Non-contrast CT radiomics-clinical machine learning model for futile recanalization after endovascular treatment in anterior circulation acute ischemic stroke.

OBJECTIVE: To establish a machine learning model based on radiomics and clinical features derived from non-contrast CT to predict futile recanalization (FR) in patients with anterior circulation acute ischemic stroke (AIS) undergoing endovascular treatment. METHODS: A retrospective analysis was conducted on 174 patients who underwent endovascular treatment for acute anterior circulation ischemic stroke between January 2020 and December 2023. FR was defined as successful recanalization but poor prognosis at 90 days (modified Rankin Scale, mRS 4-6). Radiomic features were extracted from non-contrast CT and selected using the least absolute shrinkage and selection operator (LASSO) regression method. Logistic regression (LR) model was used to build models based on radiomic and clinical features. A radiomics-clinical nomogram model was developed, and the predictive performance of the models was evaluated using area under the curve (AUC), accuracy, sensitivity, and specificity. RESULTS: A total of 174 patients were included. 2016 radiomic features were extracted from non-contrast CT, and 9 features were selected to build the radiomics model. Univariate and stepwise multivariate analyses identified admission NIHSS score, hemorrhagic transformation, NLR, and admission blood glucose as independent factors for building the clinical model. The AUC of the radiomics-clinical nomogram model in the training and testing cohorts were 0.860 (95%CI 0.801-0.919) and 0.775 (95%CI 0.605-0.945), respectively. CONCLUSION: The radiomics-clinical nomogram model based on non-contrast CT demonstrated satisfactory performance in predicting futile recanalization in patients with anterior circulation acute ischemic stroke.

When Life-Supporting Interventions Lead to Moral Distress.

Moral distress can impact nurses and the care team significantly. A profession dedicated to the principles of caring and compassion is often subjected to patients receiving futile treatment. With the proliferation of extreme life-prolonging measures come the difficulties in the withdrawal of those medical modalities. If a prognosis is poor and care is perceived as curative rather than palliative, providers may often feel conflicted and distressed by their interventions. The American Association of Colleges of Nursing has expressed growing concern about an increase in the use of inappropriate life-support treatments related to futile care. The compelling case of a severely beaten 69-year-old homeless man who had cardiac-arrested and was resuscitated after an unknown amount of down-time, provides the contextual framework for this report. Ethical conflicts can become very challenging, which inevitably increases the suffering of the patient and their caregivers. Research findings suggest that health care organizations can benefit from enacting processes that make ethical considerations an early and routine part of everyday clinical practice. A proactive approach to ethical conflicts may improve patient care outcomes and decrease moral distress.

Predictors of futile recanalization after endovascular treatment of acute ischemic stroke.

OBJECTIVE: Endovascular therapy (EVT) is the most successful treatment for patients with acute ischemic stroke (AIS) due to large vessel occlusion (LVO) in the anterior circulation. However, futile recanalization (FR) seriously affects the prognosis of these patients. The aim of this study was to investigate predictors of FR after EVT in patients with AIS. METHOD: Patients diagnosed with AIS due to anterior circulation LVO and receiving EVT between June 2020 and October 2022 were prospectively enrolled. FR after EVT was defined as a poor 90-day prognosis (modified Rankin Scale [mRS] score ≥ 3) despite achieving successful reperfusion (modified Thrombolysis in Cerebral Infarction [mTICI] classification of 2b-3). All included patients were categorized into control group (mRS score < 3) and FR group (mRS score ≥ 3). Demographic characteristics, comorbidities (hypertension, diabetes, atrial fibrillation, smoking, etc.), stroke-specific data (NIHSS score, ASPECT score and site of occlusion), procedure data (treatment type [direct thrombectomy vs. bridging thrombectomy], degree of vascular recanalization [mTICI], procedure duration time and onset-recanalization time), laboratory indicators (lymphocytes count, neutrophils count, monocytes count, C-reactive protein, neutrophil-to-lymphocyte ratio [NLR], monocyte-to-high-density lipoprotein ratio [MHR], lymphocyte-to-monocyte ratio [LMR], lymphocyte-to-C-reactive protein ratio [LCR], lymphocyte-to-high-density lipoprotein ratio[LHR], total cholesterol and triglycerides.) were compared between the two groups. Multivariate logistic regression analysis was performed to explore independent predictors of FR after EVT. RESULTS: A total of 196 patients were included in this study, among which 57 patients were included in the control group and 139 patients were included in the FR group. Age, proportion of patients with hypertension and diabetes mellitus, median NIHSS score, CRP level, procedure duration time, neutrophil count and NLR were higher in the FR group than in the control group. Lymphocyte count, LMR, and LCR were lower in the FR group than in the control group. There were no significant differences in platelet count, monocytes count, total cholesterol, triglycerides, HDL, LDL, gender, smoking, atrial fibrillation, percentage of occluded sites, onset-recanalization time, ASPECT score and type of treatment between the two groups. Multivariate logistic regression analysis demonstrated that NLR was independently associated with FR after EVT (OR = 1.37, 95%CI = 1.005-1.86, P = 0.046). CONCLUSION: This study demonstrated that high NLR was associated with a risk of FR in patients with AIS due to anterior circulation LVO. These findings may help clinicians determine which patients with AIS are at higher risk of FR after EVT. Our study can provide a theoretical basis for interventions in the aforementioned population.

