The new coronavirus disease 2019 (COVID-19) has been emerged as a rapidly spreading pandemic. The disease is thought to spread mainly from person-to-person through respiratory droplets produced when an infected person coughs, sneezes, or talks. The pathogen of COVID-19 is the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It infects the cells binding to the angiotensin-converting enzyme 2 receptor (ACE2) which is expressed by cells throughout the airways as targets for cellular entry. Although the majority of persons infected with SARS-CoV-2 experience symptoms of mild upper respiratory tract infection, in some people infections of the acinar airways result in severe, potentially fatal pneumonia. However, the induction of COVID-19 pneumonia requires that SARS-CoV-2 reaches the acinar airways. While huge efforts have been made to understand the spread of the disease as well as the pathogenesis following cellular entry, much less attention is paid to how SARS-CoV-2 from the environment reach the receptors of the target cells. The aim of the present study is to characterize the deposition distribution of SARS-CoV-2 in the airways upon exposure to cough-generated droplets and aerosol particles. For this purpose, the Stochastic Lung Deposition Model has been applied. Particle size distribution, breathing parameters supposing normal breathing through the nose, and viral loads were taken from the literature. We found that the probability of direct infection of the acinar airways due to inhalation of particles emitted by a bystander cough is very low. As the number of viruses deposited in the extrathoracic airways is about 7 times higher than in the acinar airways, we concluded that in most cases COVID-19 pneumonia must be preceded by SARS-CoV-2 infection of the upper airways. Our results suggest that without the enhancement of viral load in the upper airways, COVID-19 would be much less dangerous. The period between the onset of initial symptoms and the potential clinical deterioration could provide an opportunity for prevention of pneumonia by blocking or significantly reducing the transport of viruses towards the acinar airways. Therefore, even non-specific treatment forms like disinfection of the throat and nasal and oral mucosa may effectively keep the viral load of the upper airways low enough to avoid or prolong the progression of the disease. In addition, using a tissue or cloth in order to absorb droplets and aerosol particles emitted by own coughs of infected patients before re-inhalation is highly recommended even if they are alone in quarantine. There are currently few studies that define the pathophysiological characteristics of COVID-19, and there is great uncertainty regarding its mechanism of spread3. However, the disease is thought to spread (1) mainly from person-to-person, (2) mainly through respiratory droplets produced when an infected person coughs, sneezes or talks (3) which can land in the mouths or noses of people who are nearby or possibly be inhaled into the lungs4. Virological assessment of COVID-19 also suggests that the transmission is droplet-, rather than fomite-, based5.The new coronavirus disease 2019 (COVID-19) has been emerged as a rapidly spreading pandemic6, which infects the cells binding to the angiotensin-converting enzyme 2 receptor (ACE2)7 primarily in the respiratory system. Cells expressing ACE2 can be found throughout the airways8, and therefore, cellular entry of SARS-CoV-2 can also take place throughout the airways9. Although the majority of persons infected with SARS-CoV-2 experience symptoms of mild upper respiratory tract infection, in some people infections of the acinar airways result in severe pneumonia potentially leading to significant hypoxia with acute respiratory distress syndrome (ARDS) and death. However, the induction of COVID-19 pneumonia and ARDS requires that SARS-CoV-2 reaches the lower airways.The pathogen of COVID 19 is the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)While huge efforts have been made to understand the spread of COVID-19 disease as well as its pathogenesis following cellular entry of SARS-CoV-2, much less attention is paid to how viruses from the environment reach the receptors of the target cells in the respiratory system. The aims of the present study are to quantify the deposition distribution of cough-generated droplets and aerosol particles carrying SARS-CoV-2, to estimate the amount of deposited SARS-CoV-2 in different parts of the human airways upon exposure to cough generated droplets and aerosol particles, and to discuss its consequences on the pathogenesis of COVID-19.10. It was originally developed by Koblinger and Hofmann12, and continuously extended during the last three decades14. The concept and algorithms of the model are comprehensively described in the original publication11. However, a summary of the model is