Survival after resection of malignant peripheral nerve sheath tumors: Introducing and validating a novel type-specific prognostic model.

Background: This study aimed to assess the performance of currently available risk calculators in a cohort of patients with malignant peripheral nerve sheath tumors (MPNST) and to create an MPNST-specific prognostic model including type-specific predictors for overall survival (OS). Methods: This is a retrospective multicenter cohort study of patients with MPNST from 11 secondary or tertiary centers in The Netherlands, Italy and the United States of America. All patients diagnosed with primary MPNST who underwent macroscopically complete surgical resection from 2000 to 2019 were included in this study. A multivariable Cox proportional hazard model for OS was estimated with prespecified predictors (age, grade, size, NF-1 status, triton status, depth, tumor location, and surgical margin). Model performance was assessed for the Sarculator and PERSARC calculators by examining discrimination (C-index) and calibration (calibration plots and observed-expected statistic; O/E-statistic). Internal-external cross-validation by different regions was performed to evaluate the generalizability of the model. Results: A total of 507 patients with primary MPNSTs were included from 11 centers in 7 regions. During follow-up (median 8.7 years), 211 patients died. The C-index was 0.60 (95% CI 0.53-0.67) for both Sarculator and PERSARC. The MPNST-specific model had a pooled C-index of 0.69 (95%CI 0.65-0.73) at validation, with adequate discrimination and calibration across regions. Conclusions: The MPNST-specific MONACO model can be used to predict 3-, 5-, and 10-year OS in patients with primary MPNST who underwent macroscopically complete surgical resection. Further validation may refine the model to inform patients and physicians on prognosis and support them in shared decision-making.

Artificial Intelligence-based Segmentation of Residual Pancreatic Cancer in Resection Specimens Following Neoadjuvant Treatment (ISGPP-2): International Improvement and Validation Study.

Neoadjuvant therapy (NAT) has become routine in patients with borderline resectable pancreatic cancer. Pathologists examine pancreatic cancer resection specimens to evaluate the effect of NAT. However, an automated scoring system to objectively quantify residual pancreatic cancer (RPC) is currently lacking. Herein, we developed and validated the first automated segmentation model using artificial intelligence techniques to objectively quantify RPC. Digitized histopathological tissue slides were included from resected pancreatic cancer specimens from 14 centers in 7 countries in Europe, North America, Australia, and Asia. Four different scanner types were used: Philips (56%), Hamamatsu (27%), 3DHistech (10%), and Leica (7%). Regions of interest were annotated and classified as cancer, non-neoplastic pancreatic ducts, and others. A U-Net model was trained to detect RPC. Validation consisted of by-scanner internal-external cross-validation. Overall, 528 unique hematoxylin and eosin (H & E) slides from 528 patients were included. In the individual Philips, Hamamatsu, 3DHistech, and Leica scanner cross-validations, mean F1 scores of 0.81 (95% CI, 0.77-0.84), 0.80 (0.78-0.83), 0.76 (0.65-0.78), and 0.71 (0.65-0.78) were achieved, respectively. In the meta-analysis of the cross-validations, the mean F1 score was 0.78 (0.71-0.84). A final model was trained on the entire data set. This ISGPP model is the first segmentation model using artificial intelligence techniques to objectively quantify RPC following NAT. The internally-externally cross-validated model in this study demonstrated robust performance in detecting RPC in specimens. The ISGPP model, now made publically available, enables automated RPC segmentation and forms the basis for objective NAT response evaluation in pancreatic cancer.

Development and Validation of a Prediction Model for 1-Year Mortality in Patients With a Hematologic Malignancy Admitted to the ICU.

OBJECTIVES: To develop and validate a prediction model for 1-year mortality in patients with a hematologic malignancy acutely admitted to the ICU. DESIGN: A retrospective cohort study. SETTING: Five university hospitals in the Netherlands between 2002 and 2015. PATIENTS: A total of 1097 consecutive patients with a hematologic malignancy were acutely admitted to the ICU for at least 24 h. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We created a 13-variable model from 22 potential predictors. Key predictors included active disease, age, previous hematopoietic stem cell transplantation, mechanical ventilation, lowest platelet count, acute kidney injury, maximum heart rate, and type of malignancy. A bootstrap procedure reduced overfitting and improved the model's generalizability. This involved estimating the optimism in the initial model and shrinking the regression coefficients accordingly in the final model. We assessed performance using internal-external cross-validation by center and compared it with the Acute Physiology and Chronic Health Evaluation II model. Additionally, we evaluated clinical usefulness through decision curve analysis. The overall 1-year mortality rate observed in the study was 62% (95% CI, 59-65). Our 13-variable prediction model demonstrated acceptable calibration and discrimination at internal-external validation across centers (C-statistic 0.70; 95% CI, 0.63-0.77), outperforming the Acute Physiology and Chronic Health Evaluation II model (C-statistic 0.61; 95% CI, 0.57-0.65). Decision curve analysis indicated overall net benefit within a clinically relevant threshold probability range of 60-100% predicted 1-year mortality. CONCLUSIONS: Our newly developed 13-variable prediction model predicts 1-year mortality in hematologic malignancy patients admitted to the ICU more accurately than the Acute Physiology and Chronic Health Evaluation II model. This model may aid in shared decision-making regarding the continuation of ICU care and end-of-life considerations.

