From prognostication to therapeutics: Four key questions to accelerate the development of therapeutics for patients with acute-on-chronic liver failure.

Acute-on-chronic liver failure (ACLF) has come a long way as a clinical concept within the hepatology and liver transplant communities. Though the term was proposed in 1995, the first recognition of the entity along with a consensus definition emerged in 2009. Subsequently, the entity has sparked great interest, inspired several consensus conferences, and inspired national societies to form professional ACLF affinity groups (eg, special interest group). Multicenter consortia have been established all over the world to study this condition, including the North American Consortium for the Study of End-Stage Liver Disease, Chronic Liver Failure consortium, Asian Pacific Association for the Study of Liver Diseases ACLF Research Consortium, Chronic Liver disease Evolution And Registry for Events and Decompensation, and the LiverHope Consortium. Collectively, these consortia have enrolled tens of thousands of patients with or at risk for ACLF across dozens of countries and characterized in detail the predictors, pathogenesis, and progression of patients with ACLF. Perhaps most importantly, they have produced essential data characterizing the excess morbidity and mortality that patients with ACLF face, making a compelling case for the urgent need for therapeutic strategies for this condition.

Validation in type 2 diabetes of a metabolomic signature of all-cause mortality.

CONTEXT: Mortality in type 2 diabetes is twice that of the normoglycemic population. Unravelling biomarkers that identify high-risk patients for referral to the most aggressive and costly prevention strategies is needed. OBJECTIVE: To validate in type 2 diabetes the association with all-cause mortality of a 14-metabolite score (14-MS) previously reported in the general population and whether this score can be used to improve well-established mortality prediction models. METHODS: This is a sub-study consisting of 600 patients from the "Sapienza University Mortality and Morbidity Event Rate" (SUMMER) study in diabetes, a prospective multicentre investigation on all-cause mortality in patients with type 2 diabetes. Metabolic biomarkers were quantified from serum samples using high-throughput proton nuclear magnetic resonance metabolomics. RESULTS: In type 2 diabetes, the 14-MS showed a significant (p < 0.0001) association with mortality, which was lower (p < 0.0001) than that reported in the general population. This difference was mainly due to two metabolites (histidine and ratio of polyunsaturated fatty acids to total fatty acids) with an effect size that was significantly (p = 0.01) lower in diabetes than in the general population. A parsimonious 12-MS (i.e. lacking the 2 metabolites mentioned above) improved patient discrimination and classification of two well-established mortality prediction models (p < 0.0001 for all measures). CONCLUSIONS: The metabolomic signature of mortality in the general population is only partially effective in type 2 diabetes. Prediction markers developed and validated in the general population must be revalidated if they are to be used in patients with diabetes.

Optimizing dynamic predictions from joint models using super learning.

Joint models for longitudinal and time-to-event data are often employed to calculate dynamic individualized predictions used in numerous applications of precision medicine. Two components of joint models that influence the accuracy of these predictions are the shape of the longitudinal trajectories and the functional form linking the longitudinal outcome history to the hazard of the event. Finding a single well-specified model that produces accurate predictions for all subjects and follow-up times can be challenging, especially when considering multiple longitudinal outcomes. In this work, we use the concept of super learning and avoid selecting a single model. In particular, we specify a weighted combination of the dynamic predictions calculated from a library of joint models with different specifications. The weights are selected to optimize a predictive accuracy metric using V-fold cross-validation. We use as predictive accuracy measures the expected quadratic prediction error and the expected predictive cross-entropy. In a simulation study, we found that the super learning approach produces results very similar to the Oracle model, which was the model with the best performance in the test datasets. All proposed methodology is implemented in the freely available R package JMbayes2.

Prognostic Factors and Models to Predict Mortality Outcomes in Patients with Peripheral Arterial Disease: A Systematic Review.

