Prospective Validation Research Articles

A Retrospective Comparison of Multiple Approaches to Anatomically Informed Contact Selection in Subthalamic Deep Brain Stimulation for Parkinson's Disease.

Conventional deep brain stimulation (DBS) programming via trial-and-error warrants improvement to ensure swift achievement of optimal outcomes. The definition of a sweet spot for subthalamic DBS in Parkinson's disease (PD-STN-DBS) may offer such advancement. This investigation examines the association of long-term motor outcomes with contact selection during monopolar review and different strategies for anatomically informed contact selection in a retrospective real-life cohort of PD-STN-DBS. We compared contact selection based on a monopolar review (MPR) to multiple anatomically informed contact selection strategies in a cohort of 28 PD patients with STN-DBS. We employed a commercial software package for contact selection based on visual assessment of individual anatomy following two predefined strategies and two algorithmic approaches with automatic targeting of either the sensorimotor STN or our previously published sweet spot. Similarity indices between chronic stimulation and contact selection strategies were correlated to motor outcomes at 12 months follow-up. Lateralized motor outcomes of chronic DBS were correlated to the similarity between chronic stimulation and visual contact selection targeting the dorsal part of the posterior STN (rho = 0.36, p = 0.007). Similar relationships could not be established for MPR or any of the other investigated strategies. Our data demonstrates that a visual contact selection following a predefined strategy can be linked to beneficial long-term motor outcomes in PD-STN-DBS. Since similar correlations could not be observed for the other approaches to anatomically informed contact selection, we conclude that clear definitions and prospective validation of any approach to imaging-based DBS-programming is warranted.

313 Development and Validation of a Scoring System for Hemorrhage Risk in Brain Arteriovenous Malformations

INTRODUCTION: The dilemma between natural rupture risk and adverse outcomes of intervention is of major concern for patients with unruptured arteriovenous malformations (AVMs). The existing risk score for AVM rupture includes factors that are controversial and lacks prospective validation. METHODS: This prognostic study developed a prediction model derived from a single-center cohort (derivation cohort) and validated in a multicenter external validation cohort and a conservative treatment validation cohort. Patients were recruited from a nationwide multicenter prospective collaboration registry in China. A total of 4135 patients were enrolled. 3962 patients were included (3585 in the derivation cohort and 377 in the multicenter external validation cohort); 1028 patients from the derivation cohort who had time-to-event data and prerupture imaging results were included in the conservative treatment validation cohort. RESULTS: Four risk factors were used to develop the scoring system: ventricular system involvement, venous aneurysm, deep location, and exclusively deep drainage (VALE). The VALE scoring system performed well in all 3 cohorts, with areas under the receiver operating characteristic curve of 0.77 (95% CI, 0.75-0.78) in the derivation cohort, 0.85 (95% CI, 0.81-0.89) in the multicenter external validation cohort, and 0.73 (95% CI, 0.65-0.81) in the conservative treatment validation cohort. The 10-year hemorrhage-free rate was 95.5% (95% CI, 87.1%-100%) in the low-risk group, 92.8% (95% CI, 88.8%-97.0%) in the moderate-risk group, and 75.8% (95% CI, 65.1%-88.3%) in the high-risk group; the model discrimination was significant when comparing these rates between the high-risk group and the low- and moderate-risk groups (p < .001 for both comparisons). CONCLUSIONS: In this prognostic study, the VALE scoring system was developed to distinguish rupture risk among patients with AVMs. The stratification of unruptured AVMs may enable patients with low risk of rupture to avoid unnecessary interventions.

Abstract 7669: Use of computational pathology to predict biochemical recurrence in prostate cancer (Pca) (Rp) patients following radical prostatectomy: Evaluation in low decipher risk category