Clinical and Imaging Characteristics, Care Pathways, and Outcomes of Traumatic Epidural Hematomas: A Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury Study.

BACKGROUND AND OBJECTIVES: Guideline recommendations for surgical management of traumatic epidural hematomas (EDHs) do not directly address EDHs that co-occur with other intracranial hematomas; the relative rates of isolated vs nonisolated EDHs and guideline adherence are unknown. We describe characteristics of a contemporary cohort of patients with EDHs and identify factors influencing acute surgery. METHODS: This research was conducted within the longitudinal, observational Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury cohort study which prospectively enrolled patients with traumatic brain injury from 65 hospitals in 18 European countries from 2014 to 2017. All patients with EDH on the first scan were included. We describe clinical, imaging, management, and outcome characteristics and assess associations between site and baseline characteristics and acute EDH surgery, using regression modeling. RESULTS: In 461 patients with EDH, median age was 41 years (IQR 24-56), 76% were male, and median EDH volume was 5 cm3 (IQR 2-20). Concomitant acute subdural hematomas (ASDHs) and/or intraparenchymal hemorrhages were present in 328/461 patients (71%). Acute surgery was performed in 99/461 patients (21%), including 70/86 with EDH volume ≥30 cm3 (81%). Larger EDH volumes (odds ratio [OR] 1.19 [95% CI 1.14-1.24] per cm3 below 30 cm3), smaller ASDH volumes (OR 0.93 [95% CI 0.88-0.97] per cm3), and midline shift (OR 6.63 [95% CI 1.99-22.15]) were associated with acute surgery; between-site variation was observed (median OR 2.08 [95% CI 1.01-3.48]). Six-month Glasgow Outcome Scale-Extended scores ≥5 occurred in 289/389 patients (74%); 41/389 (11%) died. CONCLUSION: Isolated EDHs are relatively infrequent, and two-thirds of patients harbor concomitant ASDHs and/or intraparenchymal hemorrhages. EDHs ≥30 cm3 are generally evacuated early, adhering to Brain Trauma Foundation guidelines. For heterogeneous intracranial pathology, surgical decision-making is related to clinical status and overall lesion burden. Further research should examine the optimal surgical management of EDH with concomitant lesions in traumatic brain injury, to inform updated guidelines.

Machine Learning Did Not Outperform Conventional Competing Risk Modeling to Predict Revision Arthroplasty.

BACKGROUND: Estimating the risk of revision after arthroplasty could inform patient and surgeon decision-making. However, there is a lack of well-performing prediction models assisting in this task, which may be due to current conventional modeling approaches such as traditional survivorship estimators (such as Kaplan-Meier) or competing risk estimators. Recent advances in machine learning survival analysis might improve decision support tools in this setting. Therefore, this study aimed to assess the performance of machine learning compared with that of conventional modeling to predict revision after arthroplasty. QUESTION/PURPOSE: Does machine learning perform better than traditional regression models for estimating the risk of revision for patients undergoing hip or knee arthroplasty? METHODS: Eleven datasets from published studies from the Dutch Arthroplasty Register reporting on factors associated with revision or survival after partial or total knee and hip arthroplasty between 2018 and 2022 were included in our study. The 11 datasets were observational registry studies, with a sample size ranging from 3038 to 218,214 procedures. We developed a set of time-to-event models for each dataset, leading to 11 comparisons. A set of predictors (factors associated with revision surgery) was identified based on the variables that were selected in the included studies. We assessed the predictive performance of two state-of-the-art statistical time-to-event models for 1-, 2-, and 3-year follow-up: a Fine and Gray model (which models the cumulative incidence of revision) and a cause-specific Cox model (which models the hazard of revision). These were compared with a machine-learning approach (a random survival forest model, which is a decision tree-based machine-learning algorithm for time-to-event analysis). Performance was assessed according to discriminative ability (time-dependent area under the receiver operating curve), calibration (slope and intercept), and overall prediction error (scaled Brier score). Discrimination, known as the area under the receiver operating characteristic curve, measures the model's ability to distinguish patients who achieved the outcomes from those who did not and ranges from 0.5 to 1.0, with 1.0 indicating the highest discrimination score and 0.50 the lowest. Calibration plots the predicted versus the observed probabilities; a perfect plot has an intercept of 0 and a slope of 1. The Brier score calculates a composite of discrimination and calibration, with 0 indicating perfect prediction and 1 the poorest. A scaled version of the Brier score, 1 - (model Brier score/null model Brier score), can be interpreted as the amount of overall prediction error. RESULTS: Using machine learning survivorship analysis, we found no differences between the competing risks estimator and traditional regression models for patients undergoing arthroplasty in terms of discriminative ability (patients who received a revision compared with those who did not). We found no consistent differences between the validated performance (time-dependent area under the receiver operating characteristic curve) of different modeling approaches because these values ranged between -0.04 and 0.03 across the 11 datasets (the time-dependent area under the receiver operating characteristic curve of the models across 11 datasets ranged between 0.52 to 0.68). In addition, the calibration metrics and scaled Brier scores produced comparable estimates, showing no advantage of machine learning over traditional regression models. CONCLUSION: Machine learning did not outperform traditional regression models. CLINICAL RELEVANCE: Neither machine learning modeling nor traditional regression methods were sufficiently accurate in order to offer prognostic information when predicting revision arthroplasty. The benefit of these modeling approaches may be limited in this context.

