ICG-Fluorescence Imaging for Margin Assessment During Minimally Invasive Colorectal Liver Metastasis Resection.

Importance: Unintended tumor-positive resection margins occur frequently during minimally invasive surgery for colorectal liver metastases and potentially negatively influence oncologic outcomes. Objective: To assess whether indocyanine green (ICG)-fluorescence-guided surgery is associated with achieving a higher radical resection rate in minimally invasive colorectal liver metastasis surgery and to assess the accuracy of ICG fluorescence for predicting the resection margin status. Design, Setting, and Participants: The MIMIC (Minimally Invasive, Indocyanine-Guided Metastasectomy in Patients With Colorectal Liver Metastases) trial was designed as a prospective single-arm multicenter cohort study in 8 Dutch liver surgery centers. Patients were scheduled to undergo minimally invasive (laparoscopic or robot-assisted) resections of colorectal liver metastases between September 1, 2018, and June 30, 2021. Exposures: All patients received a single intravenous bolus of 10 mg of ICG 24 hours prior to surgery. During surgery, ICG-fluorescence imaging was used as an adjunct to ultrasonography and regular laparoscopy to guide and assess the resection margin in real time. The ICG-fluorescence imaging was performed during and after liver parenchymal transection to enable real-time assessment of the tumor margin. Absence of ICG fluorescence was favorable both during transection and in the tumor bed directly after resection. Main Outcomes and Measures: The primary outcome measure was the radical (R0) resection rate, defined by the percentage of colorectal liver metastases resected with at least a 1 mm distance between the tumor and resection plane. Secondary outcomes were the accuracy of ICG fluorescence in detecting margin-positive (R1; <1 mm margin) resections and the change in surgical management. Results: In total, 225 patients were enrolled, of whom 201 (116 [57.7%] male; median age, 65 [IQR, 57-72] years) with 316 histologically proven colorectal liver metastases were included in the final analysis. The overall R0 resection rate was 92.4%. Re-resection of ICG-fluorescent tissue in the resection cavity was associated with a 5.0% increase in the R0 percentage (from 87.4% to 92.4%; P < .001). The sensitivity and specificity for real-time resection margin assessment were 60% and 90%, respectively (area under the receiver operating characteristic curve, 0.751; 95% CI, 0.668-0.833), with a positive predictive value of 54% and a negative predictive value of 92%. After training and proctoring of the first procedures, participating centers that were new to the technique had a comparable false-positive rate for predicting R1 resections during the first 10 procedures (odds ratio, 1.36; 95% CI, 0.44-4.24). The ICG-fluorescence imaging was associated with changes in intraoperative surgical management in 56 (27.9%) of the patients. Conclusions and Relevance: In this multicenter prospective cohort study, ICG-fluorescence imaging was associated with an increased rate of tumor margin-negative resection and changes in surgical management in more than one-quarter of the patients. The absence of ICG fluorescence during liver parenchymal transection predicted an R0 resection with 92% accuracy. These results suggest that use of ICG fluorescence may provide real-time feedback of the tumor margin and a higher rate of complete oncologic resection.

The AccelerAge framework: a new statistical approach to predict biological age based on time-to-event data.

Aging is a multifaceted and intricate physiological process characterized by a gradual decline in functional capacity, leading to increased susceptibility to diseases and mortality. While chronological age serves as a strong risk factor for age-related health conditions, considerable heterogeneity exists in the aging trajectories of individuals, suggesting that biological age may provide a more nuanced understanding of the aging process. However, the concept of biological age lacks a clear operationalization, leading to the development of various biological age predictors without a solid statistical foundation. This paper addresses these limitations by proposing a comprehensive operationalization of biological age, introducing the "AccelerAge" framework for predicting biological age, and introducing previously underutilized evaluation measures for assessing the performance of biological age predictors. The AccelerAge framework, based on Accelerated Failure Time (AFT) models, directly models the effect of candidate predictors of aging on an individual's survival time, aligning with the prevalent metaphor of aging as a clock. We compare predictors based on the AccelerAge framework to a predictor based on the GrimAge predictor, which is considered one of the best-performing biological age predictors, using simulated data as well as data from the UK Biobank and the Leiden Longevity Study. Our approach seeks to establish a robust statistical foundation for biological age clocks, enabling a more accurate and interpretable assessment of an individual's aging status.