OBJECTIVE: Predicting adverse outcomes in patients with peripheral arterial disease (PAD) is a complex task owing to the heterogeneity in patient and disease characteristics. This systematic review aimed to identify prognostic factors and prognostic models to predict mortality outcomes in patients with PAD Fontaine stage I - III or Rutherford category 0 - 4. DATA SOURCES: PubMed, Embase, and Cochrane Database of Systematic Reviews were searched to identify studies examining individual prognostic factors or studies aiming to develop or validate a prognostic model for mortality outcomes in patients with PAD. REVIEW METHODS: Information on study design, patient population, prognostic factors, and prognostic model characteristics was extracted, and risk of bias was evaluated. RESULTS: Sixty nine studies investigated prognostic factors for mortality outcomes in PAD. Over 80 single prognostic factors were identified, with age as a predictor of death in most of the studies. Other common factors included sex, diabetes, and smoking status. Six studies had low risk of bias in all domains, and the remainder had an unclear or high risk of bias in at least one domain. Eight studies developed or validated a prognostic model. All models included age in their primary model, but not sex. All studies had similar discrimination levels of > 70%. Five of the studies on prognostic models had an overall high risk of bias, whereas two studies had an overall unclear risk of bias. CONCLUSION: This systematic review shows that a large number of prognostic studies have been published, with heterogeneity in patient populations, outcomes, and risk of bias. Factors such as sex, age, diabetes, hypertension, and smoking are significant in predicting mortality risk among patients with PAD Fontaine stage I - III or Rutherford category 0 - 4.

Development of prognostic models for predicting 90-day neurological function and mortality after cardiac arrest.

BACKGROUND: The survivors of cardiac arrest experienced vary extent of hypoxic ischemic brain injury causing mortality and long-term neurologic disability. However, there is still a need to develop robust and reliable prognostic models that can accurately predict these outcomes. OBJECTIVES: To establish reliable models for predicting 90-day neurological function and mortality in adult ICU patients recovering from cardiac arrest. METHODS: We enrolled patients who had recovered from cardiac arrest at Binhaiwan Central Hospital of Dongguan, from January 2018 to July 2021. The study's primary outcome was 90-day neurological function, assessed and divided into two categories using the Cerebral Performance Category (CPC) scale: either good (CPC 1-2) or poor (CPC 3-5). The secondary outcome was 90-day mortality. We analyzed the relationships between risk factors and outcomes individually. A total of four models were developed: two multivariable logistic regression models (models 1 and 2) for predicting neurological function, and two Cox regression models (models 3 and 4) for predicting mortality. Models 2 and 4 included new neurological biomarkers as predictor variables, while models 1 and 3 excluded. We evaluated calibration, discrimination, clinical utility, and relative performance to establish superiority between the models. RESULTS: Model 1 incorporates variables such as gender, site of cardiopulmonary resuscitation (CPR), total CPR time, and acute physiology and chronic health evaluation II (APACHE II) score, while model 2 includes gender, site of CPR, APACHE II score, and serum level of ubiquitin carboxy-terminal hydrolase L1 (UCH-L1). Model 2 outperforms model 1, showcasing a superior area under the receiver operating characteristic curve (AUC) of 0.97 compared to 0.83. Additionally, model 2 exhibits improved accuracy, sensitivity, and specificity. The decision curve analysis confirms the net benefit of model 2. Similarly, models 3 and 4 are designed to predict 90-day mortality. Model 3 incorporates the variables such as site of CPR, total CPR time, and APACHE II score, while model 4 includes APACHE II score, total CPR time, and serum level of UCH-L1. Model 4 outperforms model 3, showcasing an AUC of 0.926 and a C-index of 0.830. The clinical decision curve analysis also confirms the net benefit of model 4. CONCLUSIONS: By integrating new neurological biomarkers, we have successfully developed enhanced models that can predict 90-day neurological function and mortality outcomes more accurately.

Development and validation of prognostic models for small cell lung cancer patients with liver metastasis: a SEER population-based study.