Aspin: neurosurgical aspirin intervention prognostic study - perioperative continuation versus discontinuation of aspirin in lumbar spinal surgery, a randomized controlled, noninferiority trial.

RATIONALE: Aspirin is typically discontinued in cranial and spinal surgery because of the increased risk of hemorrhagic complications, but comes together with the risk of resulting in an increase of cardiac and neurologic thrombotic perioperative events. OBJECTIVE: The aim of this study is to investigate the non-inferiority of perioperative continuation of aspirin patients undergoing low complex lumbar spinal surgery, compared with the current policy of perioperative discontinuation of aspirin. STUDY DESIGN: A randomized controlled trial with two parallel groups of 277 cases (554 in total). STUDY POPULATION: Patients undergoing low complex lumbar spinal surgery and using aspirin. All patients are aged >18 years. INTERVENTION: Peri-operative continuation of aspirin. STUDY OUTCOMES: Primary study outcome: composite of the following bleeding complications: Neurological deterioration as a result of hemorrhage in the surgical area with cauda and/or nerve root compression. Post-surgical anemia with hemoglobin level lower than 5 mmol/l, requiring transfusion. Subcutaneous hematoma leading to wound leakage and pain higher than NRS=7. Major and/or minor hemorrhage in any other body system according to the definition of the International Society on Thrombosis and Haemostasis bleeding scale. Secondary study outcomes: Each of the individual components of the primary outcome Absolute mean difference in operative blood loss between the study arms Thrombo-embolic-related complications: Myocardial infarction Venous thromboembolism Stroke Arterial thromboembolism FURTHER STUDY OUTCOMES: Anticoagulant treatment satisfaction by the Anti-Clot Treatment Scale (ACTS) and general health by the Patient-Reported Outcomes Measurement Information System (PROMIS Global-10) in the pre- and postoperative phase. NATURE AND EXTENT OF THE BURDEN AND RISKS ASSOCIATED WITH PARTICIPATION, BENEFIT, AND GROUP RELATEDNESS: Participation in this study imposes no additional risk to patients. Currently, there is no consensus on whether or not aspirin should be discontinued before cranial or spinal surgery. Currently, aspirin is typically discontinued in cranial and spinal surgery, because of a potential increased risk of hemorrhagic complication. An argument not based on a clinical trial. However, this policy might delay surgical procedures or carry the risk of resulting in an increase in cardiac and neurologic thrombotic perioperative events. It is unclear if the possibility of an increase in hemorrhage-related complications outweighs the risk of an increase in cardiac and neurologic thrombotic perioperative events. Furthermore, the Data Safety Monitoring Board (DSMB) will be asked for safety analysis by monitoring the study. There are no further disadvantages to participating in this study. Outcome measurements are recorded during admission and regular outpatient visits, and thus, do not require additional visits to the hospital.

Improving shared decision-making about cancer treatment through design-based data-driven decision-support tools and redesigning care paths: an overview of the 4D PICTURE project.

Background: Patients with cancer often have to make complex decisions about treatment, with the options varying in risk profiles and effects on survival and quality of life. Moreover, inefficient care paths make it hard for patients to participate in shared decision-making. Data-driven decision-support tools have the potential to empower patients, support personalized care, improve health outcomes and promote health equity. However, decision-support tools currently seldom consider quality of life or individual preferences, and their use in clinical practice remains limited, partly because they are not well integrated in patients' care paths. Aim and objectives: The central aim of the 4D PICTURE project is to redesign patients' care paths and develop and integrate evidence-based decision-support tools to improve decision-making processes in cancer care delivery. This article presents an overview of this international, interdisciplinary project. Design methods and analysis: In co-creation with patients and other stakeholders, we will develop data-driven decision-support tools for patients with breast cancer, prostate cancer and melanoma. We will support treatment decisions by using large, high-quality datasets with state-of-the-art prognostic algorithms. We will further develop a conversation tool, the Metaphor Menu, using text mining combined with citizen science techniques and linguistics, incorporating large datasets of patient experiences, values and preferences. We will further develop a promising methodology, MetroMapping, to redesign care paths. We will evaluate MetroMapping and these integrated decision-support tools, and ensure their sustainability using the Nonadoption, Abandonment, Scale-Up, Spread, and Sustainability (NASSS) framework. We will explore the generalizability of MetroMapping and the decision-support tools for other types of cancer and across other EU member states. Ethics: Through an embedded ethics approach, we will address social and ethical issues. Discussion: Improved care paths integrating comprehensive decision-support tools have the potential to empower patients, their significant others and healthcare providers in decision-making and improve outcomes. This project will strengthen health care at the system level by improving its resilience and efficiency.

