Science deciphered in detail how cytotoxic T lymphocytes (CTLs) kill target cells.1 They found that perforin and pellet enzyme—the cytotoxic substances in CTLs—combined and assembled into supramolecular attack particles (SMAPs). The SMAPs were subsequently released toward the cell membrane of the target cell. The shell of SMAP was rich in glycoproteins that enabled its stability to maintain the cell-killing activity for hours, in which the thrombospondin-1 (TSP-1) was critical  and releases perforin (Prf1) and soluble granzymes (Gzmb) from secretory lysosomes (SLs). Prf1 creates holes in the membrane of target cells through which Gzmb enters to initiate various apoptosis pathways.4 Tamzalit et al. found that even in the absence of perforin, the CTL could initiate necrosis and kill target cells by its mechanical movement.5 The other mechanism is by activating the self-apoptosis process of the cells. CTLs can recognize the death receptor Fas (FasL) expressed on target cells to induce programmed cell death. Despite the ongoing studies, it remains unclear on the details of killing mechanism and its relation to the secretion of cytotoxic molecules Prf1 and Gzmb.There are many types of T cells, among which the CTLs play a major role in destroying virus-infected cells and cancerous cells.To track how perforin and granzyme work when CTLs kill the targets, the authors labeled granular enzymes in CTLs and glycoproteins in SLs with different fluorescein in this work. After in contact with the CTLs, the target cells showed intense double-positive puncta on the surface. These labeled multiprotein structures were defined as SMAPs. With a supported lipid bilayers (SLB) to simulate the target cell, the authors investigated the kinetics of SMAPs release. The SLs in the CTLs rapidly accumulated at the IS after the CTLs were incubated on the SLB. It was rapidly followed by the appearance of Gzmb puncta in the SLB and the attachment of SMAPs to the surface of the simulated target cell.+ and Gzmb+ SMAPs were found to remain attached on the SLB. The authors then tested the stability of SMAPs after being released. After 20 minutes of CTL removal, the SMAPs still existed in the IS and maintained the activity of killing the target cell. The authors further investigated the SMAPs captured on SLB by mass spectrometry. More than 285 proteins were present in SMAPs, including peptides from Prf1 and Gzmb. This result was also confirmed by SDS-PAGE and immunoblotting. Among these proteins, TSP-1 caught the authors’ attention due to its signature Ca2+ binding repeats, which corresponded to the well-established Ca2+ dependent steps in CTL mediated killing. After knocking out TSP-1 by 60%, the CTLs killing efficacy reduced by 30%. These results indicated that the TSP-1 component in SMAP played an important role in the process of CTL killing target cells.After CTL removal, Prf1Furthermore, they investigated SMAPs from a microscopic perspective. Each IS contained about 27 SMAPs with a diameter of about 120 nm. TSP-1 was found to be co-localized with Prf1 and Gzmb, either in the secreted SMAPs or in the CTLs. It was likely that molecules such as Prf1 and Gzmb were assembled into SMAPs and waited to be released in the secretory lysosome of CTLs. The distribution of TSP-1 in SMAP was also consistent with the glycoprotein shell, which turned out to be a component of the SMAP glycoprotein shell. All these results indicated the specific construction of SMAP—a mixed particle with a diameter of about 120 nm consisting of a dense shell with TSP-1, and a core structure containing Prf1, Gzmb, and other cytotoxic substances.In summary, the study by Bálint and coworkers innovatively illustrated how CTLs attack the target cells by secreting SMAPs containing TSP-1 and deadly chemicals such as Prf1 and Gzmb. They revealed the details and mechanisms that CTLs kill target cells, further enhancing our understanding of CTLs. Besides, SMAPs contain many immune regulatory factors that may regulate the immunity in the tumor microenvironment. The key protein TSP-1 has a binding site for “don’t eat me” signal CD47 to myeloid cells, so that any cell escaped from SMAPs may be culled by phagocytosis. All of these findings provide implications for developing new ideas for cancer immunotherapy.


Deep learning models based on medical images play an increasingly important role for cancer outcome prediction. The standard approach involves usage of convolutional neural networks (CNNs) to automatically extract relevant features from the patient’s image and perform a binary classification of the occurrence of a given clinical endpoint. In this work, a 2D-CNN and a 3D-CNN for the binary classification of distant metastasis (DM) occurrence in head and neck cancer patients were extended to perform time-to-event analysis. The newly built CNNs incorporate censoring information and output DM-free probability curves as a function of time for every patient. In total, 1037 patients were used to build and assess the performance of the time-to-event model. Training and validation was based on 294 patients also used in a previous benchmark classification study while for testing 743 patients from three independent cohorts were used. The best network could reproduce the good results from 3-fold cross validation  in two out of three testing cohorts . Additionally, the capability of the models for patient stratification into high and low-risk groups was investigated, the CNNs being able to significantly stratify all three testing cohorts. Results suggest that image-based deep learning models show good reliability for DM time-to-event analysis and could be used for treatment personalisation. Over the past years, several biomarkers (e.g. human papilloma virus (HPV) status or positron emission tomography based hypoxia levels) have been proposed to help clinical decision making for improved management of certain cancers2 and has been shown to be able to unravel tumor phenotype in multiple studies6. The typical radiomics workflow involves imaging of the patient, identification of the gross tumor volume (GTV), conversion of images to higher dimensional data (i.e. radiomic features) and the subsequent integration and mining of these data for model building. Thus, radiomics allows to build diagnostic, prognostic and predictive models for clinical outcomes using imaging data which is acquired as a part of clinical routine7. However, several challenges have to be faced during radiomic signature development such as issues in reproducibility, standardisation in both the image acquisition, the handcrafted feature extraction and the statistical model building, and other limitations and pitfalls8. For instance, Welch et al.9 have shown that a previously developed set of radiomic features was a surrogate for tumor volume, highlighting the need for simple baseline models to be compared with advanced radiomic signatures.Radiomics relies on non-invasive biomarkers based on advanced imaging analytics10. In the past few years, a sub-field of machine learning called deep learning11 has been widely and successfully adopted in a variety of fields. For medical applications, convolutional neural networks (CNNs) have been extensively used as they take spatial information into account and are therefore the preferred architecture for image-related tasks12. Compared to traditional radiomics, deep learning based radiomics approaches exploit the inherent non-linearity of deep neural networks to learn relevant features automatically13. This enables end-to-end analysis as the contoured or cropped images are given to the algorithm which outputs directly the predictions, therefore skipping the step of handcrafted feature extraction and the efforts connected with it.Conventional radiomic models are built using machine learning algorithms: for binary classification tasks (e.g. whether the patient survives or not) typically random forests, support vector machines or artificial neural networks (ANNs) are used, while for survival analysis  Cox proportional hazards regression, random survival forests, and support vector survival methods are commonly used14 or extranodal extensions in head and neck squamous cell carcinoma15, fully-automated localisation and segmentation of rectal cancer16 and mortality risk stratification for lung cancer patients17 to name a few. Within radiology and radiotherapy, examples are detection of mammographic lesions18, cone-beam computed tomography (CT) intensity correction19 or synthetic CT generation from magnetic resonance images21. Also they have been shown to either equal or outperform their engineered features counterparts in many classification tasks. For instance Diamant et al.22 could show that a CNN trained from scratch on pre-treatment CT images of head and neck cancer patients outperformed a traditional radiomics model developed in a previous study5 using exactly the same CT images. In both studies distant metastasis (DM), loco-regional failure and overall survival were used as endpoints for 300 patients available on the cancer imaging archive (TCIA)24 and coming from four different hospitals in Quebec, Canada. Compared to dichotomised classification, only few deep learning based survival analysis models have been applied to the field of medical imaging, although several models have already been proposed27. Katzman et al.26 implemented DeepSurv, a deep neural network which outputs a single number, that is, the log-risk function of a Cox proportional hazards model allowing for personalized treatment recommendations. Gensheimer et al.27 proposed Nnet-survival, a scalable discrete-time survival model for neural networks capable of outputting survival curves for every patient in a given time span.Deep learning based models have been proven successful in a wide range of medical applications including classification of skin cancer28. Prognosis of these cancers depends on several factors including tumour site, TNM-stage, extracapsular nodal extension and HPV status, with 5-year survival rates that have been shown to vary from 90% for HPV29. Therefore, models that are able to identify high and low risk patients and that can support clinical decision making are highly desirable and could for instance be used for dose de-escalation in radiotherapy, with the potential of decreasing long-term toxicity.Head and neck cancers are a set of very heterogeneous malignancies which are diagnosed worldwide more than 830,000 times and lead to more than 430,000 deaths every year23 and replicated the results obtained by Diamant et al. on this cohort using cross validation (CV) on 294 patients with a 2D-CNN. Additionally, we performed CV with a 3D-CNN, in order to exploit the full volumetric information of the tumour. Then, we downloaded two additional head and neck cancer cohorts from TCIA, 136 patients