Certification Training and Liver Transplant Experience Improves Liver Procurement Outcomes: The Dutch Approach.

BACKGROUND: This study investigates the impact of certification training and liver transplant experience on procurement outcomes of deceased donor liver procurement in the Netherlands. METHODS: Three groups (trainee, certified, and master) were formed, with further subdivision based on liver transplant experience. Three key outcomes-surgical injury, graft discard after injury, and donor hepatectomy duration-were analyzed. RESULTS: There were no significant differences in surgical graft injury in the three groups (trainee, 16.9%; certified, 14.8%; master, 18.2%; P = 0.357; 2011 to 2018). The only predictor for surgical graft injury was donation after cardiac death (odds ratio [OR], 1.49; 95% confidence interval [CI], 1.10-2.02). Of the three groups, the master group had the highest discard rate after surgical injury (trainee, 0%; certified, 1.3%; master, 2.8%; P = 0.013). Master group without liver transplant experience (OR, 3.16; 95% CI, 1.21-8.27) and male donor sex (OR, 3.58; 95% CI, 1.32-9.73) were independent risk factors for discarding livers after surgical injury. Independent predictors for shorter hepatectomy durations included donors older than 50 years (coefficient [Coeff], -7.04; 95% CI, -8.03 to -3.29; P < 0.001), and master group (Coeff, -9.84; 95% CI, -14.37 to -5.31; P < 0.001) and certified group with liver transplant experience (Coeff, -6.54; 95% CI, -10.83 to -2.26; P = 0.003). On the other hand, master group without liver transplant experience (Coeff, 5.00; 95% CI, 1.03-8.96; P = 0.014) and donation after cardiac death (Coeff, 10.81; 95% CI, 8.32-13.3; P < 0.001) were associated with longer hepatectomy durations. CONCLUSIONS: Training and certification in abdominal organ procurement surgery were associated with a reduced discard rate for surgical injured livers and shorter hepatectomy times. The contrast between master group with and without liver transplant experience underscores the need for specialized training in this field.

Oncological outcomes after a pathological complete response following total neoadjuvant therapy or chemoradiotherapy for high-risk locally advanced rectal cancer in the RAPIDO trial.

BACKGROUND: A pathological complete response (pCR) following chemoradiation (CRT) or short-course radiotherapy (scRT) leads to a favourable prognosis in patients with rectal cancer. Total neo-adjuvant therapy (TNT) doubles the pCR rate, but it is unknown whether oncological outcomes remain favourable and whether the same characteristics are associated with pCR as after CRT. METHODS: Comparison between patients with pCR in the RAPIDO trial in the experimental [EXP] (scRT, chemotherapy, surgery, as TNT) and standard-of-care treatment [STD] (CRT, surgery, postoperative chemotherapy depending on hospital policy) groups. Primary and secondary outcomes were time-to-recurrence (TTR), overall survival (OS) and association between patient, tumour, and treatment characteristics and pCR. RESULTS: Among patients with a resection within six months after preoperative treatment, 120/423 (28%) [EXP] and 57/398 (14%) [STD] achieved a pCR. Following pCR, 5-year cumulative TTR and OS rates in the EXP and STD arms were 8% vs. 7% (hazard ratio 1.04, 95%CI 0.32-3.38) and 94% vs. 93% (hazard ratio 1.41, 95%CI 0.51-3.92), respectively. Besides the EXP treatment (odds ratio 2.70, 95%CI 1.83-3.97), pre-treatment carcinoembryonic antigen (CEA) <5, pre-treatment tumour size <40 mm and cT2 were associated with pCR. Distance from the anal verge was the only characteristic with a statistically significant difference in association with pCR between the EXP and STD treatment (Pinteraction=0.042). pCR rates did not increase with prolonged treatment time. CONCLUSIONS: The doubled pCR rate of TNT compared to CRT results in similar oncological outcomes. Characteristics associated with pCR are the EXP treatment, normal CEA, and small tumour size.

