The proportion of patients developing severe D3 SA-AKI increased directly with increasing PERSEVERE-II risk category, and increasing PERSEVERE-II mortality probability was independently associated with increased odds of severe D3 SA-AKI after adjustment for age and illness severity (odds ratio, 1.4; 95% confidence interval, 1.2-1.7; P < 0.001). Similar associations were found between increasing PERSEVERE-II mortality probability and the need for renal replacement therapy. Lower PERSEVERE-II mortality probability was independently associated with increased odds of renal recovery among patients with early AKI. A newly derived model incorporating the PERSEVERE biomarkers and Day 1 AKI status predicted severe D3 SA-AKI with an area under the received operating characteristic curve of 0.95 (95% confidence interval, 0.92-0.98).Conclusions: Among children with septic shock, the PERSEVERE biomarkers predict severe D3 SA-AKI and identify patients with early AKI who are likely to recover.

Persistent severe acute respiratory distress syndrome for the prognostic enrichment of trials.

BackgroundAcute respiratory distress syndrome (ARDS) is heterogeneous. As an indication of the heterogeneity of ARDS, there are patients whose syndrome improves rapidly (i.e., within 24 hours), others whose hypoxemia improves gradually and still others whose severe hypoxemia persists for several days. The latter group of patients with persistent severe ARDS poses challenges to clinicians. We attempted to assess the baseline characteristics and outcomes of persistent severe ARDS and to identify which variables are useful to predict it.MethodsA secondary analysis of patient-level data from the ALTA, EDEN and SAILS ARDSNet clinical trials was conducted. We defined persistent severe ARDS as a partial pressure of arterial oxygen to fraction of inspired oxygen ratio (PaO2:FiO2) of equal to or less than 100 mmHg on the second study day following enrollment. Regularized logistic regression with an L1 penalty [Least Absolute Shrinkage and Selection Operator (LASSO)] techniques were used to identify predictive variables of persistent severe ARDS.ResultsOf the 1531 individuals with ARDS alive on the second study day after enrollment, 232 (15%) had persistent severe ARDS. Of the latter, 100 (43%) individuals had mild or moderate hypoxemia at baseline. Usage of vasopressors was greater [144/232 (62%) versus 623/1299 (48%); p<0.001] and baseline severity of illness was higher in patients with versus without persistent severe ARDS. Mortality at 60 days [95/232 (41%) versus 233/1299 (18%); p<0.001] was higher, and ventilator-free (p<0.001), intensive care unit-free [0 (0–14) versus 19 (7–23); p<0.001] and non-pulmonary organ failure-free [3 (0–21) versus 20 (1–26); p<0.001] days were fewer in patients with versus without persistent severe ARDS. PaO2:FiO2, FiO2, hepatic failure and positive end-expiratory pressure at enrollment were useful predictive variables.ConclusionsPatients with persistent severe ARDS have distinct baseline characteristics and poor prognosis. Identifying such patients at enrollment may be useful for the prognostic enrichment of trials.

Plasma sTNFR1 and IL8 for prognostic enrichment in sepsis trials: a prospective cohort study

BackgroundEnrichment strategies improve therapeutic targeting and trial efficiency, but enrichment factors for sepsis trials are lacking. We determined whether concentrations of soluble tumor necrosis factor receptor-1 (sTNFR1), interleukin-8 (IL8), and angiopoietin-2 (Ang2) could identify sepsis patients at higher mortality risk and serve as prognostic enrichment factors.MethodsIn a multicenter prospective cohort study of 400 critically ill septic patients, we derived and validated thresholds for each marker and expressed prognostic enrichment using risk differences (RD) of 30-day mortality as predictive values. We then used decision curve analysis to simulate the prognostic enrichment of each marker and compare different prognostic enrichment strategies.Measurements and main resultsAn admission sTNFR1 concentration > 8861 pg/ml identified patients with increased mortality in both the derivation (RD 21.6%) and validation (RD 17.8%) populations. Among immunocompetent patients, an IL8 concentration > 94 pg/ml identified patients with increased mortality in both the derivation (RD 17.7%) and validation (RD 27.0%) populations. An Ang2 level > 9761 pg/ml identified patients at 21.3% and 12.3% increased risk of mortality in the derivation and validation populations, respectively. Using sTNFR1 or IL8 to select high-risk patients improved clinical trial power and efficiency compared to selecting patients with septic shock. Ang2 did not outperform septic shock as an enrichment factor.ConclusionsThresholds for sTNFR1 and IL8 consistently identified sepsis patients with higher mortality risk and may have utility for prognostic enrichment in sepsis trials.