Abstract TMP85: Predictive Score of Hemorrhagic Transformation in Patients Not Submitted to Reperfusion Therapies - PROpHET

Stroke ◽  
2020 ◽  
Vol 51 (Suppl_1) ◽  
Author(s):  
Joao B Andrade ◽  
Gisele S Silva ◽  
Jay P Mohr ◽  
Joao J Carvalho ◽  
Luisa Franciscatto ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Validation Cohort ◽  
Characteristic Curve ◽  
Reperfusion Therapy ◽  
Predictive Score ◽  
Brier Score ◽  
Multivariable Logistic Regression Model ◽  
Derivation Cohort ◽  
Digital Version

Objective: To create an accurate and user-friendly pr edictive sc o re for he morrhagic t ransformation in patients not submitted to reperfusion therapies (PROpHET). Methods: We created a multivariable logistic regression model to assess the prediction of Hemorrhage Transformation (HT) for acute ischemic strokes not treated with reperfusion therapy. One point was assigned for each of gender, cardio-aortic embolism, hyperdense middle cerebral artery sign, leukoaraiosis, hyperglycemia, 2 points for ASPECTS ≤7, and -3 points for lacunar syndrome. Acute ischemic stroke patients admitted to the Fortaleza Comprehensive Stroke Center in Brazil from 2015 to 2017 were randomly selected to the derivation cohort. The validation cohort included similar, but not randomized, cases from 5 Brazilian and one American Comprehensive Stroke Centers. Symptomatic cases were defined as NIHSS ≥4 at 24 hours after the event. Results from the derivation and validation cohorts were assessed with the area under the receiver operating characteristic curve (AUC-ROC). Results: From 2,432 of acute ischemic stroke screened in Fortaleza, 448 were prospectively selected for the derivation cohort and a 7-day follow-up. From 1,847 not selected, 577 underwent reperfusion therapy, 734 were excluded due to inadequate imaging or refusal of consent, and 538 whose data were obtained retrospectively and were selected only for the validation cohort. A score ≥3 had 78% sensitivity and 75% specificity, AUC-ROC 0.82 for all cases of HT, Hosmer-Lemeshow 0.85, Brier Score 0.1, and AUC-ROC 0.83 for those with symptomatic HT. An AUC-ROC of 0.84 was found for the validation cohort of 1,910 from all 6 centers, and a score ≥3 was found in 65% of patients with HT against 11.3% of those without HT. In comparison with 8 published predictive scores of HT, PROpHET was the most accurate (p < 0.01). Conclusions: PROpHET offers a tool simple, quick and easy-to-perform to estimate risk stratification of HT in patients not submitted to RT. A digital version of PROpHET is available in www.score-prophet.com Classification of evidence: This study provides Class I evidence from prospective data acquisition.

Abstract 164: Outcome Prediction After Endovascular Reperfusion Therapy For Acute Ischemic Stroke: The SNARL Score

Stroke ◽  
2013 ◽  
Vol 44 (suppl_1) ◽  
Author(s):  
Shyam Prabhakaran ◽  
Kevin N Sheth ◽  
John B Terry ◽  
Raul G Nogueira ◽  
Anat Horev ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Outcome Prediction ◽  
Validation Cohort ◽  
Reperfusion Therapy ◽  
Anterior Circulation ◽  
Derivation Cohort ◽  
Good Outcome ◽  
C Statistic ◽  
Pre Treatment