Abstract Background: Current tools for predicting prostate cancer biochemical recurrence (BCR) after RP often depend on parameters determined by pathologists, such as tumor grade, which is known to vary between reviewers. Genomic risk classifiers provide useful information but require substantial tissue, are costly, and are not commonly accessible in many medical centers. The study presents an artificial intelligence (AI) model for automated risk assessment of biochemical recurrence (BCR) by analyzing nuclear morphologic patterns from Pca archival H&E slides. Nuclear morphometric features are essential for disease diagnosis and gaining insights into cellular functions. Specifically, we sought to evaluate whether the computational pathology approach could provide additional granular risk stratification within the low-risk group identified by Decipher. Methods: The study employed two cohorts from the University of Pennsylvania D1 and D2. D1 comprised 180 patients, while D2 comprised 175 patients, including 63 individuals from the Decipher low-risk group. None of the patients in either cohort had received any pre-operative treatments. Within the Decipher cohort, a tumor segmentation model was created using the U-net deep learning network. The HoVer-Net model was used to both segment and classify nuclei. The feature extracted included a combination of nuclear texture and quantitative measurements of the spatial arrangement of nuclei. The eleven most significant features, selected via the least absolute shrinkage and selection operator (LASSO), were utilized to build a Cox regression model for predicting the risk of BCR in D1. In D2, patients were classified as either low-risk or high-risk for BCR based on the median risk score derived from D1. Results: Patients from D1 identified as high risk by the AI model experienced shorter survival, with a median BCR time of 27 months, in contrast to 55 months for those classified as low risk. The AI model demonstrated significant prognostic value for BCR in both D1 (HR = 6.82, 95% confidence interval [CI]: 3.54-13.12, p < 0.0019), and D2 (HR = 2.72, 95% CI: 1.53-4.82, p < 0.0032). Furthermore, the model effectively stratified the Decipher low-risk group in D2 into distinct low and high-risk categories (HR = 4.52, 95% confidence interval [CI]: 1.70-12.05, p < 0.0085). The most prognostic features identified included five related to nuclei shape diversity (Minor Axis Length, Eccentricity, Equivalent Diameter, Solidity, and Circularity) in conjunction with nine nuclear texture features. Conclusions: The AI model's findings suggest that a digital image-based prognostic classifier for prostate cancer could serve as an alternative or complementary method to molecular-based companion risk tests. Additional, independent multi-site and prospective validation of these findings are warranted. Citation Format: Kamal Hammouda, Tilak Pathak, Tuomas Mirtti, Priti Lal, Shilpa Gupta, Anant Madabhushi. Use of computational pathology to predict biochemical recurrence in prostate cancer (Pca) (Rp) patients following radical prostatectomy: Evaluation in low decipher risk category [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7669.

Abstract 1167: Prospective analysis of pretreatment circulating CD4 memory T cell features associated with severe immunotherapy toxicity

Abstract Introduction: Immune checkpoint inhibitors (ICIs) have revolutionized melanoma treatment, significantly improving outcomes. Nevertheless, too many melanoma patients undergoing ICI treatment experience severe and often debilitating immune-related adverse events (irAEs). In a retrospective melanoma cohort treated with ICIs, we previously found that elevated baseline levels of circulating CD4 effector memory T (TEM) cells were associated with the development of severe irAEs, irrespective of the affected organ system (Lozano et al., Nature Medicine, 2022). Here, we present data from an extended prospective validation cohort from 47 patients across two academic medical centers. Methods: Of 47 patients, 26 received combination anti-PD1/anti-CTLA4 ICIs, 3 combination anti-PD1/anti-LAG3 ICIs, 17 anti-PD1 ICI, and 1 anti-CTLA4 ICI. Peripheral blood mononuclear cells (PBMCs) were prepared from pretreatment (Cycle 1 Day 1) blood samples obtained from all patients. We performed mass cytometry (CyTOF) on PBMCs using a 38-marker panel to deeply profile immune subsets. We further leveraged bulk RNA-seq from 24 patients to (i) deconvolve CD4 memory T cell abundance using CIBERSORTx and (ii) determine T cell receptor (TCR) diversity using Shannon entropy following MiXCR clonotype assembly. irAEs were graded on a 5-point scale using the Common Terminology Criteria for Adverse Events v5. Results: After treatment initiation, 14 patients experienced severe irAEs (grade 3+) at a median of 12.1 weeks (range 4-54), including 4 with life-threatening (grade 4) irAEs. CyTOF revealed elevated circulating CD4 TEM cells in patients that developed severe irAEs (P<0.0001; AUC=0.87), including those treated with combination ICIs (P=0.0001; AUC=0.89). We further observed a continuous relationship between increasing CD4 TEM levels and irAE grade (P=3e-6, Jonckheere-Terpstra test). We then combined CD4 memory T cell abundance and TCR diversity derived from bulk RNA-seq on 24 of these patients using logistic regression (trained on data from Lozano et al.) to yield a composite model score. The pretreatment composite model score was strongly associated with severe irAE development (P=0.0009, AUC=0.88), and in a median split, identified patients with shorter time to severe irAE development (3 months vs. not reached; P=0.006; HR=10.5). Notably, composite model features and the overall score had no significant association with immunotherapy benefit. Conclusion: In this prospective study, both circulating CD4 TEM levels determined by CyTOF and a composite model score measured by RNA-seq were strongly associated with severe irAE development, independent of response status. These findings hold promise for future pretreatment risk stratification of ICI therapy. Citation Format: Abul Usmani, Noah Earland, Wubing Zhang, Peter K. Harris, Antonietta Bacchiocchi, Chloe M. Sachs, Aishwarya Nene, David Y. Chen, Mario Sznol, Ruth Halaban, Aaron M. Newman, Aadel A. Chaudhuri. Prospective analysis of pretreatment circulating CD4 memory T cell features associated with severe immunotherapy toxicity [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1167.

Testing the validity of three acute care assessment tools for assessing residents' performance during in situ simulation: the ACAT-SimSit study.