Futile recanalization is associated with increased risk of post-stroke epilepsy.

BACKGROUND: Endovascular treatment (EVT) is the standard of care of ischaemic stroke due to occlusion of large vessels. Although EVT can significantly improve short- and long-term outcomes, functional dependence can persist despite the achievement of a successful recanalization. The evidence about the predictors of post-stroke epilepsy (PSE) in patients with stroke treated by EVT is limited. We aimed to evaluate the relationship between futile recanalization and the risk of PSE. METHODS: We retrospectively identified consecutive adults with first-ever ischaemic stroke of anterior circulation who were treated with EVT. Futile recanalization was defined as poor 3-month functional status (modified Rankin scale score ≥ 3) despite complete or near-complete recanalization. Study outcome was the occurrence of PSE during the follow-up. RESULTS: The study included 327 patients with anterior circulation ischaemic stroke treated with EVT. Futile recanalization occurred in 116 (35.5%) patients and 26 (8.0%) developed PSE during a median follow-up of 35 [interquartile range, 22.7-55.2] months. Futile recanalization was more common among patients who developed PSE compared to those who did not (76.9% versus 31.9%; p < 0.001). Futile recanalization [hazard ratio (HR) = 5.63, 95% confidence interval (CI): 1.88-16.84; p = 0.002], large artery atherosclerosis (HR = 3.48, 95% CI: 1.44-8.40; p = 0.006), cortical involvement (HR = 15.51, 95% CI: 2.06-116.98; p = 0.008), and acute symptomatic status epilepticus (HR = 14.40, 95% CI: 2.80-73.98; p = 0.001) increased the risk of PSE. CONCLUSIONS: Futile recanalization after EVT is associated with increased risk of PSE in patients with ischaemic stroke due to occlusion of large vessel of the anterior circulation.

Impact of a prospective feedback loop aimed at reducing non-beneficial treatments in older people admitted to hospital and potentially nearing the end of life. A cluster stepped-wedge randomised controlled trial.

OBJECTIVES: To investigate if a prospective feedback loop that flags older patients at risk of death can reduce non-beneficial treatment at end of life. DESIGN: Prospective stepped-wedge cluster randomised trial with usual care and intervention phases. SETTING: Three large tertiary public hospitals in south-east Queensland, Australia. PARTICIPANTS: 14 clinical teams were recruited across the three hospitals. Teams were recruited based on a consistent history of admitting patients aged 75+ years, and needed a nominated lead specialist consultant. Under the care of these teams, there were 4,268 patients (median age 84 years) who were potentially near the end of life and flagged at risk of non-beneficial treatment. INTERVENTION: The intervention notified clinicians of patients under their care determined as at-risk of non-beneficial treatment. There were two notification flags: a real-time notification and an email sent to clinicians about the at-risk patients at the end of each screening day. The nudge intervention ran for 16-35 weeks across the three hospitals. MAIN OUTCOME MEASURES: The primary outcome was the proportion of patients with one or more intensive care unit (ICU) admissions. The secondary outcomes examined times from patients being flagged at-risk. RESULTS: There was no improvement in the primary outcome of reduced ICU admissions (mean probability difference [intervention minus usual care] = -0.01, 95% confidence interval -0.08 to 0.01). There were no differences for the times to death, discharge, or medical emergency call. There was a reduction in the probability of re-admission to hospital during the intervention phase (mean probability difference -0.08, 95% confidence interval -0.13 to -0.03). CONCLUSIONS: This nudge intervention was not sufficient to reduce the trial's non-beneficial treatment outcomes in older hospital patients. TRIAL REGISTRATION: Australia New Zealand Clinical Trial Registry, ACTRN12619000675123 (registered 6 May 2019).

Upstrapping to determine futility: predicting future outcomes nonparametrically from past data.