Background: Tools to predict outcome after endovascular reperfusion therapy (ERT) for acute ischemic stroke (AIS) have previously included only pre-treatment variables. We sought to derive and validate an outcome prediction score based on readily available pre-treatment and treatment factors. Methods: The derivation cohort consisted of 516 patients with anterior circulation AIS from 9 centers from September 2009-July 2011. The validation cohort consisted of 110 patients with anterior circulation AIS from the Penumbra Pivotal Trial. Multivariable logistic regression identified predictors of good outcome, defined as a modified Rankin Score (mRS) of < 2, in the derivation sample; model beta coefficients were used to assign point scores. Discrimination was tested using C-statistics. We then validated the score in the Penumbra cohort and performed calibration (predicted versus observed good outcome) in both cohorts. Results: Good outcome at 3 months was noted in 189 (36.8%) patients in the derivation cohort. The independent predictors of good outcome were A ge (2 pts: <60; 1 pt: 60-79; 0 pts: >79), N IHSS score (4 pts: 0-10; 2 pts: 11-20; 0 pts: > 20), L ocation of clot (2 pts: M2; 1 pt: M1; 0 pts: ICA), R ecanalization (5 pts: TICI 2 or 3), and S ymptomatic hemorrhage (2 pts: none, HT1-2, or PH1; 0 pts: PH2). The outcome (SNARL) score demonstrated good discrimination in the derivation cohort (C-statistic 0.78, 95% CI 0.72-0.78) and validation cohort (C-statistic 0.74, 95% CI 0.64-0.84). There was excellent calibration in each cohort (Figure). Conclusions: The SNARL score is a validated tool to determine the probability of functional recovery among AIS treated with endovascular reperfusion strategies. Unlike previous scores that did not include treatment factors such as successful recanalization or hemorrhagic complications, our score can be applied to patients after treatment and may provide guidance to physicians, patients, and families about expected functional outcome.

scholarly journals A novel biomarker-based prognostic score in acute ischemic stroke

Neurology ◽  
2019 ◽  
Vol 92 (13) ◽  
pp. e1517-e1525 ◽  
Author(s):  
Gian Marco De Marchis ◽  
Theresa Dankowski ◽  
Inke R. König ◽  
Joachim Fladt ◽  
Felix Fluri ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Validation Cohort ◽  
Characteristic Curve ◽  
Prognostic Score ◽  
Unfavorable Outcome ◽  
University Hospital ◽  
Derivation Cohort ◽  
The University ◽  
Better Than

ObjectivesTo derive and externally validate a copeptin-based parsimonious score to predict unfavorable outcome 3 months after an acute ischemic stroke (AIS).MethodsThe derivation cohort consisted of patients with AIS enrolled prospectively at the University Hospital Basel, Switzerland. The validation cohort was prospectively enrolled after the derivation cohort at the University Hospital of Bern and University Hospital Basel, Switzerland, as well as Frankfurt a.M., Germany. The score components were copeptin levels, age, NIH Stroke Scale, and recanalization therapy (CoRisk score). Copeptin levels were measured in plasma drawn within 24 hours of AIS and before any recanalization therapy. The primary outcome of disability and death at 3 months was defined as modified Rankin Scale score of 3 to 6.ResultsOverall, 1,102 patients were included in the analysis; the derivation cohort contributed 319 patients, and the validation cohort contributed 783. An unfavorable outcome was observed among 436 patients (40%). For the 3-month prediction of disability and death, the CoRisk score was well calibrated in the validation cohort, for which the area under the receiver operating characteristic curve was 0.819 (95% confidence interval [CI] 0.787–0.849). The calibrated CoRisk score correctly classified 75% of patients (95% CI 72–78). The net reclassification index between the calibrated CoRisk scores with and without copeptin was 46% (95% CI 32–60).ConclusionsThe biomarker-based CoRisk score for the prediction of disability and death was externally validated, was well calibrated, and performed better than the same score without copeptin.ClinicalTrials.gov identifierNCT00390962 (derivation cohort) and NCT00878813 (validation cohort).

scholarly journals Individualized predictions of early isolated distal deep vein thrombosis in patients with acute ischemic stroke: a retrospective study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Hao-Ran Cheng ◽  
Gui-Qian Huang ◽  
Zi-Qian Wu ◽  
Yue-Min Wu ◽  
Gang-Qiang Lin ◽  
...  
Keyword(s):  
Deep Vein Thrombosis ◽  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Validation Cohort ◽  
Characteristic Curve ◽  
Multivariate Logistic Regression Analysis ◽  
Training Cohort ◽  
Vein Thrombosis ◽  
Distal Deep Vein Thrombosis ◽  
Deep Vein