Clarifying the biological and statistical assumptions of cross-sectional biological age predictors: an elaborate illustration using synthetic and real data.

BACKGROUND: There is divergence in the rate at which people age. The concept of biological age is postulated to capture this variability, and hence to better represent an individual's true global physiological state than chronological age. Biological age predictors are often generated based on cross-sectional data, using biochemical or molecular markers as predictor variables. It is assumed that the difference between chronological and predicted biological age is informative of one's chronological age-independent aging divergence ∆. METHODS: We investigated the statistical assumptions underlying the most popular cross-sectional biological age predictors, based on multiple linear regression, the Klemera-Doubal method or principal component analysis. We used synthetic and real data to illustrate the consequences if this assumption does not hold. RESULTS: The most popular cross-sectional biological age predictors all use the same strong underlying assumption, namely that a candidate marker of aging's association with chronological age is directly informative of its association with the aging rate ∆. We called this the identical-association assumption and proved that it is untestable in a cross-sectional setting. If this assumption does not hold, weights assigned to candidate markers of aging are uninformative, and no more signal may be captured than if markers would have been assigned weights at random. CONCLUSIONS: Cross-sectional methods for predicting biological age commonly use the untestable identical-association assumption, which previous literature in the field had never explicitly acknowledged. These methods have inherent limitations and may provide uninformative results, highlighting the importance of researchers exercising caution in the development and interpretation of cross-sectional biological age predictors.

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.

Bias of the additive hazard model in the presence of causal effect heterogeneity.

Hazard ratios are prone to selection bias, compromising their use as causal estimands. On the other hand, if Aalen's additive hazard model applies, the hazard difference has been shown to remain unaffected by the selection of frailty factors over time. Then, in the absence of confounding, observed hazard differences are equal in expectation to the causal hazard differences. However, in the presence of effect (on the hazard) heterogeneity, the observed hazard difference is also affected by selection of survivors. In this work, we formalize how the observed hazard difference (from a randomized controlled trial) evolves by selecting favourable levels of effect modifiers in the exposed group and thus deviates from the causal effect of interest. Such selection may result in a non-linear integrated hazard difference curve even when the individual causal effects are time-invariant. Therefore, a homogeneous time-varying causal additive effect on the hazard cannot be distinguished from a time-invariant but heterogeneous causal effect. We illustrate this causal issue by studying the effect of chemotherapy on the survival time of patients suffering from carcinoma of the oropharynx using data from a clinical trial. The hazard difference can thus not be used as an appropriate measure of the causal effect without making untestable assumptions.

Intraoperative transit time flow measurement predicts maturation of radiocephalic arteriovenous fistulas.

OBJECTIVE: The arteriovenous fistula (AVF) is the first choice for gaining vascular access for hemodialysis. However, 20% to 50% of AVFs fail within 4 months after creation. Although demographic risk factors have been described, there is little evidence on the intraoperative predictors of AVF maturation failure. The aim of this study was to assess the predictive value of intraoperative transit time flow measurements (TTFMs) on AVF maturation failure. METHODS: In this retrospective cohort study, intraoperative blood flow, measured using TTFM, was compared with AVF maturation after 6 weeks in 55 patients. Owing to its significantly higher prevalence and risk of nonmaturation, the radiocephalic AVF (RCAVF) was the main focus of this study. A recommended cutoff point for high vs low intraoperative blood flow was determined for RCAVFs, using a receiver operating characteristic curve. RESULTS: The average intraoperative blood flow in RCAVFs was 156 mL/min. Patients with an intraoperative blood flow equal or lower than the determined cutoff point of 160 mL/min, showed a 3.03 times increased risk of AVF maturation failure after 6 weeks, compared with patients with a higher intraoperative blood flow (P < .001). CONCLUSIONS: The intraoperative blood flow in RCAVFs measured by TTFM provides an adequate means of predicting AVF nonmaturation 6 weeks after surgery. For RCAVFs, a cutoff point for intraoperative blood flow of 160 mL/min is recommended for maximum sensitivity and specificity to predict AVF maturation failure after 6 weeks.