The assessment of technical and nontechnical skills in emergency medicine requires reliable and usable tools. Three Acute Care Assessment Tools (ACATs) have been developed to assess medical learners in their management of cardiac arrest (ACAT-CA), coma (ACAT-coma) and acute respiratory failure (ACAT-ARF). This study aims to analyze the reliability and usability of the three ACATs when used for in situ (bedside) simulation. This prospective multicenter validation study tested ACATs using interprofessional in situ simulations in seven emergency departments and invited training residents to participate in them. Each session was rated by two independent raters using ACAT. Intraclass correlation coefficients (ICC) were used to assess interrater reliability, and Cronbach's alpha coefficient was used to assess internal consistency for each ACAT. The correlation between ACATs' scores and the learners' level of performance was also assessed. Finally, a questionnaire and two focus groups were used to assess the usability of the ACATs. A total of 104 in situ simulation sessions, including 85 residents, were evaluated by 37 raters. The ICC for ACAT-CA, ACAT-coma and ACAT-ARF were 0.95 [95% confidence interval (CI), 0.93-0.98], 0.89 (95% CI, 0.77-0.95) and 0.92 (95%CI 0.83-0.96), respectively. The Cronbach's alphas were 0.79, 0.80 and 0.73, respectively. The ACAT-CA and ARF showed good construct validity, as third-year residents obtained significantly higher scores than first-year residents ( P < 0.001; P < 0.019). The raters supported the usability of the tools, even though they expressed concerns regarding the use of simulations in a summative way. This study reported that the three ACATs showed good external validity and usability.

Postoperative haemoglobin and anaemia-associated ischaemic events after major noncardiac surgery: A sex-stratified cohort study

Study objectiveTo determine the sex-specific associations between postoperative haemoglobin and mortality or complications reflecting ischaemia or inadequate oxygen supply after major noncardiac surgery. DesignA retrospective cohort study with prospective validation. SettingA large university hospital health system in China. PatientsMen and women undergoing elective major noncardiac surgery. Interventions and measurementsThe primary exposure was nadir haemoglobin within 48 h after surgery. The outcome of interest was a composite of postoperative mortality or ischaemic events including myocardial injury, acute kidney injury and stroke within hospitalisation. Main resultsThe study included 26,049 patients (15,757 men and 10,292 women). Low postoperative haemoglobin was a strong predictor of the composite outcome in both sexes, with the risk progressively increasing as the nadir haemoglobin concentration dropped below 130 g l−1 in men and 120 g l−1 in women (adjusted odds ratio [OR] 1.43, 95% CI 1.37–1.50 in men, and OR 1.45, 95% CI 1.35–1.55 in women, per 10 g l−1 decrease in postoperative nadir haemoglobin). Above these sex-specific thresholds, the change of nadir haemoglobin was no longer associated with odds of the composite outcome in either men or women. There was no significant interaction between patient sex and the association between postoperative haemoglobin and the composite outcome (Pinteraction = 0.673). Validation in an external prospective cohort (n = 2120) with systematic postoperative troponin and creatinine measurement confirmed our findings. ConclusionsPostoperative haemoglobin levels following major noncardiac surgery were nonlinearly associated with ischaemic complications or mortality, without any clinically important interaction with patient sex.

Prospective external validation of the FluScore risk score for influenza in outpatients.

Testing for influenza in patients with acute lower respiratory tract infection (LRTI) is common and in some cases is performed for all patients with LRTI. A more selective approach to testing could be more efficient. We used data from two prospective studies in the US primary and urgent care settings that enrolled patients with acute LRTI or influenza-like illness. Data were collected in the 2016, 2019, 2021, and 2022 flu seasons. All patients underwent polymerase chain reaction (PCR) testing for influenza and the FluScore was calculated based on patient-reported symptoms at their initial visit. The probability of influenza in each risk group was reported, as well as stratum-specific likelihood ratios (SSLRs) for each risk level. The prevalence of influenza within risk groups varied based on overall differences in flu seasons and populations. However, the FluScore exhibited consistent performance across various seasons and populations based on the SSLRs. The FluScore had a consistent SSLR range of 0.20 to 0.23 for the low-risk group, 0.63 to 0.99 for the moderate-risk group, and 1.46 to 1.67 for the high-risk group. The diagnostic odds ratio based on the midpoints of these ranges was 7.25. The FluScore could streamline patient categorization, identifying patients who could be exempted from testing, while identifying candidates for rapid influenza tests. This has the potential to be more efficient than a "one size fits all" test strategy, as it strategically targets the use of tests on patients most likely to benefit. It is potentially usable in a telehealth setting.