BACKGROUND: Clinical trials often involve some form of interim monitoring to determine futility before planned trial completion. While many options for interim monitoring exist (e.g., alpha-spending, conditional power), nonparametric based interim monitoring methods are also needed to account for more complex trial designs and analyses. The upstrap is one recently proposed nonparametric method that may be applied for interim monitoring. METHODS: Upstrapping is motivated by the case resampling bootstrap and involves repeatedly sampling with replacement from the interim data to simulate thousands of fully enrolled trials. The p-value is calculated for each upstrapped trial and the proportion of upstrapped trials for which the p-value criteria are met is compared with a pre-specified decision threshold. To evaluate the potential utility for upstrapping as a form of interim futility monitoring, we conducted a simulation study considering different sample sizes with several different proposed calibration strategies for the upstrap. We first compared trial rejection rates across a selection of threshold combinations to validate the upstrapping method. Then, we applied upstrapping methods to simulated clinical trial data, directly comparing their performance with more traditional alpha-spending and conditional power interim monitoring methods for futility. RESULTS: The method validation demonstrated that upstrapping is much more likely to find evidence of futility in the null scenario than the alternative across a variety of simulations settings. Our three proposed approaches for calibration of the upstrap had different strengths depending on the stopping rules used. Compared to O'Brien-Fleming group sequential methods, upstrapped approaches had type I error rates that differed by at most 1.7% and expected sample size was 2-22% lower in the null scenario, while in the alternative scenario power fluctuated between 15.7% lower and 0.2% higher and expected sample size was 0-15% lower. CONCLUSIONS: In this proof-of-concept simulation study, we evaluated the potential for upstrapping as a resampling-based method for futility monitoring in clinical trials. The trade-offs in expected sample size, power, and type I error rate control indicate that the upstrap can be calibrated to implement futility monitoring with varying degrees of aggressiveness and that performance similarities can be identified relative to considered alpha-spending and conditional power futility monitoring methods.

The Texas Medical Board and the Futility of Medical Exceptions to Abortion Bans.

This Viewpoint describes the failure of yet another state institution to generate meaningful guidance about medical exceptions to abortion bans.

Beta spending function based on conditional power in group sequential design.

Conditional power (CP) serves as a widely utilized approach for futility monitoring in group sequential designs. However, adopting the CP methods may lead to inadequate control of the type II error rate at the desired level. In this study, we introduce a flexible beta spending function tailored to regulate the type II error rate while employing CP based on a predetermined standardized effect size for futility monitoring (a so-called CP-beta spending function). This function delineates the expenditure of type II error rate across the entirety of the trial. Unlike other existing beta spending functions, the CP-beta spending function seamlessly incorporates beta spending concept into the CP framework, facilitating precise stagewise control of the type II error rate during futility monitoring. In addition, the stopping boundaries derived from the CP-beta spending function can be calculated via integration akin to other traditional beta spending function methods. Furthermore, the proposed CP-beta spending function accommodates various thresholds on the CP-scale at different stages of the trial, ensuring its adaptability across different information time scenarios. These attributes render the CP-beta spending function competitive among other forms of beta spending functions, making it applicable to any trials in group sequential designs with straightforward implementation. Both simulation study and example from an acute ischemic stroke trial demonstrate that the proposed method accurately captures expected power, even when the initially determined sample size does not consider futility stopping, and exhibits a good performance in maintaining overall type I error rates for evident futility.

Assessing Futile Trauma Transfers in Rural Appalachia Following a Regional Health Care System Consolidation.

This study sought to define and analyze rates of futile trauma transfers (FTTs) after the consolidation of two rural level 1 trauma centers into one. Data was extracted from the regional trauma registry for a period of 5 years (2017-2022) for all trauma patients transferred into our level 1 trauma center (n = 3369). An FTT was defined as a transfer that (1) received no major interventions and (2) died or was discharged to a hospice facility within 72 hours. Out of the 3369 transfer patients analyzed during the 33-month pre-consolidation and 33-month post-consolidation periods, 34 patients met the criteria of an FTT within the transfer-to-discharge window. The pre-consolidation category contained 12, and the post-consolidation category contained 22. Chi-square analysis indicated no significant difference in FTT rate between categories. Furthermore, the post-consolidation FTT rate of 1.1% remained consistent with the estimated national average of 1.5%.

Optimal timing for an accelerated interim futility analysis incorporating real world data.