Improving the cancer patient journey and respecting personal preferences: an overview of the 4D PICTURE project The 4D PICTURE project aims to help cancer patients, their families and healthcare providers better undertstand their options. It supports their treatment and care choices, at each stage of disease, by drawing on large amounts of evidence from different types of European data. The project involves experts from many different specialist areas who are based in nine European countries. The overall aim is to improve the cancer patient journey and ensure personal preferences are respected.

Predicting Depression Risk in Patients With Cancer Using Multimodal Data: Algorithm Development Study.

BACKGROUND: Patients with cancer starting systemic treatment programs, such as chemotherapy, often develop depression. A prediction model may assist physicians and health care workers in the early identification of these vulnerable patients. OBJECTIVE: This study aimed to develop a prediction model for depression risk within the first month of cancer treatment. METHODS: We included 16,159 patients diagnosed with cancer starting chemo- or radiotherapy treatment between 2008 and 2021. Machine learning models (eg, least absolute shrinkage and selection operator [LASSO] logistic regression) and natural language processing models (Bidirectional Encoder Representations from Transformers [BERT]) were used to develop multimodal prediction models using both electronic health record data and unstructured text (patient emails and clinician notes). Model performance was assessed in an independent test set (n=5387, 33%) using area under the receiver operating characteristic curve (AUROC), calibration curves, and decision curve analysis to assess initial clinical impact use. RESULTS: Among 16,159 patients, 437 (2.7%) received a depression diagnosis within the first month of treatment. The LASSO logistic regression models based on the structured data (AUROC 0.74, 95% CI 0.71-0.78) and structured data with email classification scores (AUROC 0.74, 95% CI 0.71-0.78) had the best discriminative performance. The BERT models based on clinician notes and structured data with email classification scores had AUROCs around 0.71. The logistic regression model based on email classification scores alone performed poorly (AUROC 0.54, 95% CI 0.52-0.56), and the model based solely on clinician notes had the worst performance (AUROC 0.50, 95% CI 0.49-0.52). Calibration was good for the logistic regression models, whereas the BERT models produced overly extreme risk estimates even after recalibration. There was a small range of decision thresholds for which the best-performing model showed promising clinical effectiveness use. The risks were underestimated for female and Black patients. CONCLUSIONS: The results demonstrated the potential and limitations of machine learning and multimodal models for predicting depression risk in patients with cancer. Future research is needed to further validate these models, refine the outcome label and predictors related to mental health, and address biases across subgroups.

The Potential Value of Prehabilitation for Preventing Delirium in Elective Surgery for Aneurysms of the Abdominal Aorta.

Objective: Delirium is a common and serious postoperative complication in elderly patients undergoing abdominal aortic aneurysm (AAA) repair and is associated with a variety of adverse outcomes. Multimodal prehabilitation aims to identify and minimize potential risk factors for delirium and improve overall health. The aim of this study is to investigate the effect of multimodal prehabilitation on delirium incidence in elderly patients undergoing elective repair for AAA. Methods: A single-centre cohort analysis was performed in the Netherlands for patients aged ≥70 years, undergoing elective repair for AAA (open surgery and endovascular aortic repair). Prehabilitation was gradually introduced between 2016 and 2019 and offered as standard care from 2019. The program was constructed to optimize overall health and included delirium risk assessment, home-based tailor-made exercises by a physical therapist, nutritional optimization by a dietician, iron infusion in case of anaemia and a comprehensive geriatric assessment by a geriatrician in case of frailty. The primary outcome was incidence of delirium within 30 days after surgery. Results: A total of 81 control and 123 prehabilitation patients were included. A reduction in incidence of delirium was found (11.1% in the control group to 4.9% in the prehabilitation group), with too small numbers to reach statistical significance (p=0.09). Also, patients in the prehabilitation group had a small, non-significant decreased length of hospital stay (4 days) compared to the control group (5 days) (p=0.07). Conclusion: Although no significant differences were found, we carefully conclude that this study provides some support for implementing multimodal prehabilitation for delirium prevention in elderly patients undergoing AAA repair. Further research with larger cohorts is necessary to identify and select patients that would most benefit from prehabilitation.

Discontinuation of Anti-Tumour Necrosis Factor Therapy in Patients with Perianal Fistulizing Crohn's Disease: Individual Participant Data Meta-Analysis of 309 Patients from 12 Studies.

BACKGROUND: The risk of relapse after anti-tumour necrosis factor [TNF] therapy discontinuation in Crohn's disease patients with perianal fistulas [pCD] is unclear. We aimed to assess this risk. METHODS: A systematic literature search was conducted to identify cohort studies on the incidence of relapse following anti-TNF discontinuation in pCD patients. Individual participant data were requested from the original study cohorts. Inclusion criteria were age ≥16 years, pCD as a (co)indication for start of anti-TNF therapy, more than three doses, and remission of luminal and pCD at anti-TNF discontinuation. The primary outcome was the cumulative incidence of CD relapse using Kaplan-Meier estimates. Secondary outcomes included response to re-treatment and risk factors associated with relapse as assessed by Cox regression analysis. RESULTS: In total, 309 patients from 12 studies in ten countries were included. The median duration of anti-TNF treatment was 14 months [interquartile range 5.8-32.5]. Most patients were treated for pCD without active luminal disease [89%], received first-line anti-TNF therapy [87%], and continued immunomodulatory therapy following anti-TNF discontinuation [78%]. The overall cumulative incidence of relapse was 36% (95% confidence interval [CI] 25-48%) and 42% [95% CI 32-53%] at 1 and 2 years after anti-TNF discontinuation, respectively. Risk factors for relapse included smoking (hazard ratio [HR] 1.5 [1.0, 2.1]) and history of proctitis (HR 1.7 [1.1, 2.5]). The overall re-treatment response rate was 82%. CONCLUSIONS: This individual participant data meta-analysis, on predominantly patients with pCD without active luminal disease and first-line anti-TNF therapy, shows that over half of patients remain in remission 2 years after anti-TNF discontinuation. Therefore, anti-TNF discontinuation may be considered in this subgroup.