Abstract Background Although isolated distal deep vein thrombosis (IDDVT) is a clinical complication for acute ischemic stroke (AIS) patients, very few clinicians value it and few methods can predict early IDDVT. This study aimed to establish and validate an individualized predictive nomogram for the risk of early IDDVT in AIS patients. Methods This study enrolled 647 consecutive AIS patients who were randomly divided into a training cohort (n = 431) and a validation cohort (n = 216). Based on logistic analyses in training cohort, a nomogram was constructed to predict early IDDVT. The nomogram was then validated using area under the receiver operating characteristic curve (AUROC) and calibration plots. Results The multivariate logistic regression analysis revealed that age, gender, lower limb paralysis, current pneumonia, atrial fibrillation and malignant tumor were independent risk factors of early IDDVT; these variables were integrated to construct the nomogram. Calibration plots revealed acceptable agreement between the predicted and actual IDDVT probabilities in both the training and validation cohorts. The nomogram had AUROC values of 0.767 (95% CI: 0.742–0.806) and 0.820 (95% CI: 0.762–0.869) in the training and validation cohorts, respectively. Additionally, in the validation cohort, the AUROC of the nomogram was higher than those of the other scores for predicting IDDVT. Conclusions The present nomogram provides clinicians with a novel and easy-to-use tool for the prediction of the individualized risk of IDDVT in the early stages of AIS, which would be helpful to initiate imaging examination and interventions timely.

scholarly journals Simple Scoring Tool to Estimate Risk of Hospitalization and Mortality in Ambulatory and Emergency Department Patients with COVID-19

2021 ◽  
Author(s):  
Brandon J. Webb ◽  
Nicholas M. Levin ◽  
Nancy Grisel ◽  
Samuel M. Brown ◽  
Ithan D. Peltan ◽  
...  
Keyword(s):  
Monoclonal Antibodies ◽  
High Risk ◽  
Validation Cohort ◽  
Characteristic Curve ◽  
External Validation ◽  
Receiver Operator Characteristic Curve ◽  
Multivariable Logistic Regression Model ◽  
White Race ◽  
Derivation Cohort ◽  
Scoring Tool

AbstractBackgroundAccurate methods of identifying patients with COVID-19 who are at high risk of poor outcomes has become especially important with the advent of limited-availability therapies such as monoclonal antibodies. Here we describe development and validation of a simple but accurate scoring tool to classify risk of hospitalization and mortality.MethodsAll consecutive patients testing positive for SARS-CoV-2 from March 25-October 1, 2020 within the Intermountain Healthcare system were included. The cohort was randomly divided into 70% derivation and 30% validation cohorts. A multivariable logistic regression model was fitted for 14-day hospitalization. The optimal model was then adapted to a simple, probabilistic score and applied to the validation cohort and evaluated for prediction of hospitalization and 28-day mortality.Results22,816 patients were included; mean age was 40 years, 50.1% were female and 44% identified as non-white race or Hispanic/Latinx ethnicity. 6.2% required hospitalization and 0.4% died. Criteria in the simple model included: age (0.5 points per decade); high-risk comorbidities (2 points each): diabetes mellitus, severe immunocompromised status and obesity (body mass index≥30); non-white race/Hispanic or Latinx ethnicity (2 points), and 1 point each for: male sex, dyspnea, hypertension, coronary artery disease, cardiac arrythmia, congestive heart failure, chronic kidney disease, chronic pulmonary disease, chronic liver disease, cerebrovascular disease, and chronic neurologic disease. In the derivation cohort (n=16,030) area under the receiver-operator characteristic curve (AUROC) was 0.82 (95% CI 0.81-0.84) for hospitalization and 0.91 (0.83-0.94) for 28-day mortality; in the validation cohort (n=6,786) AUROC for hospitalization was 0.8 (CI 0.78-0.82) and for mortality 0.8 (CI 0.69-0.9).ConclusionA prediction score based on widely available patient attributes accurately risk stratifies patients with COVID-19 at the time of testing. Applications include patient selection for therapies targeted at preventing disease progression in non-hospitalized patients, including monoclonal antibodies. External validation in independent healthcare environments is needed.

scholarly journals Development and validation of the RCOS prognostic index: a bedside multivariable logistic regression model to predict hypoxaemia or death in patients with of SARS-CoV-2 infection