The built-in selection bias of hazard ratios formalized using structural causal models.

It is known that the hazard ratio lacks a useful causal interpretation. Even for data from a randomized controlled trial, the hazard ratio suffers from so-called built-in selection bias as, over time, the individuals at risk among the exposed and unexposed are no longer exchangeable. In this paper, we formalize how the expectation of the observed hazard ratio evolves and deviates from the causal effect of interest in the presence of heterogeneity of the hazard rate of unexposed individuals (frailty) and heterogeneity in effect (individual modification). For the case of effect heterogeneity, we define the causal hazard ratio. We show that the expected observed hazard ratio equals the ratio of expectations of the latent variables (frailty and modifier) conditionally on survival in the world with and without exposure, respectively. Examples with gamma, inverse Gaussian and compound Poisson distributed frailty and categorical (harming, beneficial or neutral) distributed effect modifiers are presented for illustration. This set of examples shows that an observed hazard ratio with a particular value can arise for all values of the causal hazard ratio. Therefore, the hazard ratio cannot be used as a measure of the causal effect without making untestable assumptions, stressing the importance of using more appropriate estimands, such as contrasts of the survival probabilities.

Dynamic prediction of survival using multivariate functional principal component analysis: A strict landmarking approach.

Dynamically predicting patient survival probabilities using longitudinal measurements has become of great importance with routine data collection becoming more common. Many existing models utilize a multi-step landmarking approach for this problem, mostly due to its ease of use and versatility but unfortunately most fail to do so appropriately. In this article we make use of multivariate functional principal component analysis to summarize the available longitudinal information, and employ a Cox proportional hazards model for prediction. Additionally, we consider a centred functional principal component analysis procedure in an attempt to remove the natural variation incurred by the difference in age of the considered subjects. We formalize the difference between a 'relaxed' landmarking approach where only validation data is landmarked and a 'strict' landmarking approach where both the training and validation data are landmarked. We show that a relaxed landmarking approach fails to effectively use the information contained in the longitudinal outcomes, thereby producing substantially worse prediction accuracy than a strict landmarking approach.

Transplant Outcomes After Exposure of Deceased Kidney Donors to Contrast Medium.

BACKGROUND: The administration of contrast medium is associated with acute kidney injury; however, the effect of exposure of a deceased organ donor to contrast medium on kidney transplant outcomes has been poorly studied. METHODS: A retrospective analysis of all deceased kidney donors between 2011 and 2021 and their corresponding recipients in the Netherlands was conducted. Multivariable analyses were performed to assess the associations between contrast medium exposure and delayed graft function (DGF)/graft survival. Linear mixed models were used to assess the differences in mean estimated glomerular filtration rate values in recipients 1 to 6 y after transplantation. RESULTS: In total, 2177 donors and 3638 corresponding kidney graft recipients were included. Twenty-four percent of the donors (n = 520) were exposed to contrast medium, corresponding to 23% of recipients (n = 832). DGF was observed in 36% (n = 1321) and primary nonfunction in 3% (n = 122) of recipients. DGF rates for donation after brain death (DBD) and donation after circulatory death (DCD) donors showed no significant effect of contrast medium exposure ( P = 0.15 and P = 0.60 for DBD and DCD donors, respectively). In multivariable analyses, contrast medium administration was not significantly associated with a higher DGF risk (odds ratio 1.06; 95% confidence interval, 0.86-1.36; P = 0.63) nor was a significant predictor for death-censored graft failure (hazard ratio 1.01; 95% confidence interval, 0.77-1.33; P = 0.93). Linear mixed models showed no difference in mean estimated glomerular filtration rate values in recipients 1 to 6 y posttransplantation ( P = 0.78). CONCLUSIONS: This study indicates that contrast medium administration in DBD and DCD donors has no negative effect on early and long-term kidney graft function.