Prospective validation of the EASL management algorithm for acute kidney injury in cirrhosis

The management of acute kidney injury (AKI) in cirrhosis is challenging. The EASL guidelines proposed an algorithm for the management of AKI, but this has never been validated. We aimed to prospectively evaluate this algorithm in clinical practice. We performed a prospective cohort study in consecutive hospitalized patients with cirrhosis and AKI. The EASL management algorithm includes identification/treatment of precipitating factors, 2-day albumin infusion in patients with AKI ≥stage 1B, and treatment with terlipressin in patients with hepatorenal syndrome (HRS-AKI). The primary outcome was treatment response, which included both full and partial response. Secondary outcomes were survival and adverse events associated with terlipressin therapy. A total of 202 AKI episodes in 139 patients were included. Overall treatment response was 80%, while renal replacement therapy was required in only 8%. Response to albumin infusion was achieved in one-third of episodes. Of patients not responding to albumin, most (74%) did not meet the diagnostic criteria of HRS-AKI, with acute tubular necrosis (ATN) being the most common phenotype. The response rate in patients not meeting the criteria for HRS-AKI was 70%. Only 30 patients met the diagnostic criteria for HRS-AKI, and their response rate to terlipressin was 61%. Median time from AKI diagnosis to terlipressin initiation was only 2.5 days. While uNGAL (urinary neutrophil gelatinase-associated lipocalin) could differentiate ATN from other phenotypes (AUROC 0.78), it did not predict response to therapy in HRS-AKI. Ninety-day transplant-free survival was negatively associated with MELD-Na, ATN and HRS-AKI as well as uNGAL. Three patients treated with terlipressin developed pulmonary edema. The application of the EASL AKI algorithm is associated with very good response rates and does not significantly delay initiation of terlipressin therapy. The occurrence of acute kidney injury (AKI) in patients with cirrhosis is associated with poor short-term mortality. Improving its rapid identification and prompt management was the focus of the recently proposed EASL AKI algorithm. This is the first prospective study demonstrating that high AKI response rates are achieved with the use of this algorithm, which includes identification of AKI, treatment of precipitating factors, a 2-day albumin challenge in patients with AKI ≥1B, and supportive therapy in patients with persistent AKI not meeting HRS-AKI criteria or terlipressin with albumin in those with HRS-AKI. These findings support the use of this algorithm in clinical practice.

Patient metabolic profile defined by liver and muscle 18F-FDG PET avidity is independently associated with overall survival in gastric cancer.

PET-CT-based patient metabolic profiling is a novel concept to incorporate patient-specific metabolism into gastric cancer care. Staging PET-CTs, demographics, and clinicopathologic variables of gastric cancer patients were obtained from a prospectively maintained institutional database. PET-CT avidity was measured in tumor, liver, spleen, four paired muscles, and two paired fat areas in each patient. The liver to rectus femoris (LRF) ratio was defined as the ratio of SUVmean of liver to the average SUVmean of the bilateral rectus femoris muscles. Kaplan-Meier and Cox-proportional hazards models were used to identify the impact of LRF ratio on OS. Two hundred and one patients with distal gastroesophageal (48%) or gastric (52%) adenocarcinoma were included. Median age was 65years, and 146 (73%) were male. On univariate analysis, rectus femoris PET-CT avidity and LRF ratio were significantly associated with overall survival (p < 0.05). LRF ratio was significantly higher in males, early-stage cancer, patients with an ECOG 0 or 1 performance status, patients with albumin > 3.5mg/dL, and those with moderately differentiated tumor histology. In multivariable regression, gastric cancer stage, albumin, and LRF ratio were significant independent predictors of overall survival (LRF ratio HR = 0.73 (0.56-0.96); p = 0.024). Survival curves showed that the prognostic impact of LRF was associated with metastatic gastric cancer (p = 0.009). Elevated LRF ratio, a patient-specific PET-CT-based metabolic parameter, was independently associated with an improvement in OS in patients with metastatic gastric cancer. With prospective validation, LRF ratio may be a useful, host-specific metabolic parameter for prognostication in gastric cancer.

NUM-score: A clinical-analytical model for personalised imaging after urinary tract infections.

To identify predictive variables and construct a predictive model along with a decision algorithm to identify nephrourological malformations (NUM) in children with febrile urinary tract infections (fUTI), enhancing the efficiency of imaging diagnostics. We performed a retrospective study of patients aged <16 years with fUTI at the Emergency Department with subsequent microbiological confirmation between 2014 and 2020. The follow-up period was at least 2 years. Patients were categorised into two groups: 'NUM' with previously known nephrourological anomalies or those diagnosed during the follow-up and 'Non-NUM' group. Out of 836 eligible patients, 26.8% had underlying NUMs. The study identified six key risk factors: recurrent UTIs, non-Escherichia coli infection, moderate acute kidney injury, procalcitonin levels >2 μg/L, age <3 months at the first UTI and fUTIs beyond 24 months. These risk factors were used to develop a predictive model with an 80.7% accuracy rate and elaborate a NUM-score classifying patients into low, moderate and high-risk groups, with a 10%, 35% and 93% prevalence of NUM. We propose an algorithm for approaching imaging tests following a fUTI. Our predictive score may help physicians decide about imaging tests. However, prospective validation of the model will be necessary before its application in daily clinical practice.

Development of a clinical scoring system to make a presumptive diagnosis of Kyasanur Forest Disease: a case-control study from South India.