2021 ◽  
Author(s):  
Gerardo Alvarez-Uria ◽  
Sumanth Gandra ◽  
Venkata R Gurram ◽  
Raghu P Reddy ◽  
Manoranjan Midde ◽  
...  
Keyword(s):  
Validation Cohort ◽  
Characteristic Curve ◽  
Prognostic Score ◽  
Prognostic Index ◽  
Prognostic Scores ◽  
Prognostic Models ◽  
Brier Score ◽  
Multivariable Logistic Regression Model ◽  
Neutrophil Lymphocyte Ratio ◽  
Development And Validation

Previous COVID-19 prognostic models have been developed in hospital settings, and are not applicable to COVID-19 cases in the general population. There is an urgent need for prognostic scores aimed to identify patients at high risk of complications at the time of COVID-19 diagnosis. The RDT COVID-19 Observational Study (RCOS) collected clinical data from patients with COVID-19 admitted regardless of the severity of their symptoms in a general hospital in India. We aimed to develop and validate a simple bedside prognostic score to predict the risk of hypoxaemia or death. 4035 patients were included in the development cohort and 2046 in the validation cohort. The primary outcome occurred in 961 (23.8%) and 548 (26.8%) patients in the development and validation cohorts, respectively. The final model included 12 variables: age, systolic blood pressure, heart rate, respiratory rate, aspartate transaminase, lactate dehydrogenase, urea, C-reactive protein, sodium, lymphocyte count, neutrophil count and neutrophil/lymphocyte ratio. In the validation cohort, the area under the receiver operating characteristic curve (AUROCC) was 0.907 (95% CI, 0.892-0.922) and the Brier Score was 0.098. The decision curve analysis showed good clinical utility in hypothetical scenarios where admission of patients was decided according to the prognostic index. When the prognostic index was used to predict mortality in the validation cohort, the AUROCC was 0.947 (95% CI, 0.925-0.97) and the Brier score was 0.0188. If our results are validated in other settings, the RCOS prognostic index could help improve the decision making in the current COVID-19 pandemic, especially in resource limited-settings.

scholarly journals Predicting in-hospital mortality from Coronavirus Disease 2019: A simple validated app for clinical use

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0245281
Author(s):  
Bianca Magro ◽  
Valentina Zuccaro ◽  
Luca Novelli ◽  
Lorenzo Zileri ◽  
Ciro Celsa ◽  
...  
Keyword(s):  
Hospital Mortality ◽  
Validation Cohort ◽  
Characteristic Curve ◽  
Prediction Rule ◽  
Clinical Prediction Rule ◽  
Brier Score ◽  
Clinical Prediction ◽  
Duration Of Symptoms ◽  
Derivation Cohort ◽  
Independent Risk Factors

Backgrounds Validated tools for predicting individual in-hospital mortality of COVID-19 are lacking. We aimed to develop and to validate a simple clinical prediction rule for early identification of in-hospital mortality of patients with COVID-19. Methods and findings We enrolled 2191 consecutive hospitalized patients with COVID-19 from three Italian dedicated units (derivation cohort: 1810 consecutive patients from Bergamo and Pavia units; validation cohort: 381 consecutive patients from Rome unit). The outcome was in-hospital mortality. Fine and Gray competing risks multivariate model (with discharge as a competing event) was used to develop a prediction rule for in-hospital mortality. Discrimination and calibration were assessed by the area under the receiver operating characteristic curve (AUC) and by Brier score in both the derivation and validation cohorts. Seven variables were independent risk factors for in-hospital mortality: age (Hazard Ratio [HR] 1.08, 95% Confidence Interval [CI] 1.07–1.09), male sex (HR 1.62, 95%CI 1.30–2.00), duration of symptoms before hospital admission <10 days (HR 1.72, 95%CI 1.39–2.12), diabetes (HR 1.21, 95%CI 1.02–1.45), coronary heart disease (HR 1.40 95% CI 1.09–1.80), chronic liver disease (HR 1.78, 95%CI 1.16–2.72), and lactate dehydrogenase levels at admission (HR 1.0003, 95%CI 1.0002–1.0005). The AUC was 0.822 (95%CI 0.722–0.922) in the derivation cohort and 0.820 (95%CI 0.724–0.920) in the validation cohort with good calibration. The prediction rule is freely available as a web-app (COVID-CALC: https://sites.google.com/community.unipa.it/covid-19riskpredictions/c19-rp). Conclusions A validated simple clinical prediction rule can promptly and accurately assess the risk for in-hospital mortality, improving triage and the management of patients with COVID-19.