from MAASTRO clinic in the Netherlands30 and 497 patients from Princess Margaret Cancer Centre (PMH) in Canada31 and obtained an additional cohort of 110 patients from Centro di Riferimento Oncologico (CRO) in Italy as a third independent testing cohort. This allowed us to perform a retrospective multicentric study with 1037 CTs of different head and neck cancer patients in total. An overview of the workflow and the cohort subdivision for training, validation and testing is shown in Figure With this work we wanted to assess the ability of CNN based approaches to accurately predict the occurrence of DM on several independent testing cohorts. To do so, we first downloaded the same 300 patients available on TCIA27 we have therefore constructed a deep learning framework which incorporates censoring information and is capable to output in an end-to-end fashion DM-free probability curves as function of time for every patient given the contoured pre-treatment CT image of the corresponding patient. Throughout this study, we also compared the performance of the CNNs with the performance of an ANN based on multiple clinical variables such as overall TNM stage, tumor site and volume to assess whether the CNN outperforms more classical prognostic models when it comes to discriminative performance or patient stratification. We also combined the image based CNNs with the clinical covariates based ANN in what we call CNN+Clinical, to see if a combined input could improve overall performance. Finally, to gain a better understanding of the importance of texture for the CNN prediction we performed a binary masking experiment with the input GTV and compared the performance on the testing sets with the performance achieved when using the standard image input.Additionally, we extended the task of the network from binary classification to time-to-event analysis as no CNN being able to predict DM risk was found in literature. By combining Diamant’s network in 2D and 3D with the survival model by Gensheimer et al.22 is able to transfer its predictive performance to independent testing sets we first had to replicate the results obtained for the validation. For this, we built a 2D-CNN as well as 3D-CNN for classification and performed 3-fold CV on the same patient cohort used in the benchmark study. In general, the area under the curves (AUCs) obtained with the CNNs were smaller or in line with the ones obtained by Diamant et al. , we gave the networks a binary image, i.e. the GTV with all values inside set to mentclass2pt{minim32 as it incorporates censoring in the computation of the metric and is therefore well suited to assess performance for this task.To evaluate the performance of time-to-event models on different cohorts we used HCITable Of interest is also the fact that when performing the binary masking experiment the HCI for the 3D-CNN and for the 3D-CNN+Clinical decreased by no more than 0.03 (i.e. not significantly). A more substantial drop  was observed for the 2D-CNN and the 2D-CNN+Clinical.In addition to the assessment of the discriminative power according to HCI, we also analysed patient stratification capability of the time-to-event networks. To do so, we found an optimal threshold to split the CV cohorts into high- and low-risk patients and then applied this threshold to the three independent testing cohorts. To infer whether the stratification on the testing cohorts was significant we applied the log rank test on the two patient groups. The resulting p value and corresponding Kaplan–Meier plots for the two groups are shown in Fig. As can be seen in Fig. 22 was chosen as a starting point, also making use of public data on the TCIA24. Compared to Diamant et al. we used a 3D network in addition to a 2D network and both the primary and the lymph node GTVs instead of only the primary GTV. When assessing whether this increase in complexity was justified based on the CV cohort, we observed an average increase in performance when going from the 2D-CNN to the 3D-CNN  can be transferred to independent testing sets we gathered three additional data sets from three different institutions in North America (PMH) and Europe  and applied our trained models to them Fig. . As can To see if combined clinical and image information leads to better performance we extended the 2D and 3D CNNs to include clinical covariates but obtained no improvement in performance when looking at testing cohorts. It should be noted that we only used 7 clinical variables as these were the only ones available in all 4 cohorts. Using more covariates might increase the performance of the ANN and the CNN+Clinical.27. This allowed for incorporating censoring information in the training process and enabled output of DM-free probability curves at different time points for every patient  and a HCI of 0.69 (83% CI 0.64–0.73). Considering the substantial GTV volume difference between the other cohorts and PMH  or genomic biomarkers can enhance the performance of deep learning algorithms for binary classification or time-to-event prediction.The main drawback of this study consists in its retrospective nature. Consequently, not all clinical background information was available. For example, for many patients there is uncertainty on whether they received surgery after the primary radiotherapy treatment . All patients were treated with either radiation alone or with chemo-radiation as primary treatment and no patient received surgery. This cohort was obtained via a collaboration and can be shared upon request.For all patients, the pre-treatment CT and primary (GTVp) and lymph node (GTVn) gross tumor volumes contoured by expert physicians of the corresponding hospitals were available. For all cohorts but PMH the GTVns were clearly labeled in the RTSTRUCT. For PMH every region of interest was contoured, thus to select the GTVn we looked into every patient manually and labeled as GTVns those nodes which were inside the clinical target volume. After that, under consultation of an expert physician in our department, we excluded all previously selected lymph nodes which had a volume smaller than 2 cmFor this study, 4 different cohorts totalling 1037 patients with head and neck cancer were used. All patients selected for this study received either radiation alone or chemo-radiation as main treatment and have a follow-up time larger than 2 years. All patients which had a metastasis at time of diagnosis were excluded.The Canadian benchmark cohort, MAASTRO and PMH are publicly available datasets and were retrieved from TCIA in anonymised form. The CRO patient data was analysed retrospectively, in anonymised form and are part of two studies approved by the Unique Regional Ethics Committee, with following approval numbers: CRO-2017-50 and CRO-2019-66. All methods were carried out in accordance with relevant guidelines and regulations. Informed consent was obtained from all patients.22. As depicted in Fig. 11 and drastically reduces the number of parameters, and a parametric rectified linear unit (PReLu) as non-linear activation function , forms the output of the network For the binary classification network this is a single number between 0 and Both our 2D-CNN and our 3D-CNN architectures are inspired by the 2D-CNN built by Diamant et al.22 are that we used both a 3D-CNN and a 2D-CNN and that we extended the CNNs with the survival model by Gensheimer et al.27 to be able to incorporate time-to-event information.The main differences of our network with respect to the benchmark study22. The input for the 3D-CNN was the full 3D image of n_time_intervals, i.e. to the number of time intervals for which the network outputs an event probability  one-hot encoded clinical covariates. The resulting vector is then fed to the remaining architecture with fully connected layers exactly as for the normal CNN Fig. .Figure 422 achieve good prediction results, they are limited by the fact that time-to-event information is discarded during model training. Especially in some situations, for instance when predicting death or metastasis occurrence for older patients, it is fundamental for treatment customisation to know how fast the event would occur. Additionally, binary classification models usually are trained on a specific time point (e.g. 2-year OS) so they would need to be re-trained to make predictions for a different one27.To overcome these issues we extended Diamant’s network to include time-to-event information by incorporating the survival model by Gensheimer et al.27. Their discrete-time survival model has been implemented in Keras37  and allows to train deep neural networks while taking into account patient follow-up times and outputs discrete event probability/survival curves for each patient. To our knowledge, the only applications of Gensheimer’s model to the field of medical imaging are the study by Zhang et al.38, who recently showed that their CT-based CNN survival model was able to outperform a Cox proportional hazards model based on radiomic features for pancreatic ductal adenocarcinoma and the study by Kim et al.39, who developed a CT-based deep learning model which successfully predicts disease-free survival for lung adenocarcinoma patients.Although binary classification models as the ones built insurv_likelihood provided by the authors is used. At architecture level, the number of neurons in the final connected layer has to be changed in order to match the desired number of output time intervals  function to compute the concordance between a patient’s metastasis-free probability after 3 years and the patient’s ground truth event time. Following this definition, a perfect HCI for our model would be 1.0, as patients with e.g. a low value for metastasis-free probability should be patients with a short time-to-event.To evaluate the performance by taking into account time-to-event information (conversely to the AUC) we used HCI40 using the high-level library Keras37. Training and testing was carried out on three different graphical processing units: a NVIDIA P6000 with 24 GB, a NVIDIA P5000 with 16 GB and a NVIDIA Titan V with 12 GB of memory. The input shape of the 3D CT of 41 with a constant learning rate of surv_likelihood by Gensheimer et al.27.All the code needed for data pre-processing and running the models was written in Python 3.6. The networks were built and optimized with Tensorflow 2.2.042. The full CTs where interpolated in 3D using linear interpolation. Moreover, all CT values were windowed to Prior to the augmentations, all images were isotropically re-sampled to a 43 (https://github.com/MIC-DKFZ/batchgenerators) to our workflow. This allowed us to use a slightly wider range of augmentations and especially to perform the augmentations online (during the training), in contrast to Diamant’s study. To be specific, we used random cropping to 128 