Prognostic models for clinical outcomes in patients with venous leg ulcers: A systematic review.

OBJECTIVE: The purpose of this review was to identify prognostic models for clinical application in patients with venous leg ulcers (VLUs). METHODS: Literature searches were conducted in Embase, Medline, Cochrane, and CINAHL databases from inception to December 22, 2021. Eligible studies reported prognostic models aimed at developing, validating, and adjusting multivariable prognostic models that include multiple prognostic factors combined, and that predicted clinical outcomes. Methodological quality was assessed using the CHARMS checklist and PROBAST short form questionnaire. RESULTS: Thirteen studies were identified, of which three were validation studies of previously published models, four reported derivation and validation of models, and the remainder reported derivation models only. There was substantial heterogeneity in the model characteristics, including 11 studies focused on wound healing outcomes reporting 91 different predictors. Three studies shared similar predicted outcomes, follow-up timepoint and used a Cox proportional hazards model. However, these models reported different predictor selection methods and different predictors and it was therefore not feasible to summarize performance, such as discriminative ability. CONCLUSIONS: There are no standout risk prediction models in the literature with promising clinical application for patients with VLUs. Future research should focus on developing and validating high-performing models in wider VLU populations.

Predicting procedure duration of colorectal endoscopic submucosal dissection at Western endoscopy centers.

Background and study aims Overcoming logistical obstacles for the implementation of colorectal endoscopic submucosal dissection (ESD) requires accurate prediction of procedure times. We aimed to evaluate existing and new prediction models for ESD duration. Patients and methods Records of all consecutive patients who underwent single, non-hybrid colorectal ESDs before 2020 at three Dutch centers were reviewed. The performance of an Eastern prediction model [GIE 2021;94(1):133-144] was assessed in the Dutch cohort. A prediction model for procedure duration was built using multivariable linear regression. The model's performance was validated using internal validation by bootstrap resampling, internal-external cross-validation and external validation in an independent Swedish ESD cohort. Results A total of 435 colorectal ESDs were analyzed (92% en bloc resections, mean duration 139 minutes, mean tumor size 39 mm). The performance of current unstandardized time scheduling practice was suboptimal (explained variance: R 2 =27%). We successfully validated the Eastern prediction model for colorectal ESD duration <60 minutes (c-statistic 0.70, 95% CI 0.62-0.77), but this model was limited due to dichotomization of the outcome and a relatively low frequency (14%) of ESDs completed <60 minutes in the Dutch centers. The model was more useful with a dichotomization cut-off of 120 minutes (c-statistic: 0.75; 88% and 17% of "easy" and "very difficult" ESDs completed <120 minutes, respectively). To predict ESD duration as continuous outcome, we developed and validated the six-variable cESD-TIME formula ( https://cesdtimeformula.shinyapps.io/calculator/ ; optimism-corrected R 2 =61%; R 2 =66% after recalibration of the slope). Conclusions We provided two useful tools for predicting colorectal ESD duration at Western centers. Further improvements and validations are encouraged with potential local adaptation to optimize time planning.

Radiomics preoperative-Fistula Risk Score (RAD-FRS) for pancreatoduodenectomy: development and external validation.

BACKGROUND: Accurately predicting the risk of clinically relevant postoperative pancreatic fistula after pancreatoduodenectomy before surgery may assist surgeons in making more informed treatment decisions and improved patient counselling. The aim was to evaluate the predictive accuracy of a radiomics-based preoperative-Fistula Risk Score (RAD-FRS) for clinically relevant postoperative pancreatic fistula. METHODS: Radiomic features were derived from preoperative CT scans from adult patients after pancreatoduodenectomy at a single centre in the Netherlands (Amsterdam, 2013-2018) to develop the radiomics-based preoperative-Fistula Risk Score. Extracted radiomic features were analysed with four machine learning classifiers. The model was externally validated in a single centre in Italy (Verona, 2020-2021). The radiomics-based preoperative-Fistula Risk Score was compared with the Fistula Risk Score and the updated alternative Fistula Risk Score. RESULTS: Overall, 359 patients underwent a pancreatoduodenectomy, of whom 89 (25 per cent) developed a clinically relevant postoperative pancreatic fistula. The radiomics-based preoperative-Fistula Risk Score model was developed using CT scans of 118 patients, of which three radiomic features were included in the random forest model, and externally validated in 57 patients. The model performed well with an area under the curve of 0.90 (95 per cent c.i. 0.71 to 0.99) and 0.81 (95 per cent c.i. 0.69 to 0.92) in the Amsterdam test set and Verona data set respectively. The radiomics-based preoperative-Fistula Risk Score performed similarly to the Fistula Risk Score (area under the curve 0.79) and updated alternative Fistula Risk Score (area under the curve 0.79). CONCLUSION: The radiomics-based preoperative-Fistula Risk Score, which uses only preoperative CT features, is a new and promising radiomics-based score that has the potential to be integrated with hospital CT report systems and improve patient counselling before surgery. The model with underlying code is readily available via www.pancreascalculator.com and www.github.com/PHAIR-Consortium/POPF-predictor.