scholarly journals Serum Occludin Level Combined With NIHSS Score Predicts Hemorrhage Transformation in Ischemic Stroke Patients With Reperfusion

2021 ◽  
Vol 15 ◽  
Author(s):  
Shuhua Yuan ◽  
Weili Li ◽  
Chengbei Hou ◽  
Huining Kang ◽  
Qingfeng Ma ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Characteristic Curve ◽  
Multivariate Logistic Regression Analysis ◽  
Clinical Information ◽  
Reperfusion Therapy ◽  
Hemorrhagic Transformation ◽  
Stroke Severity ◽  
Stroke Patients ◽  
Nihss Score

Hemorrhagic transformation (HT) is a severe complication following acute ischemic stroke, particularly with reperfusion interventions, leading to poor prognosis. Serum occludin level is related with blood brain barrier disruption, and the National Institute of Health stroke scale (NIHSS) score reflects stroke severity. We investigated whether the two covariates are independently associated with HT and their combination can improve the accuracy of HT prediction in ischemic stroke patients with reperfusion therapy. Seventy-six patients were screened from the established database of acute ischemic stroke in our previous study, which contains all clinical information, including serum occludin levels, baseline NIHSS score, and hemorrhagic events. Multivariate logistic regression analysis showed that serum occludin level (OR = 4.969, 95% CI: 2.069–11.935, p &lt; 0.001) and baseline NIHSS score (OR = 1.293, 95% CI 1.079–1.550, p = 0.005) were independent risk factors of HT after adjusting for potential confounders. Compared with non-HT patients, HT patients had higher baseline NIHSS score [12 (10.5–18.0) versus 6 (4–12), p = 0.003] and serum occludin level (5.47 ± 1.25 versus 3.81 ± 1.19, p &lt; 0.001). Moreover, receiver operating characteristic curve based on leave-one-out cross-validation showed that the combination of serum occludin level and NIHSS score significantly improved the accuracy of predicting HT (0.919, 95% CI 0.857–0.982, p &lt; 0.001). These findings suggest that the combination of two methods may provide a better tool for HT prediction in acute ischemic stroke patients with reperfusion therapy.

scholarly journals Multiple-Factor Analyses of Futile Recanalization in Acute Ischemic Stroke Patients Treated With Mechanical Thrombectomy

2021 ◽  
Vol 12 ◽  
Author(s):  
Hui Pan ◽  
Changchun Lin ◽  
Lina Chen ◽  
Yuan Qiao ◽  
Peisheng Huang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Ischemic Stroke ◽  
Prediction Model ◽  
Acute Ischemic Stroke ◽  
Collateral Circulation ◽  
Mechanical Thrombectomy ◽  
Validation Cohort ◽  
Regression Analyses ◽  
Nihss Score ◽  
Derivation Cohort