Haplotype reconstruction for genetically complex regions with ambiguous genotype calls: Illustration by the KIR gene region.

Advances in DNA sequencing technologies have enabled genotyping of complex genetic regions exhibiting copy number variation and high allelic diversity, yet it is impossible to derive exact genotypes in all cases, often resulting in ambiguous genotype calls, that is, partially missing data. An example of such a gene region is the killer-cell immunoglobulin-like receptor (KIR) genes. These genes are of special interest in the context of allogeneic hematopoietic stem cell transplantation. For such complex gene regions, current haplotype reconstruction methods are not feasible as they cannot cope with the complexity of the data. We present an expectation-maximization (EM)-algorithm to estimate haplotype frequencies (HTFs) which deals with the missing data components, and takes into account linkage disequilibrium (LD) between genes. To cope with the exponential increase in the number of haplotypes as genes are added, we add three components to a standard EM-algorithm implementation. First, reconstruction is performed iteratively, adding one gene at a time. Second, after each step, haplotypes with frequencies below a threshold are collapsed in a rare haplotype group. Third, the HTF of the rare haplotype group is profiled in subsequent iterations to improve estimates. A simulation study evaluates the effect of combining information of multiple genes on the estimates of these frequencies. We show that estimated HTFs are approximately unbiased. Our simulation study shows that the EM-algorithm is able to combine information from multiple genes when LD is high, whereas increased ambiguity levels increase bias. Linear regression models based on this EM, show that a large number of haplotypes can be problematic for unbiased effect size estimation and that models need to be sparse. In a real data analysis of KIR genotypes, we compare HTFs to those obtained in an independent study. Our new EM-algorithm-based method is the first to account for the full genetic architecture of complex gene regions, such as the KIR gene region. This algorithm can handle the numerous observed ambiguities, and allows for the collapsing of haplotypes to perform implicit dimension reduction. Combining information from multiple genes improves haplotype reconstruction.

Survival benefit from liver transplantation for patients with and without hepatocellular carcinoma.

Background & Aims: In the USA, inequal liver transplantation (LT) access exists between patients with and without hepatocellular carcinoma (HCC). Survival benefit considers survival without and with LT and could equalise LT access. We calculated bias-corrected LT survival benefit for patients with(out) HCC who underwent a transplant, based on longitudinal data in a recent United States cohort. Methods: Adult LT candidates with(out) HCC between 2010 and 2019 were included. Waitlist survival over time was contrasted to post-transplant survival, to estimate 5-year survival benefit from the moment of LT. Waitlist survival was modelled with a bias-corrected Cox regression, and post-transplant survival was estimated through Cox proportional hazards regression. Results: Mean HCC survival without LT was always lower than non-HCC waitlist survival. Below model for end-stage liver disease (sodium) (MELD(-Na)) 30, patients with HCC gained more life-years from LT than patients without HCC at the same MELD(-Na) score. Only patients without HCC below MELD(-Na) 9 had negative benefit. Most patients with HCC underwent a transplant below MELD(-Na) 14, and most patients without HCC underwent a transplant above MELD(-Na) 26. Liver function [MELD(-Na), albumin] was the main predictor of 5-year benefit. Therefore, during 5 years, most patients with HCC gained 0.12 to 1.96 years from LT, whereas most patients without HCC gained 2.48 to 3.45 years. Conclusions: On an individual level, performing a transplant in patients with HCC resulted in survival benefit. However, on a population level, benefit was indirectly decreased, as patients without HCC were likely to gain more survival owing to decreased liver function. For patients who underwent a transplant, a constructed online calculator estimates 5-year survival benefit given specific patient characteristics. Survival benefit scores could serve to equalise LT access. Impact and implications: Benefit is a comparison of the survival with and without liver transplantation, and it is important when deciding who should undergo a transplant. Liver function is most important when predicting possible benefit from transplantation. Patients with liver cancer die sooner on the waiting list than similar patients without liver cancer. However, patients with liver cancer more often have better liver function. Most patients without liver cancer derive more benefit from transplantation than patients with liver cancer.