pixels in each dimension with a central random shift of maximal 20% of the original dropout_rate of 25%. For the CNNs all these hyper-parameters were found by performing a manual grid search meaning that we repeated the 3-fold CV on the Canadian benchmark data set until we were satisfied with the mean validation AUC or HCI which was achieved with given parameters. On the other hand, training for the ANN took much less, so after manually finding a range of parameters leading to good results we performed an automatic grid search over 16 different combinations of hyper-parameters  to find the ones performing best on average for the 3-fold CV. For all CNN+Clinical models we manually explored some hyper-parameters around the best hyper-parameters for the CNNs and found that the ones leading to best average CV were the same as for the CNN.To decrease over-fitting, a weight decay term of For all our network architectures we took the 3 models achieving the best average CV performance and used them for testing without any further change. To get a single prediction out of three we applied the validation models on test data and then averaged the resulting three predictions to obtain a single model averaged prediction per patient. A schematic view of the workflow is shown in Fig. When doing binary classification the 3D-CNN and 3D-CNN+Clinical were trained for 500 epochs, using Keras callbacks to save the model’s weights when an improvement in the AUC was observed. One 3-fold CV took on average 11 h. The 2D-CNN and 2D-CNN+Clinical were trained for 500 epochs, one 3-fold CV taking on average 30 minutes. The ANN was trained for 5000 epochs, one 3-fold CV taking on average 10 minutes. When performing time-to-event analysis the 2D and 3D CNNs and CNNs+Clinical were trained for 300 epochs while the ANN for 3000, taking on average 8 h for the 3D-CNNs, 20 min for the 2D-CNNs and around 10 min for the ANN. For all the networks and the tasks, we used early stopping with a patience of 200 for all CNNs and 2000 for the ANN to shorten the overall training time.44 (AUC) for binary classification tasks or Harrell’s concordance index32 (HCI) for time-to-event tasks and by looking at patient stratification capability using a threshold optimised on the validation cohort. To find the threshold we first averaged over the risk of all validation patients who got a DM, then we averaged over the risk of all the validation patients which did not get a DM and finally we took the mean of these two thresholds for all three CV models and obtained a final model averaged threshold to be used for stratification of the testing sets.The performance of the different models on the different testing cohorts was investigated in a two-fold way: by looking at the discriminative power using the area under the receiver operating characteristic curve46 we assessed significance of difference by using both estimation statistics and statistical tests. We applied estimation statistics45, i.e. we focused on the sizes of effects at level of data, by computing the median AUC or HCI with 83% confidence intervals from bootstrap re-sampling . We used 83% confidence as it can be shown48 that two non-overlapping 83% confidence intervals mean that the two corresponding means/medians differ significantly with a significance level of 0.05. To be in line with literature17, we also split the testing patients into a high- and low-risk group and checked whether the stratification is significant by computing the p value for the log-rank test. We consider results with p values Following the suggestions bySupplementary Information 1.


The introduction of adrenocortical extract in 1930 improved the life expectancy of hyhpoadrenal patients, with further increases seen after the introduction of cortisone acetate from 1948. Most patients are now treated with synthetic hydrocortisone, and incremental advances have been made with optimisation of daily dosing and the introduction of multidose regimens. There remains a significant mortality gap between individuals with treated hypoadrenalism and the general population. It is unclear whether this gap is a result of glucocorticoid over-replacement, under-replacement or loss of the circadian and ultradian rhythm of cortisol secretion, with the risk of detrimental excess glucocorticoid exposure at later times in the day. The way forwards will involve replacement of the diurnal cortisol rhythm with better glucocorticoid replacement regimens. The steroid profile produced by both prednisolone and dual-release hydrocortisone (Plenadren), provide a smoother glucocorticoid profile of cortisol than standard oral multidose regimens of hydrocortisone and cortisone acetate. The individualisation of prednisolone doses and lower bioavailability of Plenadren offer reductions in total steroid exposure. Although there is emerging evidence of both treatments offering better cardiometabolic outcomes than standard glucocorticoid replacement regimens, there is a paucity of evidence involving very low dose prednisolone (2–4 mg daily) compared to the larger doses (~7.5 mg) historically used. Data from upcoming clinical studies on prednisolone will therefore be of key importance in informing future practice. Between 1928 and 1938, patients with Addison’s disease had a 100% 5-year mortality , sampling cells at 08:00 h and 20:00 h  which dimerises with brain–muscle–arnt-like protein 1(BMAL1). The CLOCK/BMAL1 dimer in turn binds to enhancer sequences in the DNA of cells to promote transcription of period genes  and cryptochromes (CRY1 and CRY2) . The ‘maet al. recruited 16 healthy males, collecting PBMCs at baseline and at 6 days after the participants had taken hydrocortisone 20 mg orally every evening, 10 h post awakening  pumps, may offer a more physiological alternative cortisol replacement therapy , particuDual-release hydrocortisone (herein referred to as Plenadren), and prednisolone both offer a once-daily solution to glucocorticoid replacement therapy. Apart from the convenience and improved adherence to treatment with once-daily dosing, both drugs produce a smoother plasma profile  37, 38), 3837, 3Plenadren is a dual-release formulation of hydrocortisone containing both immediate release and sustained release hydrocortisone in a single tablet. Therefore it is designed to give a smoother glucocorticoid profile than standard- release hydrocortisone . It is a0–24 h, for Plendaren compared to standard release hydrocortisone. There was a 6.4% increase in AUC0–4 h, with subsequent reductions of 30.5 and 58.8% for AUC4–10 h and AUC10–24 h, respectively. This amounts to both, a drop in total steroid exposure and a specific reduction in the amount of steroid exposure in the afternoon and evenings. There were significant decreases with Plenadren in body weight, systolic blood pressure and diastolic blood pressure although this was tempered slightly by a significant increase in heart rate and fasting triglycerides. Further possible benefits were seen, with reductions in HbA1c by 0.1% and increase of 6.1 µg/L in procollagen type 1 N-terminal propeptide (P1NP), a bone turnover marker of osteoblastic activity. Subjective quality of life outcomes also favoured Plenadren over standard hydrocortisone. Overall, Plenadren demonstrated superiority over thrice-daily hydrocortisone in term of cardiovascular risk factors and bone health markers.In an open-label study, 64 primary hypoadrenalism patients took 3 months of thrice-daily hydrocortisone and once-daily Plenadren in a randomised crossover protocol. Patients were converted from their daily cumulative dose of thrice-daily hydrocortisone to the same total daily dose of Plenadren, administered once a day in the morning . PharmacIn an extension of the aforementioned study, 55 of the original 64 patients continued on Plenadren for up to 24 months after the end of the randomised crossover phase. An additional 16 patients were recruited into this study, who had not participated in the crossover trial and took Plenadren for up to 18 months . The safIn a further study, 19 individuals with Addison’s disease were switched from 20 mg standard hydrocortisone in divided doses to Plenadren 20 mg once daily and evaluated over a 12-month period . Over 1 There is also evidence of other metabolic benefits including improvement of hepatic steatosis . In 45 pThe effects of Plenadren on blood pressure are difficult to interpret. Ten patients out of 17, stably replaced with cortisone acetate and diagnosed with adrenal insufficiency, were converted to Plenadren in a retrospective, case-control analysis . When paA study retrospectively collected data on 14 patients who had dual energy X-ray absorptiometry (DEXA) imaging before and after switching to Plenadren . All patIn the DREAM study, 46 patients with primary or secondary hypoadrenalism were switched from multiple daily doses of either cortisone acetate or hydrocortisone to Plenadren and were compared to 43 patients who continued on their standard regimen, as well as 25 healthy controls . PatientA DREAM ancillary study looked at PBMC clock gene expression in 26 of the Plenadren group, 29 of the standard treatment group and 16 of the healthy controls . At basePlenadren leads to an approximate 20% reduction in cortisol exposure in comparison to dose-matched standard hydrocortisone . It is dDue to its formulation, Plenadren appears to be more vulnerable to malabsorption during intercurrent gastrointestinal disease. The sustained release preparation requires continued absorption of hydrocortisone from the gut for several hours, leading to diarrhoea as a common side effect. In addition to the caution for using Plenadren in chronic diarrhoea as noted above, there is a specific risk of hospitalisation with acute gastroenteritis  warrantiIn vitro studies have demonstrated that prednisolone has a greater affinity for the GR and mineralocorticoid receptor (MR)  . When cotor (MR) . It is d2 of prednisolone once-daily, whilst the hydrocortisone group received 10–15 mg/m2 in keeping with the purported bioequivalence ratio of 4:1. In order to normalise the participants’ biochemical, clinical and anthropometric markers over the course of this 1-year study, the researchers were forced to reduce the amount of prednisolone prescribed on the study to 1.8–3.0 mg/m2, whilst the patients on hydrocortisone required an increase in dose to 12–20 mg/m2. The data from this study indicates that the potency of prednisolone may be as high as six to eight times greater than hydrocortisone, so that 3 mg of prednisolone is equivalent of 20 mg of hydrocortisone.The C1–C2 double bond endows prednisolone with a longer half-life of up to 3.2 h, and an increased potency when compared to cortisol . It has These findings raise questions about the true significance of studies comparing steroid replacement regimens where