Kyasanur Forest Disease (KFD) is a viral haemorrhagic fever endemic in South India. Based on clinical presentation alone, it is challenging to distinguish KFD from other febrile illnesses in the region. The study aimed to develop a clinical scoring system for early presumptive diagnosis of KFD. This retrospective case-control study included microbiologically diagnosed KFD patients (n=186) with other undifferentiated febrile illnesses as controls (n=203). The clinical and laboratory features between cases and controls were compared. A logistic regression analysis included those variables found to be significantly associated with KFD on univariate analysis. The adjusted odds ratio for the significant variables was calculated and converted into logarithmic scales. These numbers were rounded off to the nearest integer to find the score assigned to each variable. A receiver operating characteristics curve was created to find the best cut-off for the scoring system that predicted the diagnosis of KFD. A total of 186 anonymised cases and 203 anonymised controls were recruited from the records for this study. Myalgia, headache, lymphadenopathy, bleeding manifestations, Central Nervous System (CNS) involvement, raised haematocrit, leukopenia, and raised transaminases were more common in patients with KFD. Except for lymphadenopathy and raised transaminases, all the other variables were independent predictors of making a diagnosis of KFD. Since raised transaminases tended towards significance, it was included in the scoring system with other independent predictors. A scoring system was created with a maximum score of 12. The receiver operating characteristic curve showed an Area Under Curve of 0.912 (95%CI: 0.88-0.94). A score of 4 or more was found to have a sensitivity and specificity of 83% and 87%, respectively. The presence of specific features should alert primary care physicians working in endemic areas about the possibility of KFD. This diagnostic scoring system can be used to make a presumptive diagnosis of KFD after undergoing a prospective validation study.

Serologic Detection of Hepatocellular Carcinoma: Application of Machine Learning and Implications for Diagnostic Models.

The gender, age, lens culinaris agglutinin-reactive fraction of alphafetoprotein, alphafetoprotein, des-gamma-carboxyprothrombin (GALAD) score is a biomarker-based statistical model for the serologic diagnosis of hepatocellular carcinoma (HCC) that has been developed and validated using the case-control approach with a view to early detection. Performance has, however, been suboptimal in the first prospective studies which better reflect the real-world situation. In this article, we report the application of machine learning to a large, prospectively accrued, HCC surveillance data set. Models were built on a cohort of 3,473 patients with chronic liver disease within a rigorous surveillance program between 1998 and 2014, during which 459 patients with HCC were detected. Two random forest (RF) models were trained. The first RF model uses the same variables as the original GALAD model (GALAD-RF); the second is based on routinely available clinical and laboratory features (RF-practical). For comparison, we evaluated a logistic regression GALAD model trained on this longitudinal prospective data set (termed GALAD-Ogaki). Models were evaluated using a repetitive cross-validation approach with the metrics averaged over 100 independent runs. As judged by area under the receiver operator curve (AUROC) and F1 score, the GALAD RF model significantly outperformed the original GALAD model. The RF-practical model also outperformed the original GALAD model in terms of both AUROC and F1 score, and both models outperformed the individual biomarkers. An online web application that implemented the GALAD-RF and RF-practical models is presented. RF-based models improve on the diagnostic performance of the original GALAD model in the setting of a standard HCC surveillance program. Further prospective validation studies are warranted using these models and could be expanded to offer prediction of risk of HCC development over defined periods of time.

Efficacy of fresh frozen plasma transfusion in decompensated cirrhosis patients with coagulopathy admitted to ICU: a retrospective cohort study from MIMIC-IV database

We aimed to explore the association between FFP transfusion and outcomes of DC patients with significant coagulopathy. A total of 693 DC patients with significant coagulopathy were analyzed with 233 patients per group after propensity score matching (PSM). Patients who received FFP transfusion were matched with those receiving conventional therapy via PSM. Regression analysis showed FFP transfusion had no benefit in 30-day (HR: 1.08, 95% CI 0.83–1.4), 90-day (HR: 1.03, 95% CI 0.80–1.31) and in-hospital(HR: 1.30, 95% CI 0.90–1.89) mortality, associated with increased risk of liver failure (OR: 3.00, 95% CI 1.78–5.07), kidney failure (OR: 1.90, 95% CI 1.13–3.18), coagulation failure (OR: 2.55, 95% CI 1.52–4.27), respiratory failure (OR: 1.76, 95% CI 1.15–2.69), and circulatory failure (OR: 2.15, 95% CI 1.27–3.64), and even associated with prolonged the LOS ICU (β: 2.61, 95% CI 1.59–3.62) and LOS hospital (β: 6.59, 95% CI 2.62–10.57). In sensitivity analysis, multivariate analysis (HR: 1.09, 95%CI 0.86, 1.38), IPTW (HR: 1.11, 95%CI 0.95–1.29) and CAPS (HR: 1.09, 95% CI 0.86–1.38) showed FFP transfusion had no beneficial effect on the 30-day mortality. Smooth curve fitting demonstrated the risk of liver failure, kidney failure and circulatory failure increased by 3%, 2% and 2% respectively, for each 1 ml/kg increase in FFP transfusion. We found there was no significant difference of CLIF-SOFA and MELD score between the two group on day 0, 3, 7, 14. Compared with the conventional group, INR, APTT, and TBIL in the FFP transfusion group significantly increased, while PaO2/FiO2 significantly decreased within 14 days. In conclusion, FFP transfusion had no beneficial effect on the 30-day, 90-day, in-hospital mortality, was associated with prolonged the LOS ICU and LOS hospital, and the increased risk of liver failure, kidney failure, coagulation failure, respiratory failure and circulatory failure events. However, large, multi-center, randomized controlled trials, prospective cohort studies and external validation are still needed to verify the efficacy of FFP transfusion in the future.