BACKGROUND: This study was to establish and validate prediction models to predict the cancer-specific survival (CSS) and overall survival (OS) of small-cell lung cancer (SCLC) patients with liver metastasis. METHODS: In the retrospective cohort study, SCLC patients with liver metastasis between 2010 and 2015 were retrospectively retrieved from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were randomly divided into the training group and testing group (3: 1 ratio). The Cox proportional hazards model was used to determine the predictive factors for CSS and OS in SCLC with liver metastasis. The prediction models were conducted based on the predictive factors. The performances of the prediction models were evaluated by concordance indexes (C-index), and calibration plots. The clinical value of the models was evaluated by decision curve analysis (DCA). RESULTS: In total, 8,587 patients were included, with 154 patients experiencing CSS and 154 patients experiencing OS. The median follow-up was 3 months. Age, gender, marital status, N stage, lung metastases, multiple metastases surgery of metastatic site, chemotherapy, and radiotherapy were independent predictive factors for the CSS and OS of SCLC patients with liver metastasis. The prediction models presented good performances of CSS and OS among patients with liver metastasis, with the C-index for CSS being 0.724, whereas the C-index for OS was 0.732, in the training set. The calibration curve showed a high degree of consistency between the actual and predicted CSS and OS. DCA suggested that the prediction models provided greater net clinical benefit to these patients. CONCLUSION: Our prediction models showed good predictive performance for the CSS and OS among SCLC patients with liver metastasis. Our developed nomograms may help clinicians predict CSS and OS in SCLC patients with liver metastasis.

Consolidated Reporting Guidelines for Prognostic and Diagnostic Machine Learning Models (CREMLS).

The number of papers presenting machine learning (ML) models that are being submitted to and published in the Journal of Medical Internet Research and other JMIR Publications journals has steadily increased. Editors and peer reviewers involved in the review process for such manuscripts often go through multiple review cycles to enhance the quality and completeness of reporting. The use of reporting guidelines or checklists can help ensure consistency in the quality of submitted (and published) scientific manuscripts and, for example, avoid instances of missing information. In this Editorial, the editors of JMIR Publications journals discuss the general JMIR Publications policy regarding authors' application of reporting guidelines and specifically focus on the reporting of ML studies in JMIR Publications journals, using the Consolidated Reporting of Machine Learning Studies (CREMLS) guidelines, with an example of how authors and other journals could use the CREMLS checklist to ensure transparency and rigor in reporting.

Evaluation of mortality, prognostic parameters, and treatment efficacy in mycosis fungoides.

BACKGROUND AND OBJECTIVES: Mycosis fungoides (MF), the most common primary cutaneous T-cell lymphoma, is characterized by a variable clinical course, presenting either as indolent disease or showing fatal progression due to extracutaneous involvement. Importantly, the lack of prognostic models and predominantly palliative therapy settings hamper patient care. Here, we aimed to define survival rates, disease prediction accuracy, and treatment impact in MF. PATIENTS AND METHODS: Hundred-forty MF patients were assessed retrospectively. Prognosis and disease progression/survival were analyzed using univariate Cox proportional hazards regression model and Kaplan-Meier estimates. RESULTS: Skin tumors were linked to shorter progression-free, overall survival and a 3.48 increased risk for disease progression when compared to erythroderma. The Cutaneous Lymphoma International Prognostic Index identified patients at risk in early-stage disease only. Moreover, expression of Ki-67 >20%, CD30 >10%, CD20+, and CD7- were associated with a significantly worse outcome independent of disease stage. Only single-agent interferon-α and phototherapy combined with interferon-α or retinoids/bexarotene achieved long-term disease control in MF. CONCLUSIONS: Our data support predictive validity of prognostic factors and models in MF and identified further potential parameters associated with poor survival. Prospective studies on prognostic indices across disease stages and treatment modalities are needed to predict and improve survival.

Interleukin-6 in Sepsis-Promising but Yet to Be Proven.