the ratio of 4:1 was used. In one study comparing prednisone 7.5 mg daily with hydrocortisone 30 mg daily, no difference was found between both groups in bone density . In a raArriving at the minimum required dose to maintain a patient with hypoadrenalism is difficult in the absence of established biomarkers. Slowly reducing the steroid dose can be dangerous but was inadvertently undertaken in a patient with secondary hypoadrenalism, where a replacement dose of 3 mg was found to be optimal . There hData comparing 82 patients on hydrocortisone and 64 on very low-dose prednisolone has shown no difference in most anthropometric and biochemical markers of metabolic risk, such as weight, blood pressure, lipid profiles including LDL and HDL, fasting glucose or HbA1c . Waist-hPrednisolone is particularly useful in adrenal insufficiency secondary to long-term steroid use. The same principles apply in inflammatory conditions where a slow wean of prednisolone is required in order to avoid resurgence of the initial condition . In thesAlthough both prednisolone and hydrocortisone feature on the World Health Organisation list of essential medicines, prednisolone is more widely available. Higher doses of prednisolone are used for a number of anti-inflammatory indications. As a result, patients with hypoadrenalism are routinely managed with prednisolone in many countries, but at default doses of 5 mg that are higher than the 2–4 mg which limited evidence suggests constitutes adequate replacement. Dissemination of the message that three-quarters of a 5 mg tablet is sufficient for treatment of hypoadrenalism may well have an important impact on the health of patients across the world with hypoadrenalism.Emerging evidence strongly suggests that thrice-daily standard oral hydrocortisone has long-term deleterious effects. Our approach must centre on both preventing over-replacement and ensuring that there is appropriate steroid exposure at the correct times.It may be that both Plenadren and prednisolone offer more suitable glucocorticoid replacement with concurrent cardiovascular, metabolic and immunological benefits, but there is a paucity of evidence directly comparing the two. There is also a lack of research comparing both prednisolone and Plenadren with other modalities of glucocorticoid replacement. Current studies are confounded by the relative differences in potency and dosing. Practically, both drugs offer a once-daily replacement with no current evidence of difference between the two. Further direct comparisons are therefore needed. It is hoped that ongoing trials such as PRED-AID and HYPER-AID will provide this , 68.The authors declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of this review.Health Researchhttp://dx.doi.org/10.13039/100005622 (NIHR), Doctoral Research Fellowship. TT is funded by the NIHR, NIHR BRC and the Moulton Charitable Research Foundation. KM is funded by the NIHR BRC. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care.SC is funded by a National Institute for SC is responsible for the initial draft of this manuscript. TT, KL and KM have reviewed this manuscript and made edits to the text. All authors have approved the final manuscript. All authors have contributed equally.


Does time to clinical improvement or time to death differ among hospitalized patients treated with vs without remdesivir  for coronavirus disease 2019 outside of a clinical trial?In this multicenter comparative effectiveness research study that included 2483 consecutive admissions with a high proportion of non-White individuals, treatment with remdesivir was associated with more rapid clinical improvement than no remdesivir receipt in propensity score–matched controls. The addition of corticosteroids to remdesivir was not associated with improved time to death.Remdesivir administration is associated with more rapid clinical improvement than no remdesivir receipt among patients with coronavirus disease 2019. Clinical effectiveness data on remdesivir are urgently needed, especially among diverse populations and in combination with other therapies.To examine whether remdesivir administered with or without corticosteroids for treatment of coronavirus disease 2019 (COVID-19) is associated with more rapid clinical improvement in a racially/ethnically diverse population.k days of treatment was matched to a control patient who stayed in the hospital at least k days (5 days maximum) beyond the matching day.This retrospective comparative effectiveness research study was conducted from March 4 to August 29, 2020, in a 5-hospital health system in the Baltimore, Maryland, and Washington, DC, area. Of 2483 individuals with confirmed severe acute respiratory syndrome coronavirus 2 infection assessed by polymerase chain reaction, those who received remdesivir were matched to infected individuals who did not receive remdesivir using time-invariant covariates  and time-dependent covariates . An individual in the remdesivir group with Remdesivir treatment with or without corticosteroid administration.The primary outcome was rate of clinical improvement , and the secondary outcome, mortality at 28 days. An additional outcome was clinical improvement and time to death associated with combined remdesivir and corticosteroid treatment.Of 2483 consecutive admissions, 342 individuals received remdesivir, 184 of whom also received corticosteroids and 158 of whom received remdesivir alone. For these 342 patients, the median age was 60 years , 189 (55.3%) were men, and 276 (80.7%) self-identified as non-White race/ethnicity. Remdesivir recipients had a shorter time to clinical improvement than matched controls without remdesivir treatment . Remdesivir recipients had a 28-day mortality rate of 7.7% (22 deaths) compared with 14.0% (40 deaths) among matched controls, but this difference was not statistically significant in the time-to-death analysis . The addition of corticosteroids to remdesivir was not associated with a reduced hazard of death at 28 days .In this comparative effectiveness research study of adults hospitalized with COVID-19, receipt of remdesivir was associated with faster clinical improvement in a cohort of predominantly non-White patients. Remdesivir plus corticosteroid administration did not reduce the time to death compared with remdesivir administered alone. This propensity score–matched multicenter comparative effectiveness research study that included a high proportion of non-White individuals assesses whether remdesivir administered alone or with corticosteroids is associated with time to clinical improvement or time to death in patients hospitalized with confirmed coronavirus disease 2019 (COVID-19). Remdesivir, a nucleotide analogue prodrug with in vitro effects against a broad array of RNA viruses,4 has received considerable attention.6 Clinical trials assessing the effectiveness of remdesivir have yielded conflicting results12; have not included a sufficient number of patients from groups most affected by COVID-19, such as Black or Latinx individuals13; and have not examined the coadministration of remdesivir with other drugs. The US Food and Drug Administration (FDA) approved the use of remdesivir for the treatment of hospitalized patients with COVID-1914 based mostly on the results of the Adaptive COVID-19 Treatment Trial (ACTT-1),11 whereas the World Health Organization (WHO) recommended against the use of remdesivir15 based on results from the Solidarity trial.12The pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to progress, with more than 103 million global cases and more than 2.2 million deaths as of February 2, 2021.16 Black individuals and Latinx individuals have mortality rates that are 2.4 and 1.5 times as high as White individuals, respectively.17 However, only 11% to 20% of participants in remdesivir clinical trials were Black patients, and only 17% to 23% were Latinx patients.10 Because underrepresented minority groups shoulder a disproportionate burden of morbidity and mortality from COVID-19,18 it is critical that we understand the effects of remdesivir in these populations.In addition to uncertainty about efficacy, there are concerns that the demand for remdesivir will outpace supply, raising calls for a transparent and just distribution process.19 Trials examining the benefit of different corticosteroids were stopped early after the results of RECOVERY led to corticosteroids being considered the standard of care.21 Whether there is additional benefit from using both remdesivir and corticosteroids requires further evaluation. We used time-dependent propensity score matching to examine the association of remdesivir administration with clinical improvement in patients admitted to our 5-hospital health system from March 4 through August 29, 2020.The UK-based RECOVERY  trial showed that dexamethasone administration compared with placebo led to a significant reduction in mortality rate (22.9% vs 25.7%).ISPOR) reporting guideline for comparative effectiveness research.22 The hospital institutional review boards approved this study as minimal risk and waived informed consent requirements. No one received compensation or was offered any incentive for participating in this study.This comparative effectiveness study was conducted at 5 hospitals  that comprise the Johns Hopkins Medicine System, with 2513 beds serving approximately 7 million persons. All patients consecutively admitted with SARS-CoV-2 infection confirmed by polymerase chain reaction testing from nasal, oropharyngeal, or bronchoalveolar lavage samples between March 4 and August 29, 2020, were eligible. This study was conducted according to the International Society for Pharmacoeconomics and Outcomes Research  and have an alanine aminotransferase level less than 5 times the upper reference limit. Patients who were intubated at the end of a 5-day treatment could receive an additional 5 days if they had not improved. Treatment was stopped if the alanine aminotransferase level increased more than 5 times the upper reference limit or if there were other signs or symptoms of liver toxicity. Physicians were encouraged to stop remdesivir if the creatinine clearance decreased by more than 50% with no alternative explanation. Patients who received at least 1 dose of remdesivir outside of a clinical trial were considered to have been treated with remdesivir.The health system remdesivir policy followed the guidance from the US FDA initial Emergency Use Authorization.26 Failure of clinical improvement was censored at the last day of follow-up or 28 days, whichever came first. Death was also censored at 28 days. The secondary outcome was time to death from the first remdesivir treatment day. Patients who were discharged alive were censored at 28 days to account for death and discharge being competing risks, as previously described.27 