BACKGROUND: Randomized controlled trials include interim monitoring guidelines to stop early for safety, efficacy, or futility. Futility monitoring facilitates re-allocation of limited resources. However, conventional methods for interim futility monitoring require a trial to accrue nearly half of the outcome data to make a reliable early stopping decision, limiting its benefit. As early stopping for futility will not inflate type-I error, these analyses are an appealing venue for incorporating external data to improve efficiency. METHODS: We propose a Bayesian approach to futility monitoring leveraging real world data using Semi-Supervised MIXture Multi-source Exchangeability Models, which accounts for both measured and unmeasured differences between data sources. We implement futility monitoring using predictive probabilities and investigate the optimal timing with respect to the expected sample size under the null hypothesis. Because we only incorporate external data during the interim futility analysis the proposed design is not limited by type-I error inflation. RESULTS: When the external and trial data are exchangeable, the proposed method provides a roughly 70 person reduction in expected sample size under the null. Under scenarios where exchangeability does not hold, our approach still provides a 10-20 person reduction in expected sample size under the null with about 80% power. CONCLUSIONS: External data borrowing in interim futility monitoring is a promising venue to improve trial efficiency without type-I error inflation. Approaches that are acceptable to regulatory authorities and leverage the complementary strengths of real world and trial data are vital to more efficiently allocate limited resources amongst clinical trials.

Portuguese Advance Directives-a twist against futility? A cross sectional study.

BACKGROUND: Advance Directive documents allow citizens to choose the treatments they want for end-of-life care without considering therapeutic futility. OBJECTIVES: To analyze patients' and caregivers' answers to Advance Directives and understand their expectations regarding their decisions. DESIGN AND SETTING: This study analyzed participants' answers to a previously published trial, conceived to test the document's efficacy as a communication tool. METHODS: Sixty palliative patients and 60 caregivers (n = 120) registered their preferences in the Advance Directive document and expressed their expectations regarding whether to receive the chosen treatments. RESULTS: In the patient and caregiver groups, 30% and 23.3% wanted to receive cardiorespiratory resuscitation; 23.3% and 25% wanted to receive artificial organ support; and 40% and 35% chose to receive artificial feeding and hydration, respectively. The participants ignored the concept of therapeutic futility and expected to receive invasive treatments. The concept of therapeutic futility should be addressed and discussed with both the patients and caregivers. Legal Advanced Directive documents should be made clear to reduce misinterpretations and potential legal conflicts. CONCLUSION: The authors suggest that all citizens should be clarified regarding the futility concept before filling out the Advance Directives and propose a grammatical change in the document, replacing the phrase "Health Care to Receive / Not to Receive" with the sentence "Health Care to Accept / Refuse" so that patients cannot demand treatments, but instead accept or refuse the proposed therapeutic plans. TRIAL REGISTRATION: ClinicalTrials.gov ID NCT05090072. URL: https://clinicaltrials.gov/ct2/show/NCT05090072.

Optimal futility stopping boundaries for binary endpoints.

BACKGROUND: Group sequential designs incorporating the option to stop for futility at the time point of an interim analysis can save time and resources. Thereby, the choice of the futility boundary importantly impacts the design's resulting performance characteristics, including the power and probability to correctly or wrongly stop for futility. Several authors contributed to the topic of selecting good futility boundaries. For binary endpoints, Simon's designs (Control Clin Trials 10:1-10, 1989) are commonly used two-stage designs for single-arm phase II studies incorporating futility stopping. However, Simon's optimal design frequently yields an undesirably high probability of falsely declaring futility after the first stage, and in Simon's minimax design often a high proportion of the planned sample size is already evaluated at the interim analysis leaving only limited benefit in case of an early stop. METHODS: This work focuses on the optimality criteria introduced by Schüler et al. (BMC Med Res Methodol 17:119, 2017) and extends their approach to binary endpoints in single-arm phase II studies. An algorithm for deriving optimized futility boundaries is introduced, and the performance of study designs implementing this concept of optimal futility boundaries is compared to the common Simon's minimax and optimal designs, as well as modified versions of these designs by Kim et al. (Oncotarget 10:4255-61, 2019). RESULTS: The introduced optimized futility boundaries aim to maximize the probability of correctly stopping for futility in case of small or opposite effects while also setting constraints on the time point of the interim analysis, the power loss, and the probability of stopping the study wrongly, i.e. stopping the study even though the treatment effect shows promise. Overall, the operating characteristics, such as maximum sample size and expected sample size, are comparable to those of the classical and modified Simon's designs and sometimes better. Unlike Simon's designs, which have binding stopping rules, the optimized futility boundaries proposed here are not adjusted to exhaust the full targeted nominal significance level and are thus still valid for non-binding applications. CONCLUSIONS: The choice of the futility boundary and the time point of the interim analysis have a major impact on the properties of the study design. Therefore, they should be thoroughly investigated at the planning stage. The introduced method of selecting optimal futility boundaries provides a more flexible alternative to Simon's designs with non-binding stopping rules. The probability of wrongly stopping for futility is minimized and the optimized futility boundaries don't exhibit the unfavorable properties of an undesirably high probability of falsely declaring futility or a high proportion of the planned sample evaluated at the interim time point.