How to cite this article: Sinha S. Interleukin-6 in Sepsis-Promising but Yet to Be Proven. Indian J Crit Care Med 2024;28(7):629-631.

Cuproptosis-related LncRNA signatures as a prognostic model for head and neck squamous cell carcinoma.

Cuproptosis is a novel, distinct form of regulated cell death. However, little is known about the role of cuproptosis-related lncRNAs (CRlncRNAs) in head and neck squamous cell carcinoma (HNSCC). This study aimed to identify a CRlncRNAs signature, explore its prognostic value in HNSCC. RNA-seq data and relevant clinical data were downloaded from The Cancer Genome Atlas (TCGA) database, and cuproptosis-related genes were identified from a search of the relevant candidate-gene literature. Analysis of differentially expressed lncRNAs (DElncRNAs) was performed using the R package "edgeR". The intersection of the lncRNAs between DElncRNAs and CRlncRNAs was obtained using the R package "Venn Diagram". Univariate Cox regression was used to identify cuproptosis-related prognostic lncRNAs. LASSO-Cox method was used to narrow these cuproptosis-related prognostic lncRNAs and construct a prognostic model. Multiple statistical methods were used to evaluate the predictive ability of the model. Moreover, the relationships between the model and immune cell subpopulations, related functions and pathways and drug sensitivity were explored. Then, two risk groups were established according to the risk score calculated by the CRlncRNAs signature included three lncRNAs. In HNSCC patients, the risk score was a better predictor of survival than traditional clinicopathological features. In addition, significant differences in immune cells such as B cells, T cells and macrophages were observed between the two groups. Finally, the high-risk group had a lower IC50 for certain chemotherapeutic agents, such as cisplatin and cetuximab. This 3 CRlncRNAs signature is a powerful prognostic biomarker for predicting clinical outcomes and therapeutic responses in HNSCC patients.

A model based on immune-related lncRNA pairs and its potential prognostic value in immunotherapy for melanoma.

A model based on long non-coding RNA (lncRNA) pairs independent of expression quantification was constructed to evaluate prognosis melanoma and response to immunotherapy in melanoma. RNA sequencing data and clinical information were retrieved and downloaded from The Cancer Genome Atlas and the Genotype-Tissue Expression databases. We identified differentially expressed immune-related lncRNAs (DEirlncRNAs), matched them, and used least absolute shrinkage and selection operator and Cox regression to construct predictive models. The optimal cutoff value of the model was determined using a receiver operating characteristic curve and used to categorize melanoma cases into high-risk and low-risk groups. The predictive efficacy of the model with respect to prognosis was compared with that of clinical data and ESTIMATE (Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data). Then, we analyzed the correlations of risk score with clinical characteristics, immune cell invasion, anti-tumor, and tumor-promoting activities. Differences in survival, degree of immune cell infiltration, and intensity of anti-tumor and tumor-promoting activities were also evaluated in the high- and low-risk groups. A model based on 21 DEirlncRNA pairs was established. Compared with ESTIMATE score and clinical data, this model could better predict outcomes of melanoma patients. Follow-up analysis of the model's effectiveness showed that patients in the high-risk group had poorer prognosis and were less likely to benefit from immunotherapy compared with those in the low-risk group. Moreover, there were differences in tumor-infiltrating immune cells between the high-risk and low-risk groups. By pairing the DEirlncRNA, we constructed a model to evaluate the prognosis of cutaneous melanoma independent of a specific level of lncRNA expression.

Risk Model Development and Validation in Clinical Oncology: Lessons Learned.

Reliable risk models can greatly facilitate patient-centered inferences and decisions. Herein we summarize key considerations related to risk modeling in clinical oncology. Often overlooked challenges include data quality, missing data, effective sample size estimation, and selecting the variables to be included in the risk model. The stability and quality of the model should be carefully interrogated with particular emphasis on rigorous internal validation.

Review on natural killer/T-cell lymphoma.