HEBREW TRANSLATION AND VALIDATION OF THE CATQUEST- 9SF QUESTIONNAIRE

Aims: The purpose of this study is to translate and validate a Hebrew version of the Catquest-9SF questionnaire, an efficient patient-reported outcome measure (PROM), for the assessment of cataract surgery outcome Methods: This is a prospective questionnaire validation study. The Catquest-9SFquestionnaire was translated from English to Hebrew following a standard procedure. The study population included candidates for cataract extraction surgery in both eyes. They were 21 years of age or older, Hebrew-speaking, and had Internet access. They completed the questionnaire in 2 stages, once before the surgery on the first eye and again 4-6 weeks following the surgery on the second eye. A Rasch analysis was performed to validate the Hebrew version of the questionnaire, and a sensitivity analysis was performed for the patients who completed both the preoperative and postoperative questionnaire. Results: Of the 133 patients included in the study, 125 completed the first questionnaire, and 24 also completed the second questionnaire by the time of study closure. The Hebrew translation of Catquest-9SF demonstrated 1-dimensionality, ordered response categories and good person separation. The sensitivity analysis on the 24 paired Catquest-9SF questionnaires showed good effect size (1.85). Conclusions: The Hebrew versions of the Catquest-9SF questionnaire is valid and reliable with psychometric properties of good quality and good effect size. It is applicable to Hebrew-speaking cataract patients for clinical assessment and research purposes

Deep learning model based on multi-lesion and time series CT images for predicting the benefits from anti-HER2 targeted therapy in stage IV gastric cancer.

To develop and validate a deep learning model based on multi-lesion and time series CT images in predicting overall survival (OS) in patients with stage IV gastric cancer (GC) receiving anti-HER2 targeted therapy. A total of 207 patients were enrolled in this multicenter study, with 137 patients for retrospective training and internal validation, 33 patients for prospective validation, and 37 patients for external validation. All patients received anti-HER2 targeted therapy and underwent pre- and post-treatment CT scans (baseline and at least one follow-up). The proposed deep learning model evaluated the multiple lesions in time series CT images to predict risk probabilities. We further evaluated and validated the risk score of the nomogram combining a two-follow-up lesion-based deep learning model (LDLM-2F), tumor markers, and clinical information for predicting the benefits from treatment (Nomo-LDLM-2F). In the internal validation and prospective cohorts, the one-year AUCs for Nomo-LDLM-2F using the time series medical images and tumor markers were 0.894 (0.728-1.000) and 0.809 (0.561-1.000), respectively. In the external validation cohort, the one-year AUC of Nomo-LDLM-2F without tumor markers was 0.771 (0.510-1.000). Patients with a low Nomo-LDLM-2F score derived survival benefits from anti-HER2 targeted therapy significantly compared to those with a high Nomo-LDLM-2F score (all p < 0.05). The Nomo-LDLM-2F score derived from multi-lesion and time series CT images holds promise for the effective readout of OS probability in patients with HER2-positive stage IV GC receiving anti-HER2 therapy. The deep learning model using baseline and early follow-up CT images aims to predict OS in patients with stage IV gastric cancer receiving anti-HER2 targeted therapy. This model highlights the spatiotemporal heterogeneity of stage IV GC, assisting clinicians in the early evaluation of the efficacy of anti-HER2 therapy. • Multi-lesion and time series model revealed the spatiotemporal heterogeneity in anti-HER2 therapy. • The Nomo-LDLM-2F score was a valuable prognostic marker for anti-HER2 therapy. • CT-based deep learning model incorporating time-series tumor markers improved performance.

Deep-learning-based reconstruction of undersampled MRI to reduce scan times: a multicentre, retrospective, cohort study