Early surgery versus conservative treatment in patients with traumatic intracerebral hematoma: a CENTER-TBI study.

PURPOSE: Evidence regarding the effect of surgery in traumatic intracerebral hematoma (t-ICH) is limited and relies on the STITCH(Trauma) trial. This study is aimed at comparing the effectiveness of early surgery to conservative treatment in patients with a t-ICH. METHODS: In a prospective cohort, we included patients with a large t-ICH (< 48 h of injury). Primary outcome was the Glasgow Outcome Scale Extended (GOSE) at 6 months, analyzed with multivariable proportional odds logistic regression. Subgroups included injury severity and isolated vs. non-isolated t-ICH. RESULTS: A total of 367 patients with a large t-ICH were included, of whom 160 received early surgery and 207 received conservative treatment. Patients receiving early surgery were younger (median age 54 vs. 58 years) and more severely injured (median Glasgow Coma Scale 7 vs. 10) compared to those treated conservatively. In the overall cohort, early surgery was not associated with better functional outcome (adjusted odds ratio (AOR) 1.1, (95% CI, 0.6-1.7)) compared to conservative treatment. Early surgery was associated with better outcome for patients with moderate TBI and isolated t-ICH (AOR 1.5 (95% CI, 1.1-2.0); P value for interaction 0.71, and AOR 1.8 (95% CI, 1.3-2.5); P value for interaction 0.004). Conversely, in mild TBI and those with a smaller t-ICH (< 33 cc), conservative treatment was associated with better outcome (AOR 0.6 (95% CI, 0.4-0.9); P value for interaction 0.71, and AOR 0.8 (95% CI, 0.5-1.0); P value for interaction 0.32). CONCLUSIONS: Early surgery in t-ICH might benefit those with moderate TBI and isolated t-ICH, comparable with results of the STITCH(Trauma) trial.

Comparative effectiveness of decompressive craniectomy versus craniotomy for traumatic acute subdural hematoma (CENTER-TBI): an observational cohort study.

Background: Limited evidence existed on the comparative effectiveness of decompressive craniectomy (DC) versus craniotomy for evacuation of traumatic acute subdural hematoma (ASDH) until the recently published randomised clinical trial RESCUE-ASDH. In this study, that ran concurrently, we aimed to determine current practice patterns and compare outcomes of primary DC versus craniotomy. Methods: We conducted an analysis of centre treatment preference within the prospective, multicentre, observational Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (known as CENTER-TBI) and NeuroTraumatology Quality Registry (known as Net-QuRe) studies, which enrolled patients throughout Europe and Israel (2014-2020). We included patients with an ASDH who underwent acute neurosurgical evacuation. Patients with severe pre-existing neurological disorders were excluded. In an instrumental variable analysis, we compared outcomes between centres according to treatment preference, measured by the case-mix adjusted proportion DC per centre. The primary outcome was functional outcome rated by the 6-months Glasgow Outcome Scale Extended, estimated with ordinal regression as a common odds ratio (OR), adjusted for prespecified confounders. Variation in centre preference was quantified with the median odds ratio (MOR). CENTER-TBI is registered with ClinicalTrials.gov, number NCT02210221, and the Resource Identification Portal (Research Resource Identifier SCR_015582). Findings: Between December 19, 2014 and December 17, 2017, 4559 patients with traumatic brain injury were enrolled in CENTER-TBI of whom 336 (7%) underwent acute surgery for ASDH evacuation; 91 (27%) underwent DC and 245 (63%) craniotomy. The proportion primary DC within total acute surgery cases ranged from 6 to 67% with an interquartile range (IQR) of 12-26% among 46 centres; the odds of receiving a DC for prognostically similar patients in one centre versus another randomly selected centre were trebled (adjusted median odds ratio 2.7, p < 0.0001). Higher centre preference for DC over craniotomy was not associated with better functional outcome (adjusted common odds ratio (OR) per 14% [IQR increase] more DC in a centre = 0.9 [95% CI 0.7-1.1], n = 200). Primary DC was associated with more follow-on surgeries and complications [secondary cranial surgery 27% vs. 18%; shunts 11 vs. 5%]; and similar odds of in-hospital mortality (adjusted OR per 14% IQR more primary DC 1.3 [95% CI (1.0-3.4), n = 200]). Interpretation: We found substantial practice variation in the employment of DC over craniotomy for ASDH. This variation in treatment strategy did not result in different functional outcome. These findings suggest that primary DC should be restricted to salvageable patients in whom immediate replacement of the bone flap is not possible due to intraoperative brain swelling. Funding: Hersenstichting Nederland for the Dutch NeuroTraumatology Quality Registry and the European Union Seventh Framework Program.