An additional secondary outcome was the time to clinical improvement or to death following administration of both corticosteroids and remdesivir.The primary outcome of interest was time to clinical improvement from the start of remdesivir, a composite outcome defined as discharged alive from the hospital without worsening of WHO disease severity score or at least a 2-point decrease in WHO severity score during hospitalization within 28 days or maximum follow-up.29 Patients were included in matching if their admission dates were later than the earliest admission date  among patients in the remdesivir treatment group. In addition, a time constraint was imposed so that a patient in the remdesivir group with k days of treatment was matched to a patient in the control group who stayed in the hospital at least k days (5 days maximum) beyond the matching day. The last condition avoided matches in which the control patient was healthy enough to be discharged soon after the matched day because those patients would not be similar to patients who started remdesivir , and laboratory results were included in the Cox proportional hazards regression models based on clinical interest and knowledge . To acco11 inclusion/exclusion criteria to select qualified patients for matching; (3) decreasing the number of days that matched controls had to remain in the hospital after the matched day from a maximum of 5 days to either 4 or 3 days; and (4) categorizing clinical improvement using a 1-point decrease in WHO severity score instead of a 2-point decrease.The analyses were repeated using the same propensity score–matching method but with the following conditions: (1) excluding remdesivir-treated patients who also received corticosteroids; (2) applying the ACTT-130 , participation in randomized clinical trials of remdesivir (n = 12), and being treated with remdesivir at the date of censoring (n = 4) . Among tOf 570 matched individuals (285 remdesivir and 285 matched controls), 236 (82.8%) patients who received remdesivir and 213 (74.7%) controls achieved clinical improvement before 28 days, with a median time to clinical improvement of 5.0 days  for remdesivir recipients and 7.0 days  for controls. In Cox proportional hazards regression models, remdesivir treatment was associated with significantly shortened time to clinical improvement  A. PatienRemdesivir recipients had a 28-day mortality rate of 7.7% (22 deaths) compared with 14.0% (40 deaths) for matched controls, but this difference was not statistically significant in the time-to-death analysis  A. The meWe considered only 306 remdesivir recipients who met inclusion criteria for ACTT-1 for the sensitivity analysis . Although remdesivir administration was still associated with a significant decrease in the time to clinical improvement if the requirement was reduced to 4 days , the estimated aHR decreased to 1.07  if the required period of hospitalization was lowered to 3 days or less. This finding occurred because 87 patients in the control group had their event within 4 days of matching  compared with 26 patients in the treatment group .We repeated the time-to-improvement analyses using a 1-point improvement in the WHO severity score, and the results did not appreciably change  who did not complete at least 5 days of remdesivir administration, 21 were discharged from the hospital, and 2 died before completing treatment. For the remaining patients, treatment was stopped for the following reasons: increased levels of liver enzyme or bilirubin (n = 4), kidney failure of unclear cause (n = 2), nausea (n = 1), epistaxis and tachycardia (n = 1), neck and mouth itching (n = 1), and transition to comfort care (n = 1). The incidence of liver enzyme levels above 200 U/L , bilirubin levels above 2 mg/dL , and estimated glomerular filtration rate less than 30 mL/min/1.73 m31 In our retrospective multicenter study, receipt of remdesivir was associated with a significantly shorter time to clinical improvement. The mortality rate was lower in the remdesivir group, but the results were not statistically significant. The magnitude and direction of these associations were similar to those shown in ACTT-1.32Optimal implementation of therapeutics to decrease COVID-19 morbidity and mortality is a global priority. Remdesivir has been the subject of controversy since ACTT-1, sponsored by the National Institutes of Health, showed a shortened time to clinical improvement, but the larger Solidarity study, sponsored by the WHO, did not show a mortality benefit. Although the results of Solidarity likely indicate that remdesivir alone does not have a robust mortality benefit for patients with COVID-19, remdesivir may still have an important role to play in reducing duration and severity of illness, both important outcomes when hospitals are overwhelmed with patients having COVID-19.32 Because underrepresented minority groups have shouldered a disproportionate burden during the COVID-19 pandemic but have not been widely represented in clinical trials,13 our results provide important evidence that receipt of remdesivir is associated with decreased time to clinical improvement in these populations.Our study included a much higher percentage of patients from underrepresented minority groups than previous remdesivir clinical trials. Approximately 80% of patients in our cohort were non-White individuals compared with 30% to 47% in clinical trials.9 and an open-label study found that a 5-day course, but not a 10-day course, was associated with a significant improvement in disease severity.10 Our finding that a 5-day treatment course was associated with a clinical benefit is important, particularly given reports of both US and global shortages of remdesivir and the potential for even greater supply constraints moving forward.33The vast majority of patients in the remdesivir group received 5 days of therapy, which supports recommendations for an initial 5-day course for most patients. Although ACTT-1 used a 10-day treatment course, a comparison of 5 days vs 10 days showed similar efficacy,Our cohort included several groups of individuals who were not eligible to receive remdesivir in prior clinical trials, including 9 patients who were pregnant, 2 patients who were younger than 18 years, and 16 patients who showed evidence of chronic kidney disease at baseline. Inclusion of these patients was not associated with an increased incidence of significant adverse effects as evidenced by the small percentage of patients in our cohort who stopped treatment early (2.9%) compared with ACTT-1 (9%).19 Removing patients who also received corticosteroids did not appreciably change the overall association of remdesivir with time to clinical improvement.We also took into account the increased use of dexamethasone  after the RECOVERY trial.19 and small numbers of events may have prevented us from detecting a benefit. The increased median time to death and the higher number of severely ill patients in the combination group suggest that the combination group was more severely ill and may have derived some benefit from the addition of corticosteroids that was not accounted for in the marginal structural model.Although there was no significant reduction in the hazard of death for patients who received both remdesivir and corticosteroids, our low baseline mortality rate (6.3% for remdesivir-only patients) compared with the RECOVERY trial (26% for controls)32 Although our study showed a significant association of remdesivir with time to clinical improvement in both groups, the 95% CI in the severely ill group approached 1, suggesting perhaps a more robust benefit for patients with mild to moderate disease. By contrast, the RECOVERY trial showed that only patients receiving supplemental oxygen or additional respiratory support benefitted from dexamethasone, whereas patients breathing ambient air did not.19 Although ACTT-4 will provide some insight into the effectiveness of remdesivir combined with dexamethasone, the comparator group will be remdesivir in combination with baricitinib, and the results will not be available for several months.34 The combination of corticosteroids and remdesivir in specific target populations and the timing of coadministration warrant additional study.In ACTT-1, patients breathing ambient air or nasal cannula oxygen benefitted from treatment with remdesivir, whereas patients receiving higher levels of respiratory support, such as mechanical ventilation, did not benefit.32 Among the unmatched control patients, 596 experienced their outcome within 5 days of admission, with 578 (97%) being discharged from the hospital. The matching assignment is therefore unlikely to bias the results toward the null hypothesis. We were likely underpowered to detect a mortality difference between patients receiving remdesivir and matched controls. There was a trend toward a reduction in mortality associated with remdesivir administration that was similar in magnitude to that shown in ACTT-1, but neither study result reached statistical significance.Our study has limitations. The time-dependent propensity score method produced matched sets in which patients were very similar on measured confounders. However, there could be unmeasured variables that biased our treatment effect estimates. It is possible that there was a secular trend in the quality of COVID-19 patient care as our health system gained experience; thus, we limited our matched control group to the period when remdesivir was available. We required controls to remain hospitalized for the same amount of time as patients who received remdesivir treatment, not exceeding 5 days. This criterion was a prespecified analysis design to avoid bias from matching controls with different disease severity that could not be controlled for by the measurement confounders. However, our findings were sensitive to this restriction being reduced to 3 or fewer days. This protocol is consistent with ACTT-1, which excluded patients expected to be discharged from the hospital within 3 days of randomization.This study suggests that remdesivir was associated with a significant decrease in the time to clinical recovery among patients admitted to the hospital for treatment of COVID-19. These results provide further evidence that remdesivir may be effective in reducing the duration of COVID-19 illness, that a 5-day treatment course may be sufficient, and that patients with milder disease likely benefit most. The inclusion of a larger proportion of patients from underrepresented minority groups provides much-needed evidence suggesting the effectiveness of remdesivir administration in these groups. The combination of remdesivir and corticosteroids was not associated with reduced mortality, suggesting that additional studies assessing patients with COVID-19 are warranted.