Predicting Patient Death after Allogeneic Stem Cell Transplantation for Inborn Errors Using Machine Learning (PREPAD): A European Society for Blood and Marrow Transplantation Inborn Errors Working Party Study.

Allogeneic hematopoietic stem cell transplantation (HSCT) is a curative treatment for many inborn errors of immunity, metabolism, and hematopoiesis. No predictive models are available for these disorders. We created a machine learning model using XGBoost to predict survival after HSCT using European Society for Blood and Marrow Transplant registry data of 10,888 patients who underwent HSCT for inborn errors between 2006 and 2018, and compared it to a simple linear Cox model, an elastic net Cox model, and a random forest model. The XGBoost model had a cross-validated area under the curve value of .73 at 1 year, which was significantly superior to the other models, and it accurately predicted for countries excluded while training. It predicted close to 0% and >30% mortality more often than other models at 1 year, while maintaining good calibration. The 5-year survival was 94.7% in the 25% of patients at lowest risk and 62.3% in the 25% at highest risk. Within disease and donor subgroups, XGBoost outperformed the best univariate predictor. We visualized the effect of the main predictors-diagnosis, performance score, patient age and donor type-using the SHAP ML explainer and developed a stand-alone application, which can predict using the model and visualize predictions. The risk of mortality after HSCT for inborn errors can be accurately predicted using an explainable machine learning model. This exceeds the performance of models described in the literature. Doing so can help detect deviations from expected survival and improve risk stratification in trials.

Joint models quantify associations between immune cell kinetics and allo-immunological events after allogeneic stem cell transplantation and subsequent donor lymphocyte infusion.

Alloreactive donor-derived T-cells play a pivotal role in alloimmune responses after allogeneic hematopoietic stem cell transplantation (alloSCT); both in the relapse-preventing Graft-versus-Leukemia (GvL) effect and the potentially lethal complication Graft-versus-Host-Disease (GvHD). The balance between GvL and GvHD can be shifted by removing T-cells via T-cell depletion (TCD) to reduce the risk of GvHD, and by introducing additional donor T-cells (donor lymphocyte infusions [DLI]) to boost the GvL effect. However, the association between T-cell kinetics and the occurrence of allo-immunological events has not been clearly demonstrated yet. Therefore, we investigated the complex associations between the T-cell kinetics and alloimmune responses in a cohort of 166 acute leukemia patients receiving alemtuzumab-based TCD alloSCT. Of these patients, 62 with an anticipated high risk of relapse were scheduled to receive a prophylactic DLI at 3 months after transplant. In this setting, we applied joint modelling which allowed us to better capture the complex interplay between DLI, T-cell kinetics, GvHD and relapse than traditional statistical methods. We demonstrate that DLI can induce detectable T-cell expansion, leading to an increase in total, CD4+ and CD8+ T-cell counts starting at 3 months after alloSCT. CD4+ T-cells showed the strongest association with the development of alloimmune responses: higher CD4 counts increased the risk of GvHD (hazard ratio 2.44, 95% confidence interval 1.45-4.12) and decreased the risk of relapse (hazard ratio 0.65, 95% confidence interval 0.45-0.92). Similar models showed that natural killer cells recovered rapidly after alloSCT and were associated with a lower risk of relapse (HR 0.62, 95%-CI 0.41-0.93). The results of this study advocate the use of joint models to further study immune cell kinetics in different settings.