The extended acquisition times required for MRI limit its availability in resource-constrained settings. Consequently, accelerating MRI by undersampling k-space data, which is necessary to reconstruct an image, has been a long-standing but important challenge. We aimed to develop a deep convolutional neural network (dCNN) optimisation method for MRI reconstruction and to reduce scan times and evaluate its effect on image quality and accuracy of oncological imaging biomarkers. In this multicentre, retrospective, cohort study, MRI data from patients with glioblastoma treated at Heidelberg University Hospital (775 patients and 775 examinations) and from the phase 2 CORE trial (260 patients, 1083 examinations, and 58 institutions) and the phase 3 CENTRIC trial (505 patients, 3147 examinations, and 139 institutions) were used to develop, train, and test dCNN for reconstructing MRI from highly undersampled single-coil k-space data with various acceleration rates (R=2, 4, 6, 8, 10, and 15). Independent testing was performed with MRIs from the phase 2/3 EORTC-26101 trial (528 patients with glioblastoma, 1974 examinations, and 32 institutions). The similarity between undersampled dCNN-reconstructed and original MRIs was quantified with various image quality metrics, including structural similarity index measure (SSIM) and the accuracy of undersampled dCNN-reconstructed MRI on downstream radiological assessment of imaging biomarkers in oncology (automated artificial intelligence-based quantification of tumour burden and treatment response) was performed in the EORTC-26101 test dataset. The public NYU Langone Health fastMRI brain test dataset (558 patients and 558 examinations) was used to validate the generalisability and robustness of the dCNN for reconstructing MRIs from available multi-coil (parallel imaging) k-space data. In the EORTC-26101 test dataset, the median SSIM of undersampled dCNN-reconstructed MRI ranged from 0·88 to 0·99 across different acceleration rates, with 0·92 (95% CI 0·92-0·93) for 10-times acceleration (R=10). The 10-times undersampled dCNN-reconstructed MRI yielded excellent agreement with original MRI when assessing volumes of contrast-enhancing tumour (median DICE for spatial agreement of 0·89 [95% CI 0·88 to 0·89]; median volume difference of 0·01 cm3 [95% CI 0·00 to 0·03] equalling 0·21%; p=0·0036 for equivalence) or non-enhancing tumour or oedema (median DICE of 0·94 [95% CI 0·94 to 0·95]; median volume difference of -0·79 cm3 [95% CI -0·87 to -0·72] equalling -1·77%; p=0·023 for equivalence) in the EORTC-26101 test dataset. Automated volumetric tumour response assessment in the EORTC-26101 test dataset yielded an identical median time to progression of 4·27 months (95% CI 4·14 to 4·57) when using 10-times-undersampled dCNN-reconstructed or original MRI (log-rank p=0·80) and agreement in the time to progression in 374 (95·2%) of 393 patients with data. The dCNN generalised well to the fastMRI brain dataset, with significant improvements in the median SSIM when using multi-coil compared with single-coil k-space data (p<0·0001). Deep-learning-based reconstruction of undersampled MRI allows for a substantial reduction of scan times, with a 10-times acceleration demonstrating excellent image quality while preserving the accuracy of derived imaging biomarkers for the assessment of oncological treatment response. Our developments are available as open source software and hold considerable promise for increasing the accessibility to MRI, pending further prospective validation. Deutsche Forschungsgemeinschaft (German Research Foundation) and an Else Kröner Clinician Scientist Endowed Professorship by the Else Kröner Fresenius Foundation.

Accuracy of Smartwatch Pulse Oximetry Measurements in Hospitalized Patients With Coronavirus Disease 2019

ObjectiveTo assess the ability of two commercially available smartwatches to accurately detect clinically significant hypoxia in patients hospitalized with COVID-19. Patients and MethodsA prospective multi-center validation study was performed from 1st November 2021 to 31st August 2022 assessing the Apple Watch Series 7 and Withings ScanWatch inbuilt pulse oximetry, against simultaneous ward-based oximetry as the reference standard. Patients hospitalized with active COVID-19 infection not requiring intensive care admission were recruited. Results750 smartwatch pulse oximetry measurements and 400 ward oximetry readings were successfully obtained from 200 patients (male 54%, age 66 ± 18 years). For detection of clinically significant hypoxia the Apple Watch had a sensitivity and specificity of 34.8% and 97.5% respectively, with a positive predictive value (PPV) of 78.1% and negative predictive value (NPV) of 85.6%. The Withings ScanWatch had a sensitivity and specificity of 68.5% and 80.8% respectively with a PPV of 44.7% and NPV of 91.9%. Overall accuracy was 84.9% for the Apple Watch and 78.5% for the Withings ScanWatch. Spearman rank correlation coefficients demonstrated moderate correlation to ward-based PPG (Apple: rs = 0.61; Withings: rs = 0.51, both p < 0.01). ConclusionWhile smartwatches are able to provide SpO2 readings, their overall accuracy may not be sufficient to replace standard PPG technology in detecting hypoxia in COVID-19 patients.