Several pain management guidelines recommend regular urine drug testing (UDT) in patients who are being treated with chronic opioid analgesic therapy (COAT) to monitor compliance and improve safety. Guidelines also recommend more frequent testing in patients who are at high risk of adverse events related to COAT; however, there is no consensus on how to identify high-risk patients or on the testing frequency that should be used. Using previously described clinical risk factors for UDT results that are inconsistent with the prescribed COAT, we developed a web-based tool to adjust drug testing frequency in patients treated with COAT.The objective of this study was to evaluate a risk stratification tool, the UDT Randomizer, to adjust UDT frequency in patients treated with COAT.Patients were stratified using an algorithm based on readily available clinical risk factors into categories of presumed low, moderate, high, and high+ risk of presenting with UDT results inconsistent with the prescribed COAT. The algorithm was integrated in a website to facilitate adoption across practice sites. To test the performance of this algorithm, we performed a retrospective analysis of patients treated with COAT between June 2016 and June 2017. The primary outcome was compliance with the prescribed COAT as defined by UDT results consistent with the prescribed COAT.P=.10), high risk , and high risk+  categories were associated with a nonsignificantly increased risk of inconsistency vs the low risk category.979 drug tests  were performed in 320 patients. An inconsistent drug test result was registered in 76/979 tests (7.8%). The incidences of inconsistent test results across the risk tool categories were 7/160 (4.4%) in the low risk category, 32/349 (9.2%) in the moderate risk category, 28/338 (8.3%) in the high risk category, and 9/132 (6.8%) in the high+ risk category. Generalized estimating equation analysis demonstrated that the moderate risk (odds ratio (OR) 2.1, 95% CI 0.9-5.0; The developed tool stratified patients during individual visits into risk categories of presenting with drug testing results inconsistent with the prescribed COAT; the higher risk categories showed nonsignificantly higher risk compared to the low risk category. Further development of the tool with additional risk factors in a larger cohort may further clarify and enhance its performance. Despite a decline in opioid prescriptions since the height of the opioid crisis in the United States, the use of opioids for the treatment of chronic pain continues to be common, particularly among primary care physicians . ChronicUrine drug testing (UDT) has been suggested by several guidelines as a method to observe compliance with the prescribed therapy in patients treated with COAT -8. GuideSeveral readily available treatment-related factors are known to be associated with an increased risk of UDT results that are inconsistent with the prescribed COAT. These factors include younger age ,13, concThis study was conducted in a private interventional pain management institute with 7 specialists across 4 different locations in the New York City area. We retrospectively identified patients without cancer who had chronic pain that was treated with COAT by reviewing charts between June 1, 2016 and July 1, 2016. Visits from the 12 months following the initial visit in June 2016 were reviewed for UDT results and for their consistency with the prescribed opioid therapy. The UDT Randomizer risk categories associated with each UDT result were also obtained. The UDT Randomizer risk stratification tool was implemented as part of the standard of clinical care at the institute in March 2016 and had thus been part of normal practice for some time prior to the inclusion date. Inclusion criteria for the study were age ≥18 years and treatment with opioids (extended release or immediate release) for more than 12 consecutive weeks at the start of the retrospective inclusion period. We allowed for a gap period of up to 4 weeks in opioid treatment. The underlying cause of chronic pain was retrieved from each patient’s medical record, and patients with pain due to cancer were excluded. The Staten Island University Institutional Review Board approved this study (study number: 18-0906-SIUHN) and waived the requirement to obtain informed consent for this retrospective study.The developed stratification tool is depicted in Patients allocated to the low, moderate, high, and high+ risk categories are randomly requested to undergo UDT at frequencies of 25%, 33%, 50%, and 60%, respectively. It is important to stress that the chance of being requested to participate in UDT is thus not random but is rather random with a certain pre-set probability. We arrived at the testing frequencies through evaluation of the Washington State Agency Medical Directors’ Group Interagency Guideline and American Academy of Pain Medicine recommendations on frequency of testing . We estiThe primary outcome was compliance with the prescribed opioid therapy. This was assessed by the drug test results and their consistency with the prescribed opioids over the study period. A drug test result was considered to be consistent if it was positive for the prescribed opioid or its metabolites and was negative for other opioids, their metabolites, or illicit substances. A drug test result was considered to be inconsistent if it was negative for the prescribed opioid or its metabolites or if it was positive for nonprescribed opioids, their metabolites, or illicit substances. Consistent with recent Centers for Disease Control and Prevention (CDC) guidance , we did Urine toxicology testing was performed by an independent laboratory using liquid chromatography tandem mass spectrometry . If a patient was not able to provide a urine sample, oral fluid was collected for analysis. Both urine and oral fluid samples were examined for the presence of prescribed opioids, benzodiazepines, illicit drugs, and their respective metabolites. Chromatographic tests are specific and are not susceptible to cross-reactions; thus, false positive results are rare . The detPatient demographics, diagnoses, prescribed medications, and drug testing results were collected retrospectively from the patients’ medical records. Pain diagnoses were grouped into categories of lower back pain; cervical pain; arthritis, joint, and muscle pain; and other pain. We retrieved information from all visits in the 12-month period following the initial included visit in June 2016. Because the data analysis was conducted at the individual visit level , we included data regardless of whether the patients remained in our care for the full 12-month period.P values. Statistical significance was set at P<.05. The software package SPSS version 24 (IBM Corporation) was used for all statistical analyses.Demographics and clinical data are presented as mean (SD) or as n (%). To assess the uptake of the UDT Randomizer tool, we analyzed how often the tool was used during the first visit for each patient in the study period. We also assessed how often the tool’s recommendation (Yes or No for UDT) was followed at that visit. Additionally, we assessed how often UDT testing was ordered without recommendation by the tool over the course of the entire study period as well as how often the UDT testing recommended by the tool was ignored by providers over the course of the entire study period. We performed generalized estimating equations (GEE) analysis with the factors “risk category” and “visit” to assess if the assigned risk category was related to the consistency of drug testing results with the prescribed COAT using all tests and risk assignments in the 12-month study period. We used GEE to account for repeated testing in the same patient. To assess our assumption that there is no association between marijuana use and drug testing results inconsistent with the prescribed COAT, we performed GEE analysis with THC status on drug testing as a factor for the consistency of drug testing results with the prescribed COAT as the outcome. The results of the analyses are presented as odds ratios (ORs) with 95% confidence intervals and the corresponding The study population consisted of 320 patients, of whom 172 (53.8%) were female and 148 (46.3%) were male . Most ofWe found that the uptake of the UDT Randomizer tool was high at the first visit in the study period: it was used in 318/320 patients (99.3%), and its recommendation regarding testing was followed 314 of the 318 times it was used (99.7%). Over the course of the entire 12-month study period, the recommendation of the tool to test (“Yes”) was followed in 945/964 (98.0%) of visits. Over the 12-month period, 34 tests were performed contrary to the tool’s guidance to not perform a test.A total of 979 drug tests were performed in the study population over the retrospective 12-month duration of the study. Of the performed tests, 867/979 (88.6%) were urine drug tests, whereas 112 (11.4%) were oral fluid tests. All patients provided at least 1 drug test during the follow-up period. Inconsistent drug test results were registered for 76/979 tests (7.8%) in 52/320 patients (16.3%) during this period. The incidence of inconsistent test results across the UDT Randomizer tool risk categories varied from 4.4% (low risk) to 9.2% (moderate risk), 8.3% (high risk), and 6.8% .Of the 979 drug tests, 119 (12.2%) were positive for THC, and the positive tests were obtained in 25/320 patients (7.8%). GEE analysis with the risk factors “THC” and “visit” did not demonstrate significantly higher risk of drug testing inconsistent with the prescribed COAT when a positive test for THC was also present .Because the ORs appeared to be homogenous among the moderate, high, and high+ categories, we performed a secondary GEE analysis to explore the value of stratifying patients into only 2 risk categories as a potential next step in the development of the UDT Randomizer tool. We combined the previous moderate, high, and high+ categories into one high risk category. The performance of this stratification with regard to the consistency of drug testing with the prescribed opioid therapy was found to be similar to that of the individual categories in the initial 4-category system .Additionally, we explored whether a lower cutoff point of 20 daily morphine milligram equivalents prescribed could improve discrimination by the UDT Randomizer tool. GEE analysis indicated that this cutoff did not perform better than the previous 2-risk category stratification . This percentage is similar to the percentage reported in a recent study by Knezevic et al , in whic%. This pThis was a retrospective study conducted at a single institution. A strength of the study was the prospective effective implementation of the intervention in the institution prior to the evaluation in this study.Drug testing results inconsistent with prescribed COAT have been suggested to serve as an early warning of adverse outcomes of COAT ; therefoThe developed tool stratified patients during individual visits into risk categories of presenting with drug testing results inconsistent with the prescribed COAT; the higher risk categories showed nonsignificantly higher risk than the low risk category. Further development of this tool with additional risk factors in a larger cohort may further clarify and enhance its performance.