Adherence to the Dutch recommendation for physical activity: prior to and after primary total hip and knee arthroplasty.

PURPOSE: To determine the course of adherence to physical activity (PA) recommendation in hip/knee osteoarthritis patients before and after hip/knee arthroplasty (THA/TKA). Moreover, we explored predictors for non-adherence 12 months postoperatively. MATERIALS AND METHODS: Primary THA/TKA were included in a multicenter observational study. Preoperatively and 6/12 months postoperatively, patients reported engagement in moderate-intensity PA in days/week in the past 6 months (PA-recommendation (≥30 min of moderate-intensity ≥5 days/week)). We included predictors stratified by preoperative adherence: sex, age, BMI, comorbidities, smoking, living/working status, season, mental health, HOOS/KOOS subscales before and 6 months postoperatively, and 6-month adherence. RESULTS: (1005 THA/972 TKA) Preoperatively, 50% of the population adhered. Adherence increased to 59% at 6 and 12 months. After 12 months, most patients remained at their preoperative PA level, 11% of the preoperative adherers decreased, while 20% of the preoperative non-adherers increased their PA level. In all different groups, adherence to the PA recommendation at 6 months was identified as a predictor (OR-range: 0.16-0.29). In addition, BMI was identified as predictor in the THA adherent (OR = 1.07; 95%CI [1.02-1.15]) and TKA non-adherent groups (OR = 1.08; 95%CI [1.03-1.12]). THA non-adherent group not having paid work (OR = 0.53; 95%CI [0.33-0.85]), and in the TKA adherent group, lower KOOS subscale symptoms (OR = 1.03; 95%CI [1.01-1.05]) were associated with non-adherence. CONCLUSIONS: Majority of patients remained at their preoperative PA level. Non-adherence at 6 months was highly predictive for 12-month non-adherence.Implications for rehabilitationPhysical activity (PA) is crucial to decrease mortality risk, especially among patients suffering from osteoarthritis, as these patients have the potential to become more physically active after arthroplasty surgery.We found that physical function and pain improved, but 69% of the patients remained at their preoperative PA level, while 11% decreased and 20% increased their PA levels.Using this information shortly after surgery, orthopedic surgeons and other health care professionals can address patients at risk for decreased PA levels and provide PA advice.

Prolonged antibiotic prophylaxis after pancreatoduodenectomy: systematic review and meta-analysis.

BACKGROUND: Previous studies have reported conflicting results of prolonged antibiotic prophylaxis on infectious complications after pancreatoduodenectomy. This study evaluated the effect of prolonged antibiotics on surgical-site infections (SSIs) after pancreatoduodenectomy. METHODS: A systematic review and meta-analysis was undertaken of SSIs in patients with perioperative (within 24 h) versus prolonged antibiotic (over 24 h) prophylaxis after pancreatoduodenectomy. SSIs were classified as organ/space infections or superficial SSI within 30 days after surgery. ORs were calculated using a Mantel-Haenszel fixed-effect model. RESULTS: Ten studies were included in the qualitative analysis, of which 8 reporting on 1170 patients were included in the quantitative analysis. The duration of prolonged antibiotic prophylaxis varied between 2 and 10 days after surgery. Four studies reporting on 782 patients showed comparable organ/space infection rates in patients receiving perioperative and prolonged antibiotics (OR 1.35, 95 per cent c.i. 0.94 to 1.93). However, among patients with preoperative biliary drainage (5 studies reporting on 577 patients), organ/space infection rates were lower with prolonged compared with perioperative antibiotics (OR 2.09, 1.43 to 3.07). Three studies (633 patients) demonstrated comparable superficial SSI rates between patients receiving perioperative versus prolonged prophylaxis (OR 1.54, 0.97 to 2.44), as well as in patients with preoperative biliary drainage in 4 studies reporting on 431 patients (OR 1.60, 0.89 to 2.88). CONCLUSION: Prolonged antibiotic prophylaxis is associated with fewer organ/space infection in patients who undergo preoperative biliary drainage. However, the optimal duration of antibiotic prophylaxis after pancreatoduodenectomy remains to be determined and warrants confirmation in an RCT.