provided below.For this purpose, the most recent version of the Stochastic Lung Deposition Model has been applied15 was used to account for the inertial deposition, while deposition due to diffusion was computed by Cheng’s equation 16.Particle deposition in the extrathoracic airways is computed using empirical formulae based on particle deposition measurements in hollow airway casts. In this work, the formula of Yu et al.17. The geometry of the acinar airways was taken from the description of Haefeli-Bleuer and Weibel18.Deposition in the intrathoracic airways is simulated by reconstructing the path of inhaled particles in a stochastic lung structure. Stochastic means that airway lengths, diameters, branching angles and gravity angles of the airways along the path of an inhaled particle is selected randomly from distributions of these parameters obtained by statistical analysis of morphometric data measured by Raabe et al.Particles are tracked from inhalation until they deposit or leave the airways by exhalation. During inhalation, the path of the particles at bifurcations is decided by so-called entrance probabilities. At the entrance of the lobes (lobar bronchi), the entrance probability is proportional to the lobar volume. In all other bifurcations, the entrance probability is proportional to the cross sectional area of the daughter branch. In the acinar part of the airways, the particles enter the alveoli based on entrance probability which is proportional to air volume entering the alveolus divided by air volume entering the corresponding acinar duct.19 provided for cylindrical tubes (bronchus) and spherical space .Throughout their path, particles can deposit by impaction, sedimentation, and diffusion mechanisms. In the Stochastic Lung Deposition Model, the probability of deposition by each mechanism is calculated by the formulae Yeh and SchumThe main input data of the model are spirometric  and inhalation parameters  and particle properties .The model computes the fraction of inhaled particles that deposit in each anatomical region of the lungs. In addition, it also provides the deposition fraction as the function of airway generation number.  The fraction of inhaled mass in different anatomical regions and airway generations can also be obtained, which is particularly useful if the average virus concentration in the material coughed (including aerosol particles and droplets) is supposed to be independent on particle size. These fractions are quantified for a full breathing cycle  supposing normal breath through the nose.3, tidal volume of 750 cm3, and breathing frequency of 12 min−1 were taken from the Human Respiratory Tract Model of the International Commission on Radiological Protection corresponding to an adult man in sitting position20 and applicable for normal breath if the spine is in vertical position. It was supposed that the duration of inhalation is 1.9 s followed by 0.1 s breath hold, and exhalation lasts for 2 s followed by a 1-s-long breath hold.Functional residual capacity of 3300 cm21. While influenza is a different disease than COVID-19, studies of cough aerosols from patients with various respiratory infections have shown striking similarities in aerosol size distributions22, therefore we used the data from Lindsley et al.21. The mass size distribution was obtained from the concentration, number size distribution, and density of cough-generated particles. For the calculations, we assumed that the particles are spherical and their physical size is equal to their measured optical size. The mass size distribution is plotted in Fig. Besides the geometry and flow conditions in the airways, lung deposition of droplets and aerosol particles is determined by their aerodynamic properties which depends on many parameters . Lindsley et al. measured the number size distributions of cough-generated droplets and aerosol particles by means of a combined wide range aerosol particle spectrometer , when patients with influenza cough23. During the flight, the particles may evaporate or fall down by gravitational settling. Therefore, the present simulations apply only to one of the major transmission routes, when a bystander subject (within a 1-m-distance) directly inhales cough-generated droplets and aerosol particles4. If the inhalation takes place more distant from the source in space or time, then the size distribution has to be measured near the subject inhaling the emitted particles.The cough-generated particles may travel great distance if the meteorological conditions  are favorable3 varies highly in throat swab and sputum samples of SARS-CoV-2-infected patients with a maximum viral load exceeding 1011 RNA copy per cm3, while the median values are in the order of 105–106 RNA copy per cm3. Values along this wide range were used to obtain upper estimate for RNA copy number deposited in different lung