Factors associated with limitation of life support: Post-ICU mortality case study of a tertiary hospital.

BACKGROUND: Life-sustaining treatment limitation (LSV) is the medical act of withdrawing or not initiating measures that are considered futile in a patient's specific situation. LSV in critically ill patients remains a difficult topic to study, due to the multitude of factors that condition it. OBJECTIVE: To determine factors related to LSV in ICU in cases of post-ICU in-hospital mortality, as well as factors associated with survival after discharge from ICU. DESIGN: Retrospective longitudinal study. AMBIT: Intensive care unit of a tertiary hospital. PATIENTS: People who died in the hospitalization ward after ICU treatment between January 2014 and December 2019. INTERVENTIONS: None. This is an observational study. VARIABLES OF INTEREST: Age, sex, probability of death, type of admission, LSV in ICU, oncological disease, dependence, invasive mechanical ventilation, emergency hemodialysis, transfusion of blood products, nosocomial infection (NI), pre-ICU, intra-ICU and post-ICU stays. RESULTS: Of 114 patients who died outside the ICU, 49 had LSV registered in the ICU (42.98%). Age and stay prior to ICU admission were positively associated with LSV (OR 1,03 and 1,08, respectively). Patients without LSV had a higher post-ICU stay, while it was lower for male patients. CONCLUSIONS: Our results support that LSV established within the ICU can avoid complications commonly associated with unnecessary prolongation of stay, such as NI.

Systemic biomarker associated with poor outcome after futile reperfusion.

BACKGROUND: Successful recanalization does not lead to complete tissue reperfusion in a considerable percentage of ischemic stroke patients. This study aimed to identify biomarkers associated with futile recanalization. Leukoaraiosis predicts poor outcomes of this phenomenon. Soluble tumour necrosis factor-like weak inducer of apoptosis (sTWEAK), which is associated with leukoaraiosis degrees, could be a potential biomarker. METHODS: This study includes two cohorts of ischemic stroke patients in a multicentre retrospective observational study. Effective reperfusion, defined as a reduction of ≥8 points in the National Institutes of Health Stroke Scale (NIHSS) within the first 24 h, was used as a clinical marker of effective reperfusion. RESULTS: In the first cohort study, female sex, age, and high NIHSS at admission (44.7% vs. 81.1%, 71.3 ± 13.7 vs. 81.1 ± 6.7; 16 [13, 21] vs. 23 [17, 28] respectively; p < .0001) were confirmed as predictors of futile recanalization. ROC curve analysis showed that leukocyte levels (sensitivity of 99%, specificity of 55%) and sTWEAK level (sensitivity of 92%, specificity of 88%) can discriminate between poor and good outcomes. Both biomarkers simultaneously are higher associated with outcome after effective reperfusion (OR: 2.17; CI 95% 1.63-4.19; p < .0001) than individually (leukocytes OR: 1.38; CI 95% 1.00-1.64, p = .042; sTWEAK OR: 1.00; C I95% 1.00-1.01, p = .019). These results were validated using a second cohort, where leukocytes and sTWEAK showed a sensitivity of 100% and specificity of 66.7% and 75% respectively. CONCLUSIONS: Leukocyte and sTWEAK could be biomarkers of reperfusion failure and subsequent poor outcomes. Further studies will be necessary to explore its role in reperfusion processes.

Design and monitoring of clinical trials with an interim analysis and a negative binomial endpoint.

There are very rich publications devoted to group sequential design, adaptive design and trial monitoring for continuous, binary and time to event endpoints. Many authors also discuss fixed design, blinded sample size re-estimation design and group sequential design for studies with a negative binomial outcome. Nonetheless, literature is sparse in adaptive design for a trial with a negative binomial endpoint. The features of such an endpoint in a flexible trial design setting remains inadequately understood. In this research, we seek to bridge this knowledge gap by offering a thorough examination of utilizing data components from a two-stage adaptive design for unblinded conditional power calculation and corresponding sample size re-estimation. We also provide expression for calculating the probability of meeting the futility criterion to determine the appropriate timing for the interim analysis. To evaluate the performance of the design, we conduct simulations to assess its operation characteristics. Finally, we provide a helpful and illustrative example to demonstrate the practical applications of the methods.