Almost 40 in 100 patients develop an infection after pancreatic surgery. This study collected research that studied the effect of prolonged antibiotics after pancreatic surgery on the number of infections after surgery. Research articles were selected if patients who received antibiotics only during surgery were compared with those who had prolonged antibiotics after surgery. Prolonged antibiotics means antibiotics for longer than 24 h after surgery. Comparing patients who had antibiotics during surgery and those who received prolonged antibiotics after surgery, this study focused on the number of abdominal infections and wound infections. Ten studies were selected, and these studies included 1170 patients in total. The duration of prolonged antibiotics ranged from 2 to 5 days after pancreatic surgery. Four studies (with 782 patients) showed comparable abdominal infections in patients who had antibiotics only during surgery and those who had prolonged antibiotics after surgery (OR 1.35, 95 per cent c.i. 0.94 to 1.93). However, for patients with a stent in the bile duct (5 studies on 577 patients), fewer abdominal infections were seen in patients who had prolonged antibiotics after surgery compared with patients who received antibiotics only during surgery (OR 2.09, 1.43 to 3.07). Three studies (633 patients) showed the same rate of wound infections in patients who had antibiotics only during surgery compared with those who received prolonged antibiotics after operation (OR 1.54, 0.97 to 2.44). The number of wound infections was also the same in patients with a stent in the bile duct (OR 1.60, 0.89 to 2.88). Prolonged antibiotics after pancreatic surgery seem to lower abdominal infections in patients who have a stent placed in the bile duct. However, the best duration of antibiotics is unclear; a decent study is needed.

Added Predictive Value of Female-Specific Factors and Psychosocial Factors for the Risk of Stroke in Women Under 50.

BACKGROUND AND OBJECTIVES: Female-specific factors and psychosocial factors may be important in the prediction of stroke but are not included in prediction models that are currently used. We investigated whether addition of these factors would improve the performance of prediction models for the risk of stroke in women younger than 50 years. METHODS: We used data from the Stichting Informatievoorziening voor Zorg en Onderzoek, population-based, primary care database of women aged 20-49 years without a history of cardiovascular disease. Analyses were stratified by 10-year age intervals at cohort entry. Cox proportional hazards models to predict stroke risk were developed, including traditional cardiovascular factors, and compared with models that additionally included female-specific and psychosocial factors. We compared the risk models using the c-statistic and slope of the calibration curve at a follow-up of 10 years. We developed an age-specific stroke risk prediction tool that may help communicating the risk of stroke in clinical practice. RESULTS: We included 409,026 women with a total of 3,990,185 person-years of follow-up. Stroke occurred in 2,751 women (incidence rate 6.9 [95% CI 6.6-7.2] per 10,000 person-years). Models with only traditional cardiovascular factors performed poorly to moderately in all age groups: 20-29 years: c-statistic: 0.617 (95% CI 0.592-0.639); 30-39 years: c-statistic: 0.615 (95% CI 0.596-0.634); and 40-49 years: c-statistic: 0.585 (95% CI 0.573-0.597). After adding the female-specific and psychosocial risk factors to the reference models, the model discrimination increased moderately, especially in the age groups 30-39 (Δc-statistic: 0.019) and 40-49 years (Δc-statistic: 0.029) compared with the reference models, respectively. DISCUSSION: The addition of female-specific factors and psychosocial risk factors improves the discriminatory performance of prediction models for stroke in women younger than 50 years.