Extranodal natural killer (NK)/T-cell lymphoma (ENKTL) is strongly associated with Epstein-Barr virus (EBV) and has a high prevalence in Asian and in Central and South America. About 85% of ENKTLs derive from NK cells and 15% from T-cells. Various factors have been implicated in the development of ENKTL. Molecular pathogenesis of NK/T-cell lymphomas include mutations of genes, involving in the Janus Kinase/signal transducer and activator of transcription pathway, RNA helicase family, epigenetic regulation, and tumor suppression. The relationship between ENKTL and human leukocyte antigen has been demonstrated. Radiotherapy plays a key role in the first-line treatment of early-stage. In stage III/IV diseases, non-anthracycline-regimens-containing L-asparaginase are recommended. Although clinical remission after L-asparaginase-based combination therapy has been achieved in the majority of patients with advanced-stage or relapsed/refractory extranodal NK/T-cell lymphoma-nasal type, the long-term overall survival is still poor. Recently, immunotherapy and new therapeutic targets have gained much attention. In this article, we discuss the pathogenesis, diagnosis, prognostic models and management options of ENKTL.

Baseline Quality of Life is a Strong and Independent Prognostic Factor for Overall Survival in Metastatic Colorectal Cancer.

BACKGROUND: Previous studies have established that higher baseline quality of life (QOL) scores are associated with improved survival in patients with metastatic colorectal cancer (mCRC). We examined the relationship between overall survival (OS) and baseline QOL. PATIENTS AND METHODS: A total of 1 247 patients with mCRC participating in N9741 (comparing bolus 5-FU/LV, irinotecan [IFL] vs infusional 5-FU/leucovorin [LV]/oxaliplatin [FOLFOX] vs. irinotecan/oxaliplatin [IROX]) provided data at baseline on overall QOL using a single-item linear analogue self-assessment (LASA) 0-100 point scale. The association of OS according to clinically deficient (defined as CD-QOL, score 0-50) vs not clinically deficient (nCD-QOL, score 51-100) baseline QOL scores was tested. A multivariable analysis using Cox proportional hazards modeling was performed to adjust for the effects of multiple baseline factors. An exploratory analysis was performed evaluating OS according to baseline QOL status among patients who did or did not receive second-line therapy. RESULTS: Baseline QOL was a strong predictor of OS for the whole cohort (CD-QOL vs nCD-QOL: 11.2 months vs 18.4 months, P < .0001), and in each arm IFL 12.4 vs 15.1 months, FOLFOX 11.1 months vs 20.6 months, and IROX 8.9 months vs 18.1 months. Baseline QOL was associated with baseline performance status (PS) (P < .0001). After adjusting for PS and treatment arm, baseline QOL was still associated with OS (P = .017). CONCLUSIONS: Baseline QOL is an independent prognostic factor for OS in patients with mCRC. The demonstration that patient-assessed QOL and PS are independent prognostic indicators suggests that these assessments provide important complementary prognostic information.

Using temporal recalibration to improve the calibration of risk prediction models in competing risk settings when there are trends in survival over time.

We have previously proposed temporal recalibration to account for trends in survival over time to improve the calibration of predictions from prognostic models for new patients. This involves first estimating the predictor effects using data from all individuals (full dataset) and then re-estimating the baseline using a subset of the most recent data whilst constraining the predictor effects to remain the same. In this article, we demonstrate how temporal recalibration can be applied in competing risk settings by recalibrating each cause-specific (or subdistribution) hazard model separately. We illustrate this using an example of colon cancer survival with data from the Surveillance Epidemiology and End Results (SEER) program. Data from patients diagnosed in 1995-2004 were used to fit two models for deaths due to colon cancer and other causes respectively. We discuss considerations that need to be made in order to apply temporal recalibration such as the choice of data used in the recalibration step. We also demonstrate how to assess the calibration of these models in new data for patients diagnosed subsequently in 2005. Comparison was made to a standard analysis (when improvements over time are not taken into account) and a period analysis which is similar to temporal recalibration but differs in the data used to estimate the predictor effects. The 10-year calibration plots demonstrated that using the standard approach over-estimated the risk of death due to colon cancer and the total risk of death and that calibration was improved using temporal recalibration or period analysis.