Predicting ICU Mortality in Acute Respiratory Distress Syndrome Patients Using Machine Learning: The Predicting Outcome and STratifiCation of severity in ARDS (POSTCARDS) Study.

OBJECTIVES: To assess the value of machine learning approaches in the development of a multivariable model for early prediction of ICU death in patients with acute respiratory distress syndrome (ARDS). DESIGN: A development, testing, and external validation study using clinical data from four prospective, multicenter, observational cohorts. SETTING: A network of multidisciplinary ICUs. PATIENTS: A total of 1,303 patients with moderate-to-severe ARDS managed with lung-protective ventilation. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We developed and tested prediction models in 1,000 ARDS patients. We performed logistic regression analysis following variable selection by a genetic algorithm, random forest and extreme gradient boosting machine learning techniques. Potential predictors included demographics, comorbidities, ventilatory and oxygenation descriptors, and extrapulmonary organ failures. Risk modeling identified some major prognostic factors for ICU mortality, including age, cancer, immunosuppression, Pa o2 /F io2 , inspiratory plateau pressure, and number of extrapulmonary organ failures. Together, these characteristics contained most of the prognostic information in the first 24 hours to predict ICU mortality. Performance with machine learning methods was similar to logistic regression (area under the receiver operating characteristic curve [AUC], 0.87; 95% CI, 0.82-0.91). External validation in an independent cohort of 303 ARDS patients confirmed that the performance of the model was similar to a logistic regression model (AUC, 0.91; 95% CI, 0.87-0.94). CONCLUSIONS: Both machine learning and traditional methods lead to promising models to predict ICU death in moderate/severe ARDS patients. More research is needed to identify markers for severity beyond clinical determinants, such as demographics, comorbidities, lung mechanics, oxygenation, and extrapulmonary organ failure to guide patient management.

Predicting Depression Risk in Patients with Cancer Using Multimodal Data.

When patients with cancer develop depression, it is often left untreated. We developed a prediction model for depression risk within the first month after starting cancer treatment using machine learning and Natural Language Processing (NLP) models. The LASSO logistic regression model based on structured data performed well, whereas the NLP model based on only clinician notes did poorly. After further validation, prediction models for depression risk could lead to earlier identification and treatment of vulnerable patients, ultimately improving cancer care and treatment adherence.

Clinical outcome in decompression alone versus decompression and instrumented fusion in patients with isthmic spondylolisthesis: a prospective cohort study.

OBJECTIVE: In the surgical treatment of isthmic spondylolisthesis, it is debatable whether instrumented fusion is mandatory in addition to decompression. The objective of this prospective cohort study was to assess the long-term effect of decompression alone compared with decompression and instrumented fusion in patients who underwent the intervention of their own preference. The results were compared with those in patients who underwent randomly assigned treatment. METHODS: The authors performed a prospective observational multicenter cohort study, including 91 patients with isthmic spondylolisthesis assigned to undergo either decompression alone (n = 44) or decompression and fusion (n = 47). The main outcomes were the Roland-Morris Disability Questionnaire (RDQ) scores and the patient's perceived recovery at the 2-year follow-up. Secondary outcomes were visual analog scale (VAS) leg pain and back pain scores and the reoperation rate. A meta-analysis was performed for data from this cohort study (n = 91) and from a randomized controlled trial (RCT) previously reported by the authors (n = 84). Subgroup analyses were performed on these combined data for age, sex, weight, smoking, and Meyerding grade. RESULTS: At the 12-week follow-up, improvements of RDQ scores were comparable for the two procedures (decompression alone [D group] 4.4, 95% CI 2.3-6.5; decompression and fusion [DF group] 5.8, 95% CI -4.3 to 1.4; p = 0.31). Likewise, VAS leg pain scores (D group 35.0, 95% CI 24.5-45.6; DF group 47.5, 95% CI 37.4-57.5; p = 0.09) and VAS back pain scores (D group 23.5, 95% CI 13.3-33.7; DF group 34.0, 95% CI 24.1-43.8; p = 0.15) were comparable. At the 2-year follow-up, there were no significant differences between the two groups in terms of scores for RDQ (difference -3.1, 95% CI -6.4 to 0.3, p = 0.07), VAS leg pain (difference -7.4, 95% CI -22.1 to 7.2, p = 0.31), and VAS back pain (difference -11.4, 95% CI -25.7 to 2.9, p = 0.12). In contrast, patient-perceived recovery from leg pain was significantly higher in the DF group (79% vs 51%, p = 0.02). Subgroup analyses did not demonstrate a superior outcome for decompression alone compared with decompression and fusion. Nine patients (20.5%) underwent reoperation in total, all in the D group. The meta-analysis including both the cohort and RCT populations yielded an estimated pooled mean difference in RDQ of -3.7 (95% CI -5.94 to -1.55, p = 0.0008) in favor of decompression and fusion at the 2-year follow-up. CONCLUSIONS: In patients with isthmic spondylolisthesis, at the 2-year follow-up, patients who underwent decompression and fusion showed superior functional outcome and perceived recovery compared with those who underwent decompression alone. No subgroups benefited from decompression alone. Therefore, decompression and fusion is recommended over decompression alone as a primary surgical treatment option in isthmic spondylolisthesis.