Faecal immunochemical testing (FIT) is suboptimal in detecting advanced neoplasia (AN). To increase the sensitivity and yield of a FIT-based screening programme, FIT could be combined with risk factors for AN. We evaluated the incremental yield of adding a family history questionnaire (FHQ) on colorectal cancer (CRC) and Lynch syndrome-associated tumours to the Dutch FIT-based screening programme.Six thousand screen-naive individuals, aged 59–75 years, were invited to complete a FIT  and a validated online FHQ. Participants with a positive FIT and/or positive FHQ, confirmed after genetic counselling, were referred for colonoscopy. Yield of detecting AN per 1000 invitees for the combined strategy was compared with the FIT-only strategy.p = 1.0).Of the 5979 invitees, 1952 (32.6%) completed the FIT only, 2379 (39.8%) completed both the FIT and FHQ and 95 (1.6%) completed the FHQ only. Addition of the FHQ to FIT-based screening resulted in one extra case of AN detected after 16 additional colonoscopies, resulting in a yield of 19.6  for the combined strategy versus 19.5  for the FIT-only strategy . Depending on the faecal haemoglobin concentration cut-off level and brand, pooled sensitivity was 79% for CRC and 6–56% for AN.6Faecal immunochemical testing (FIT) is a screening method that selects individuals at high risk of having advanced adenomas or CRC, the combination referred to as advanced neoplasia (AN). Screening participants with a positive FIT are subsequently invited for colonoscopy. FIT has a low burden, facilitating participation in FIT-based screening. In the Netherlands, the participation rate is around 72%.8 In this context, a positive family history is defined by the diagnosis of a hereditary CRC syndrome, such as Lynch syndrome, and/or the so-called “familial CRC syndrome”, defined by the number and age of relatives with CRC but which does not yet have a known genetic basis. Of all the CRC cases, 15–30% seem to have such a familial risk and 2–5% are related to a hereditary CRC syndrome.9To increase the detection rate of AN in FIT-based screening, FIT could be combined with risk factors for AN. Studies have reported that a false-negative FIT result was more often observed in participants with a family history of CRC than in those without.11 Colonoscopy surveillance has shown to reduce CRC-related mortality for individuals with Lynch syndrome by 72% and up to 81% for those with a familial CRC syndrome.13 However, colonoscopy uptake in individuals with a positive family history for CRC varies widely (12–51%).14 Furthermore, only 12–33% of CRC patients and their relatives who qualify for referral to a clinical geneticist are actually referred.16 Contrary to the guideline recommendations, many individuals with a hereditary or familial CRC syndrome do not receive periodic colonoscopy surveillance. Instead, they will be invited for screening whenever an organised population-based screening programme is in place.International guidelines recommend individuals with a positive CRC family history to undergo regular colonoscopy surveillance instead of participating in FIT-based screening programmes.17 Only 50% of those individuals had recently undergone surveillance.17 Another study showed that offering colonoscopy surveillance to participants with a positive FIT or CRC family history increased sensitivity in detecting AN, at the expense of specificity.18 In this study, the definition of a positive CRC family history was limited to first-degree relatives with CRC, regardless of their age.18In the Australian FIT-based population screening programme, 4% of the participants had a positive family history of CRC or polyps, warranting colonoscopy surveillance.19 In this study, participants with a positive FIT and/or a positive FHQ were offered a colonoscopy. We compared the yield of this combined strategy with that of the FIT-only screening strategy. As a secondary aim, we studied the identification of individuals with a hereditary or familial CRC syndrome by the FHQ and the subsequent referral rates for genetic counselling and colonoscopy.The limited sensitivity of FIT and the limited identification of individuals with a positive family history call for the exploration and evaluation of simple online questionnaire to improve the current situation. We designed a study to evaluate the incremental yield of adding a validated online family history questionnaire (FHQ) on CRC and Lynch syndrome-associated tumours to the Dutch organised FIT-based screening programme.We performed a prospective population-based CRC screening trial embedded in the national CRC screening programme. Six thousand screening-naive individuals, aged from 58–59 to 74–75 years, were invited to sample an FIT and to complete an FHQ. Invitees were selected from four areas in the province of North Holland based on their year of birth . These areas reflected the nationwide CRC screening programme in 2014 regarding socioeconomic status (SES), ethnicity and participation rates. Within these areas, invitees were selected with an age and sex distribution comparable to that of the national CRC screening programme population in 2016. SES was based on postal code areas. There were no specific exclusion criteria for participation in the study.In line with standard procedure of the national FIT-based screening programme, all invitees received an FIT pre-announcement letter. This letter contained a study invitation letter including a personal login code and information leaflet. Using this personal login code, a validated online FHQ could be filled out after informed consent was given.The Dutch CRC screening programme provided all invitees with an FIT kit by postal mail 14–21 days after the pre-announcement. Invitees who declined participation in FIT screening were asked to inform the screening organisation. Seven-to-14 days after sending the FIT kit, a reminder study invitation letter was sent. Invitees who had not responded to the FIT invitation received a reminder 56 days after the pre-announcement.FIT analysis and communication with participants regarding the FIT result were according to the logistics of the national CRC screening programme. The positivity threshold of the FIT was set at 47 µg haemoglobin/g faeces in the Dutch national CRC screening programme. All participants received a letter with the FIT result, either a positive or negative result, within 7–14 days after returning the device. Participants with a positive test result received an appointment for an intake visit for colonoscopy.19 Questionnaire responses were automatically evaluated against the national referral criteria for genetic testing for Lynch syndrome and surveillance colonoscopies in case of a familial CRC syndrome  within 10 days after receiving the FHQ result. To identify potential false-positive results, this trained researcher verified all answers with the participant. Once the positive CRC family history was confirmed, an intake at the Department of Clinical Genetics was arranged. During this intake, a clinical geneticist or genetic counsellor decided whether genetic testing or a surveillance advice was indicated.20 Colonoscopies in those with a familial or hereditary CRC syndrome were performed by gastroenterologists with similar adenoma detection rates. Colonoscopies were conducted according to international standard quality criteria.21 Resected lesions were evaluated by experienced pathologists according to the World Health Organisation classification and Vienna criteria.23 Advanced adenomas were defined as adenomas having a diameter ≥10 mm and/or with ≥25% villous component and/or high-grade dysplasia. Depending on the findings, surveillance colonoscopies were advised according to national guideline criteria.24All participants with a positive FIT and/or a positive FHQ, confirmed after genetic counselling, were referred for colonoscopy. Colonoscopies after a positive FIT and/or a confirmed familial CRC syndrome were performed in one nationally accredited colonoscopy centre (Bergman Clinics Amsterdam), unless the participant preferred another centre. In participants with Lynch syndrome, a colonoscopy was performed in a dedicated tertiary colonoscopy centre . All colonoscopies in participants with a positive FIT were performed by accredited screening gastroenterologists, according to the national CRC screening programme standards, who had performed at least 200 colonoscopies per year.The primary study outcome was diagnostic yield, defined as the number of participants in whom AN was detected relative to the number of invitees. We compared the diagnostic yield of a combined approach, selecting participants for colonoscopy based on FIT and family history, to that of  selection based on FIT only, in the same group of participants. Secondary outcomes were participation rate and positive predictive values. The positive predictive value of the screening instrument was defined as the number of participants in whom AN was detected relative to the number of participants testing positive. Differences in diagnostic yield and participation rate were tested for statistical significance using McNemar test statistic. A difference in positive predictive value between the two strategies was assessed for statistical significance with Chi-square test statistic.Additional outcomes were positivity rate, number of colonoscopies performed, and detection rate. A significance level of 5% was used in all statistical tests. All statistical analyses were performed using R Studio version 1.1.383.18 With a study group of 6000 individuals, we expected to detect 130 individuals with AN after a positive FIT (21.7 per 1000 invitees). By adding the FHQ, we anticipated to detect an additional number of 11 individuals with AN (23.5 per 1000 invitees): an increase in diagnostic yield of 1.8 per 1000 invitees. We then would have a power of at least 91% in rejecting the null hypothesis of no gain, using McNemar test statistic.Our sample size calculation was based on the anticipated gain in diagnostic yield when using the combined strategy.25Ethical approval for this study embedded in the national screening programme was obtained from the Dutch National Health Council. The study was performed in accordance with the Declaration of Helsinki.Of the 6000 initially selected, randomly sampled invitees, 16 were deceased, 4 had emigrated prior to the invitation for the study and 1 individual refused participation in research. Between April 2016 and January 2017, 5979 invitees received both an FIT and the FHQ  completed the FIT only, 2379 (39.8%) completed both the FIT and the FHQ and 95 (1.6%) invitees completed the FHQ only; 1553 (26.0%) invitees neither returned an FIT nor completed the FHQ.Table p < 0.001). Figure