Prognostic models of in-hospital mortality of intensive care patients using neural representation of unstructured text: A systematic review and critical appraisal.

OBJECTIVE: To review and critically appraise published and preprint reports of prognostic models of in-hospital mortality of patients in the intensive-care unit (ICU) based on neural representations (embeddings) of clinical notes. METHODS: PubMed and arXiv were searched up to August 1, 2022. At least two reviewers independently selected the studies that developed a prognostic model of in-hospital mortality of intensive-care patients using free-text represented as embeddings and extracted data using the CHARMS checklist. Risk of bias was assessed using PROBAST. Reporting on the model was assessed with the TRIPOD guideline. To assess the machine learning components that were used in the models, we present a new descriptive framework based on different techniques to represent text and provide predictions from text. The study protocol was registered in the PROSPERO database (CRD42022354602). RESULTS: Eighteen studies out of 2,825 were included. All studies used the publicly-available MIMIC dataset. Context-independent word embeddings are widely used. Model discrimination was provided by all studies (AUROC 0.75-0.96), but measures of calibration were scarce. Seven studies used both structural clinical variables and notes. Model discrimination improved when adding clinical notes to variables. None of the models was externally validated and often a simple train/test split was used for internal validation. Our critical appraisal demonstrated a high risk of bias in all studies and concerns regarding their applicability in clinical practice. CONCLUSION: All studies used a neural architecture for prediction and were based on one publicly available dataset. Clinical notes were reported to improve predictive performance when used in addition to only clinical variables. Most studies had methodological, reporting, and applicability issues. We recommend reporting both model discrimination and calibration, using additional data sources, and using more robust evaluation strategies, including prospective and external validation. Finally, sharing data and code is encouraged to improve study reproducibility.

A systematic review on prognostic factors and models for changes in quality of life and depressive symptoms after multi-domain cognitive training in healthy older adults: Who benefits?

BACKGROUND: As we age, cognitive abilities decline which can lead to a decrease in quality of life (QoL) and an increase in depressive symptoms even in healthy (i.e., non-clinical) older adults. Cognitive trainings (CT) are a promising approach to not only improve cognition, but also QoL and mood. However, it is unclear which prognostic factors are associated with changes in QoL and depression after CT. OBJECTIVE: To identify prognostic factors and models of changes in QoL and depressive symptoms after a multi-domain CT in healthy older adults. METHODS: MEDLINE, Web of Science Core Collection, CENTRAL and PsycInfo were systematically searched for multi-domain CT studies in healthy older adults until August 2022. Studies investigating prognostic factors and/or models on QoL and depressive symptoms were included. Risk of bias was assessed using the QUIPS and the PROBAST tool. RESULTS: Our search revealed N = 12,916 studies, of which only 6 could be included in the review. Prognostic factors included were sociodemographics, cognitive reserve, cognitive baseline level, and cognitive change. However, data were too rare and heterogenous regarding the assessment measures of QoL and depressive scores, the used multi-domain CT and the investigated prognostic factors to draw clear conclusions or conduct meta-analyses. CONCLUSION: There is an urgent need for research on prognostic factors and models of changes in QoL and depressive symptoms after CT in healthy older participants as they could help to tailor interventions to individuals in terms of future precision medicine approaches.

Consolidated Reporting Guidelines for Prognostic and Diagnostic Machine Learning Modeling Studies: Development and Validation.