Radiomics for residual tumour detection and prognosis in newly diagnosed glioblastoma based on postoperative [11C] methionine PET and T1c-w MRI

Personalized treatment strategies based on non-invasive biomarkers have potential to improve patient management in patients with newly diagnosed glioblastoma (GBM). The residual tumour burden after surgery in GBM patients is a prognostic imaging biomarker. However, in clinical patient management, its assessment is a manual and time-consuming process that is at risk of inter-rater variability. Furthermore, the prediction of patient outcome prior to radiotherapy may identify patient subgroups that could benefit from escalated radiotherapy doses. Therefore, in this study, we investigate the capabilities of traditional radiomics and 3D convolutional neural networks for automatic detection of the residual tumour status and to prognosticate time-to-recurrence (TTR) and overall survival (OS) in GBM using postoperative [11C] methionine positron emission tomography (MET-PET) and gadolinium-enhanced T1-w magnetic resonance imaging (MRI). On the independent test data, the 3D-DenseNet model based on MET-PET achieved the best performance for residual tumour detection, while the logistic regression model with conventional radiomics features performed best for T1c-w MRI (AUC: MET-PET 0.95, T1c-w MRI 0.78). For the prognosis of TTR and OS, the 3D-DenseNet model based on MET-PET integrated with age and MGMT status achieved the best performance (Concordance-Index: TTR 0.68, OS 0.65). In conclusion, we showed that both deep-learning and conventional radiomics have potential value for supporting image-based assessment and prognosis in GBM. After prospective validation, these models may be considered for treatment personalization.

A Score to Predict the Clinical Usefulness of Therapeutic Drug Monitoring: Application to Oral Molecular Targeted Therapies in Cancer.

Therapeutic drug monitoring (TDM) involves measuring and interpreting drug concentrations in biological fluids to adjust drug dosages. In onco-hematology, TDM guidelines for oral molecular targeted therapies (oMTTs) are varied. This study evaluates a quantitative approach with a score to predict the clinical usefulness of TDM for oMTTs. We identified key parameters for an oMTT's suitability for TDM from standard TDM recommendations. We gathered oMTT pharmacological data, which covered exposure variability (considering pharmacokinetic (PK) impact of food and proton pump inhibitors), technical intricacy (PK linearity and active metabolites), efficacy (exposure-response relationship), and safety (maximum tolerated dose, and exposure-safety relationship). To assess the validity and the relevance of the score and define relevant thresholds, we evaluated molecules with prospective validation or strong recommendations for TDM, both in oncology and in other fields. By September 1, 2021, the US Food and Drug Administration (FDA) approved 67 oMTTs for onco-hematological indications. Scores ranged from 15 (acalabrutinib) to 80 (sunitinib) with an average of 48.3 and a standard deviation of 15.6. Top scorers included sunitinib, sorafenib, cabozantinib, nilotinib, and abemaciclib. Based on scores, drugs were categorized into low (< 40), intermediate (≥ 40 and < 60), and high (≥ 60) relevance for TDM. Notably, negative controls generally scored around or under 40, whereas positive controls had a high score across different indications. In this work, we propose a quantitative and reproducible score to compare the potential usefulness of TDM for oMTTs. Future guidelines should prioritize the TDM for molecules with the highest score.

Prospective Validation of the Spinal Cord Shape Classification System in the Prediction of Intraoperative Neuromonitoring Data Loss: Assessing the Risk of Spinal Cord Data Loss During Spinal Deformity Correction.

The Spinal Cord Shape Classification System (SCSCS) class has been associated with spinal cord monitoring data loss during spinal deformity surgery. The objective of the current study was to prospectively validate the SCSCS as a predictor of spinal cord monitoring data loss during spinal deformity surgery. A prospective cohort study of consecutive patients who were undergoing primary deformity surgery at a single institution from 2018 to 2023 and whose major curve was in the spinal cord region was undertaken. Spinal cord morphology at the apex of the major curve on preoperative axial T2-weighted magnetic resonance imaging was used to categorize patients into 3 spinal cord shape types based on the SCSCS. The primary outcome was intraoperative neuromonitoring (IONM) data loss related to spinal cord dysfunction. Demographics and surgical and radiographic variables were compared between patients with IONM data loss and those without loss. Predictors of IONM loss were determined using bivariate and multivariable logistic regression analyses. A total of 256 patients (168 adult, 88 pediatric) were included and were separated into 3 SCSCS types: 110 (43.0%) with Type I, 105 (41.0%) with Type II, and 41 (16.0%) with Type III. IONM loss was observed in 30 (11.7%) of the 256 patients, including 7 (6.4%) of 110 with SCSCS Type I, 7 (6.7%) of 105 with Type II, and 16 (39.0%) of 41 with Type III. IONM loss was associated with SCSCS Type III, the preoperative deformity angular ratio, performance of 3-column osteotomies, greater operative time, greater transfusion volume, and greater postoperative sagittal corrections. SCSCS type was the strongest independent predictor of IONM data loss. SCSCS Type III had the greatest odds of IONM loss (odds ratio [OR] = 6.68, 95% confidence interval [CI] = 2.45 to 18.23 compared with Types I and II combined). The overall predictive performance with respect to IONM loss (area under the receiver operating characteristic curve = 0.827) was considered excellent. This prospective cohort study of patients undergoing spinal deformity correction confirmed that patients with a Type-III spinal cord shape had greater odds of IONM loss. Inclusion of the SCSCS in preoperative risk stratification and intraoperative management of spinal deformity corrective surgery is recommended. Prognostic Level II . See Instructions for Authors for a complete description of levels of evidence.