For the comparison of the two strategies, we included all 4426 invitees who returned the FIT and the FHQ, FIT only or the FHQ only in the combined strategy (invitations for colonoscopy based on FIT and/or FHQ). Similarly, we included all 4331 invitees who returned the FIT only or the FIT and FHQ in the FIT-only strategy (invitations for colonoscopy based on FIT only). This implies that the participation rate of the combined strategy was marginally but significantly higher  compared to a FIT-only strategy .The FHQ was initially found to be positive in 191 (7.7%) of the 2474 participants who had returned the FHQ. After a telephone check, 66 of these (34.6%) appeared to be false positives : 16.4–23.5) for the combined strategy versus 19.5 per 1000 invitees (95% CI: 16.3–23.3) for FIT-only strategy, a non-significant difference of 0.1 per 1000 invitees (n = 29), because they declined referral (n = 30) or were not reached (n = 1). Reported reasons for declining referral for genetic counselling were: financial reasons (n = 5), other comorbidities (n = 4), no perceived added value of counselling (n = 7), and no reason mentioned (n = 14). Fifty of the 65 (76.9%) participants referred for genetic counselling underwent counselling.Of these participants eligible for referral for genetic counselling, 60 were not referred, because they had already received genetic counselling and/or received an advice for surveillance (MSH2 mutation) in one participant and the detection of two participants with gene variants of unknown clinical significance in the MLH1 gene and the MSH6 gene, appointed to a 2 yearly and 5 yearly surveillance interval, respectively. Lynch syndrome was excluded by genetic testing in the 23 other participants, of whom 10 were newly diagnosed with familial CRC syndrome, 7 were previously diagnosed with familial CRC syndrome, 2 participants were advised to undergo a one-time colonoscopy and 4 were advised to return to the national population-based FIT screening programme. In seven participants, referral of family members was advised to undergo genetic testing, of whom one was newly diagnosed, one previously diagnosed with familial CRC syndrome and five were advised to return to the national population-based FIT screening programme. In 14 participants, information was requested to exclude Lynch syndrome, of whom 3 were newly diagnosed with familial CRC, 3 were previously diagnosed with familial CRC syndrome and 8 were advised to return to the national population-based FIT screening programme. Three participants had declined genetic testing after genetic counselling, of whom one turned out to have serrated polyposis syndrome at colonoscopy for a newly diagnosed familial CRC syndrome and two were advised to return to the national population-based FIT screening programme.Of the 50 participants who underwent genetic counselling, 26 underwent genetic testing because of suspicion of Lynch syndrome Fig. . This ren = 1), a carrier of a variant of unknown clinical significance in the mismatch repair (MMR) gene appointed to a 2-year surveillance interval (n = 1), newly diagnosed familial CRC syndrome (n = 11), one-time colonoscopy after exclusion of Lynch syndrome (n = 1), and previously diagnosed familial CRC syndrome withdrawn from surveillance (n = 2). One of the participants who underwent colonoscopy because of a newly diagnosed familial CRC syndrome turned out to have serrated polyposis syndrome. The second participant, carrier of a gene variant of unknown clinical significance in the MSH6 gene appointed to a 5 yearly surveillance interval, was already undergoing surveillance for familial CRC syndrome. Two patients with a new diagnosis of familial CRC syndrome declined to undergo a colonoscopy and two of them underwent the colonoscopy because of a concurrent positive FIT result. One participant advised to undergo a one-time colonoscopy after exclusion of Lynch syndrome had declined a colonoscopy because of his age.In addition to the colonoscopies after a positive FIT, 16 colonoscopies were performed as a result of a positive FHQ. Indications for colonoscopy were Lynch syndrome  participants actually underwent a colonoscopy because of the study and 2 (10.5%) because of a concurrent positive FIT. Two participants had declined surveillance for familial CRC syndrome (10.5%).In this study, we observed no significant incremental yield in detecting AN after adding a validated online FHQ on family history of CRC and Lynch syndrome-associated tumours to one round of FIT screening. In the almost 6000 invitees, 16 additional colonoscopies were performed for a positive FHQ result, but only 1 additional participant with AN was detected. Despite, we identified 19 individuals who had an indication to undergo colonoscopy surveillance instead of participating in a FIT-based screening programme.19 This prior validation had showed a comparable number of false positives but no false-negative results, when referrals based on questionnaire data were compared with referrals based on data collected in a telephone interview. Therefore, we had integrated the telephone check in the current online FHQ tool and had accepted the risk of having a false-positive result, while adequately detecting all participants at risk. Second, the SES in our sample was slightly higher than that of the national programme, even though the selected regions were considered comparable to the national SES distribution. Nevertheless, the slightly higher SES and addition of the FHQ did not affect the FIT participation rate, FIT positivity rate or AN detection rate.4 The higher SES may have influenced participation in the FHQ in a positive way.26 We observed that invitees with a lower SES were less likely to return the FHQ. As participants depend on their health insurance for reimbursement of the genetic follow-up examination, and financial reasons were often reported as a reason for declining genetic testing, costs may have been a reason for lower SES invitees not to return the FHQ.An unfavourable result of this study was the high false positivity rate (35%) using the online FHQ, despite previous validation in patients scheduled for colonoscopy in a private colonoscopy centre.A strength of this study is that it is the first to evaluate the addition of an online FHQ to a national organised FIT-based screening programme assessing the effect in participants with a positive as well as those with negative FIT result. The large randomly selected cohort was screen-naive and consisted of an average-risk population with an age and sex distribution similar to that of the national screening programme. Furthermore, the quality standards of the colonoscopy performed after a positive FHQ were comparable to that of the population screening colonoscopies. Lastly, positive FHQs were verified by a genetic counsellor or clinical geneticist.1 A previous study in the Dutch population reported that 10% of the affected and 2.3% of the unaffected respondents reported to fulfil the Dutch referral criteria for genetic testing for Lynch syndrome or surveillance for familial CRC syndrome.27 These numbers indicate that, in our country, in total almost 400,000 individuals fulfil the criteria for Lynch syndrome or familial CRC syndrome. Although the exact numbers of patients diagnosed with Lynch syndrome and familial CRC syndrome are lacking, the majority of these patients are unaware of their diagnosis and at great risk for developing CRC.28 Therefore, the implementation of an FHQ as a screening tool could be beneficial.In the Netherlands, in the year 2016 the incidence of CRC was 15,306 with a corresponding mortality of 5154 in a population of 16,980,000 individuals.27 This could have perhaps been the result of the online nature of the FHQ as predominantly younger invitees with a higher SES had participated in the FHQ. Despite the fact that positivity rates of the FHQ were comparable to previously published studies30 and considerable evidence of a higher neoplastic yield among individuals with a family history of CRC, the yield of the combined screening strategy was limited.32 This may be explained by the low referral rate to the clinical geneticist of only 40% compared to a recently reported uptake of pre-symptomatic genetic testing for Lynch syndrome according to genetic centres of 41–94%.33 A large proportion of our patients actively declined referral (24%), did not show up at their appointment or cancelled it (12%). Compliance to appointments is known to be related to perceived urgency of medical clinic visits and associated with a strong recommendation by the physician.34 Given the screening situation, participants could have perceived this advice as less urgent and therefore may have declined referral to a clinical geneticist. Lengthy waiting time might also have caused participants to change their minds. Future research should evaluate whether different strategies, for instance without verification by the clinical geneticist, may lead to an increase in diagnostic yield.Compared with a previously performed study in the Dutch population, the current participation rate of the FHQ was considerably lower.Another explanation for the limited diagnostic yield is that a positive family history of CRC considers a life-long risk that is not necessarily associated with an increase in the diagnostic yield in just one single colonoscopy. Future research should investigate whether addition of an FHQ changes the yield after several round of FIT screening.As in our study only one third of the participants with a positive FHQ result had already received genetic counselling and/or surveillance advice, there seems still room for improvement in the identification of individuals with familial or hereditary CRC syndromes. In the FIT information leaflet, there is no mention of exclusion from participation in FIT screening for patients with a positive family history for CRC. We observed that patients who had already undergone colonoscopy surveillance for a positive family history also participated in the FIT screening programme, indicating that the information for invitees on who should undergo FIT screening could be improved.In conclusion, we observed no increase in yield after introducing a validated, online FHQ for detecting AN in this study that was embedded in the Dutch FIT-based CRC screening programme. Addition of this FHQ resulted in the identification of a small number of participants at increased risk of developing CRC who should undergo periodic colonoscopy surveillance and not participate in a screening programme based on FIT screening.Supplementary Information Family History Questionnaire in DutchSupplementary Information Table 1 Referral criteria for genetic testing and colonoscopy surveillance