BACKGROUND: The reporting of machine learning (ML) prognostic and diagnostic modeling studies is often inadequate, making it difficult to understand and replicate such studies. To address this issue, multiple consensus and expert reporting guidelines for ML studies have been published. However, these guidelines cover different parts of the analytics lifecycle, and individually, none of them provide a complete set of reporting requirements. OBJECTIVE: We aimed to consolidate the ML reporting guidelines and checklists in the literature to provide reporting items for prognostic and diagnostic ML in in-silico and shadow mode studies. METHODS: We conducted a literature search that identified 192 unique peer-reviewed English articles that provide guidance and checklists for reporting ML studies. The articles were screened by their title and abstract against a set of 9 inclusion and exclusion criteria. Articles that were filtered through had their quality evaluated by 2 raters using a 9-point checklist constructed from guideline development good practices. The average κ was 0.71 across all quality criteria. The resulting 17 high-quality source papers were defined as having a quality score equal to or higher than the median. The reporting items in these 17 articles were consolidated and screened against a set of 6 inclusion and exclusion criteria. The resulting reporting items were sent to an external group of 11 ML experts for review and updated accordingly. The updated checklist was used to assess the reporting in 6 recent modeling papers in JMIR AI. Feedback from the external review and initial validation efforts was used to improve the reporting items. RESULTS: In total, 37 reporting items were identified and grouped into 5 categories based on the stage of the ML project: defining the study details, defining and collecting the data, modeling methodology, model evaluation, and explainability. None of the 17 source articles covered all the reporting items. The study details and data description reporting items were the most common in the source literature, with explainability and methodology guidance (ie, data preparation and model training) having the least coverage. For instance, a median of 75% of the data description reporting items appeared in each of the 17 high-quality source guidelines, but only a median of 33% of the data explainability reporting items appeared. The highest-quality source articles tended to have more items on reporting study details. Other categories of reporting items were not related to the source article quality. We converted the reporting items into a checklist to support more complete reporting. CONCLUSIONS: Our findings supported the need for a set of consolidated reporting items, given that existing high-quality guidelines and checklists do not individually provide complete coverage. The consolidated set of reporting items is expected to improve the quality and reproducibility of ML modeling studies.

Clinical decision support for ExtraCorporeal Membrane Oxygenation: Will we fly by wire?

Prognostic modelling techniques have rapidly evolved over the past decade and may greatly benefit patients supported with ExtraCorporeal Membrane Oxygenation (ECMO). Epidemiological and computational physiological approaches aim to provide more accurate predictive assessments of ECMO-related risks and benefits. Implementation of these approaches may produce predictive tools that can improve complex clinical decisions surrounding ECMO allocation and management. This Review describes current applications of prognostic models and elaborates on upcoming directions for their clinical applicability in decision support tools directed at improved allocation and management of ECMO patients. The discussion of these new developments in the field will culminate in a futuristic perspective leaving ourselves and the readers wondering whether we may "fly ECMO by wire" someday.

"Super-covariates": Using predicted control group outcome as a covariate in randomized clinical trials.

The power of randomized controlled clinical trials to demonstrate the efficacy of a drug compared with a control group depends not just on how efficacious the drug is, but also on the variation in patients' outcomes. Adjusting for prognostic covariates during trial analysis can reduce this variation. For this reason, the primary statistical analysis of a clinical trial is often based on regression models that besides terms for treatment and some further terms (e.g., stratification factors used in the randomization scheme of the trial) also includes a baseline (pre-treatment) assessment of the primary outcome. We suggest to include a "super-covariate"-that is, a patient-specific prediction of the control group outcome-as a further covariate (but not as an offset). We train a prognostic model or ensembles of such models on the individual patient (or aggregate) data of other studies in similar patients, but not the new trial under analysis. This has the potential to use historical data to increase the power of clinical trials and avoids the concern of type I error inflation with Bayesian approaches, but in contrast to them has a greater benefit for larger sample sizes. It is important for prognostic models behind "super-covariates" to generalize well across different patient populations in order to similarly reduce unexplained variability whether the trial(s) to develop the model are identical to the new trial or not. In an example in neovascular age-related macular degeneration we saw efficiency gains from the use of a "super-covariate".