Background and Purpose: Acute ischemic stroke (AIS) is a serious threat to the life and health of middle-aged and elderly people. Mechanical thrombectomy offers the advantages of rapid recanalization, but the response of patients to this treatment varies greatly. This study investigated the risk factors for futile recanalization in AIS patients after thrombectomy through multivariate analyses.Methods: A retrospective study was conducted in AIS patients with anterior circulation occlusion from a derivation cohort and a validation cohort who underwent thrombectomy and reperfusion defined as a modified Thrombolysis in Cerebral Infarction (mTICI) score of 2b/3. Using the modified Rankin Scale (mRS) at 90 days after the operation, the patients were divided into two groups, the meaningful recanalization group (mRS ≤ 2), and the futile recanalization group (mRS &gt; 2). Multivariate logistic regression analyses were performed, and the receiver operating characteristic (ROC) curve was used to construct a risk prediction model for futile recanalization. The performance of prediction model was evaluated on the validation cohort.Results: A total of 140 patients in the derivation cohort were enrolled, 46 patients in the meaningful recanalization group and 94 patients in the futile recanalization group. The two groups were significantly different in age, preoperative National Institute of Health Stroke Scale (NIHSS) score, and collateral circulation ASITN/SIR grade (P &lt; 0.05). In multivariate regression analyses, patients' age ≥ 71, NIHSS ≥ 12, and ASITN/SIR ≤ 3 were risk factors for futile recanalization. Hence, an ANA (Age-NIHSS-ASITN/SIR) score scale consisting of age, NIHSS score, and ASITN/SIR grade factors can effectively predict the risk for futile recanalization (area under curve 0.75, 95% CI 0.67–0.83, specificity 67.4%, and sensitivity 73.4%). The proportion of patients with futile recanalization in ANA groups 0, 1, 2, and 3 were 21.05, 56.76, 79.03, and 90.91%, respectively. Furthermore, ANA score scale had also a good performance for predicting futile recanalization on the validation cohort.Conclusions: Old age, high baseline NIHSS, and poor collateral circulation are risk factors for futile recanalization in AIS patients treated with thrombectomy. An ANA score that considers age, NIHSS, and collateral ASITN/SIR can effectively predict the risk for futile recanalization. Further studies with a larger sample size are needed to validate the prognostic value of this combined score for futile recanalization.

scholarly journals Development and Validation of a Predictive Model for Spontaneous Hemorrhagic Transformation After Ischemic Stroke

2021 ◽  
Vol 12 ◽  
Author(s):  
Chenchen Wei ◽  
Junfeng Liu ◽  
Wen Guo ◽  
Yuxi Jin ◽  
Quhong Song ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Characteristic Curve ◽  
External Validation ◽  
Predictive Performance ◽  
Bleeding Risk ◽  
Reperfusion Therapy ◽  
Hemorrhagic Transformation ◽  
Baseline Risk ◽  
Anterior Circulation ◽  
Derivation Cohort

Background: Hemorrhagic transformation (HT) after reperfusion therapy for acute ischemic stroke (AIS) has been well studied; however, there is scarce research focusing on spontaneous HT (sHT). Spontaneous HT is no less important with a relatively high incidence and could be associated with neurological worsening. We aimed to develop and validate a simple and practical model to predict sHT after AIS (SHAIS) and compared the predictive value of the SHAIS score against the models of post-Reperfusion HT for sHT.Methods: Patients with AIS admitted within 24 h of onset were prospectively screened to develop and validate the SHAIS score. The primary outcome was sHT during hospitalization (within 30 days after onset), and the secondary outcomes were symptomatic sHT and parenchymal hematoma (PH). Clinical information, laboratory, and neuroimaging data were screened to construct the SHAIS score. We selected six commonly used scales for predicting HT after reperfusion therapy and compared their predictive ability for sHT with the SHAIS score using Delong's test.Results: The derivation cohort included 539 patients (mean age, 68.1 years; men, 61.4%), of whom 91 (16.9%) patients developed sHT with 25.3% (23/91) being symptomatic sHT and 62.6% (57/91) being PH. Five variables (atrial fibrillation, NIHSS score ≥ 10, hypodensity &gt; 1/3 of middle cerebral artery territory, hyperdense artery sign, and anterior circulation infarction) composed the SHAIS score, which ranged from 0 to 11 points. The area under the receiver-operating characteristic curve (AUC) was 0.86 (95% CI 0.82–0.91, p &lt; 0.001) for the overall sHT, 0.85 (95% CI 0.76–0.92, p &lt; 0.001) for symptomatic sHT, and 0.89 (95% CI 0.85–0.94, p &lt; 0.001) for PH. No evidence of miscalibration of the SHAIS score was found to predict the overall sHT (p = 0.19), symptomatic sHT (p = 0.44), and PH (p = 0.22). The internal (n = 245) and external validation cohorts (n = 200) depicted similar predictive performance compared to the derivation cohort. The SHAIS score had a higher AUC to predict sHT than any of the six pre-Existing models (p &lt; 0.05).Conclusions: The SHAIS score provides an easy-to-use model to predict sHT, which could help providers with decision-making about treatments with high bleeding risk, and to counsel patients and families on the baseline risk of HT, aligning expectations with probable outcomes.

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