Factors influencing post-surgical survival in degenerative mitral regurgitation.

AIMS: Indications for surgery in patients with degenerative mitral regurgitation (DMR) are increasingly liberal in all clinical guidelines but the role of secondary outcome determinants (left atrial volume index ≥60 mL/m2, atrial fibrillation, pulmonary artery systolic pressure ≥50 mmHg and moderate to severe tricuspid regurgitation) and their impact on post-operative outcome remain disputed. Whether these secondary outcome markers are just reflective of the DMR severity or intrinsically affect survival after DMR surgery is uncertain and may have critical importance in the management of patients with DMR. To address these gaps of knowledge the present study gathered a large cohort of patients with quantified DMR, accounted for the number of secondary outcome markers and examined their independent impact on survival after surgical correction of the DMR. METHODS AND RESULTS: The Mitral Regurgitation International DAtabase-Quantitative registry includes patients with isolated DMR from centres across North America, Europe, and the Middle East. Patient enrolment extended from January 2003 to January 2020. All patients undergoing mitral valve surgery within 1 year of registry enrolment were selected. A total of 2276 patients [65 (55-73) years, 32% male] across five centres met study eligibility criteria. Over a median follow-up of 5.6 (3.6 to 8.7) years, 278 patients (12.2%) died. In a comprehensive multivariable Cox regression model adjusted for age, EuroSCORE II, symptoms, left ventricular ejection fraction (LVEF), left ventricular end-systolic diameter (LV ESD) and DMR severity, the number of secondary outcome determinants was independently associated with post-operative all-cause mortality, with adjusted hazard ratios of 1.56 [95% confidence interval (CI): 1.11-2.20, P = 0.011], 1.78 (95% CI: 1.23-2.58, P = 0.002) and 2.58 (95% CI: 1.73-3.83, P < 0.0001) for patients with one, two, and three or four secondary outcome determinants, respectively. A model incorporating the number of secondary outcome determinants demonstrated a higher C-index and was significantly more concordant with post-operative mortality than models incorporating traditional Class I indications alone [the presence of symptoms (P = 0.0003), or LVEF ≤60% (P = 0.006), or LV ESD ≥40 mm (P = 0.014)], while there was no significant difference in concordance observed compared with a model that incorporated the number of Class I indications for surgery combined (P = 0.71). CONCLUSION: In this large cohort of patients treated surgically for DMR, the presence and number of secondary outcome determinants was independently associated with post-surgical survival and demonstrated better outcome discrimination than traditional Class I indications for surgery. Randomised controlled trials are needed to determine if patients with severe DMR who demonstrate a cardiac phenotype with an increasing number of secondary outcome determinants would benefit from earlier surgery.

Distal Pancreatectomy Fistula Risk Score (D-FRS): Development and International Validation.

OBJECTIVE: To develop 2 distinct preoperative and intraoperative risk scores to predict postoperative pancreatic fistula (POPF) after distal pancreatectomy (DP) to improve preventive and mitigation strategies, respectively. BACKGROUND: POPF remains the most common complication after DP. Despite several known risk factors, an adequate risk model has not been developed yet. METHODS: Two prediction risk scores were designed using data of patients undergoing DP in 2 Italian centers (2014-2016) utilizing multivariable logistic regression. The preoperative score (calculated before surgery) aims to facilitate preventive strategies and the intraoperative score (calculated at the end of surgery) aims to facilitate mitigation strategies. Internal validation was achieved using bootstrapping. These data were pooled with data from 5 centers from the United States and the Netherlands (2007-2016) to assess discrimination and calibration in an internal-external validation procedure. RESULTS: Overall, 1336 patients after DP were included, of whom 291 (22%) developed POPF. The preoperative distal fistula risk score (preoperative D-FRS) included 2 variables: pancreatic neck thickness [odds ratio: 1.14; 95% confidence interval (CI): 1.11-1.17 per mm increase] and pancreatic duct diameter (OR: 1.46; 95% CI: 1.32-1.65 per mm increase). The model performed well with an area under the receiver operating characteristic curve of 0.83 (95% CI: 0.78-0.88) and 0.73 (95% CI: 0.70-0.76) upon internal-external validation. Three risk groups were identified: low risk (<10%), intermediate risk (10%-25%), and high risk (>25%) for POPF with 238 (18%), 684 (51%), and 414 (31%) patients, respectively. The intraoperative risk score (intraoperative D-FRS) added body mass index, pancreatic texture, and operative time as variables with an area under the receiver operating characteristic curve of 0.80 (95% CI: 0.74-0.85). CONCLUSIONS: The preoperative and the intraoperative D-FRS are the first validated risk scores for POPF after DP and are readily available at: http://www.pancreascalculator.com . The 3 distinct risk groups allow for personalized treatment and benchmarking.