regions and airway generations. Other studies provided similar range for viral loads25. It is important to note, however, that the number of viruses capable of infection is much lower than the number of RNA copies measured. In another study, infectious viruses could not be isolated from samples that contained less than 106 RNA copy per cm3.In a recent study, it was found that viral load quantified as the number of RNA copies per cmWe supposed that the average virus concentration (concentration of viruses capable of infection) is independent on particle size. It means that the average virus number in a particle (droplet or aerosol particle) is directly proportional to the mass of the particle. It is an appropriate approximation only in locations close to the source, because evaporation increases the virus concentrations in droplets and aerosol particles, and evaporation rate strongly depends on the particle size.Neither human, nor animal subjects were involved in this study, and therefore ethics committee approval was not required for this study.−3, the term material coughed includes aerosol particles and droplets throughout this article), the tidal volume (0.75 dm3), and the mean particle mass (4.6 pg), it can be calculated that a single inhalation results in 63 ng cough-generated material depositing in the extrathoracic airways, while 5.6 ng and 8.6 ng deposit in the bronchial and acinar airways, respectively.As the infection with SARS-CoV-2 causes very different symptoms depending on the infected region, first we focus on the regional deposition distribution of particles emitted by coughing. Figure In a subtler subdivision of the intrathoracic airways  retrieved in the open literature and compared in a review4 RNA copy per g in the throat swab results in negligible amount of RNA copies, around 4 × 10–4 in the acinar airways of a bystander person. The reason for this surprising fact is that the mass of inhaled material (cough-generated droplets and aerosol particles) per inhalation is low (1.02 × 10–7 g), and therefore the absolute virus number is also low. We assumed homogenous virus distribution in the initial throat swab sample, and that the number of viruses in a particle is proportional to its mass. In this case, the probability that a particle contains at least one RNA copy of the virus can be calculated assuming Poisson distribution.Inhalation of particles emitted by a single cough of an individual with viral loads of 4.69 × 10–4 for particles smaller than 10 µm if the viral load in the throat swab is 5 × 104 RNA copy per cm3. However, if the viral load is high, for example 5 × 1010 RNA copy per cm3, then the probability that a particle contains an RNA copy strongly increases from 1.3% at 0.8 µm and it is almost 100% at 8 µm, where the average number of RNA copies in the particles is more than one.Figure 28, which is often caused by upper respiratory infections. In addition, anosmia and ageusia have also been reported indicating infection of cells in the upper airways30.Independently on the virus concentration in the inhaled air, it can be concluded that about 7 times more viruses deposit in the extrathoracic airways than in the acinar airways. It is in agreement with the clinical observation that the typical first respiratory symptom of COVID-19 is dry cough31. It is also in agreement with a recent virological analysis of nine mild cases of COVID-19 providing proof of active replication of SARS-CoV-2 in tissues of the upper respiratory tract5. They found very high concentrations of viral RNA in and isolated the virus itself from early throat swabs, although the patients had mild symptoms only. Without the enhancement of virus concentrations in the upper airways, SARS-CoV-2 would be much less dangerous. Therefore, even non-specific treatment forms like disinfection of the throat and nasal and oral mucosa  may effectively keep the viral load low enough to avoid or prolong the progression of the disease.Our results suggest that inhalation of cough-generated particles containing SARS-CoV-2 results directly in only an upper airway infection, which then can later develop into pneumonia. It is in agreement with the clinical observation that some patients who have only mild symptoms initially will subsequently have precipitous clinical deterioration that occurs approximately one week after symptom onset5, viruses can be transported either via the cardiovascular or the respiratory system. As SARS-CoV-2 genome cannot be detected in the blood of patients with mild symptoms34, transport via the respiratory system must be the dominant route. Potential mechanisms of virus transport from the throat to the lower airways may include re-inhalation of own cough, aerosol and droplet generation in the throat during inhalation, virus transport on the surface of the bronchial airways, or gradual infection of neighboring cells expressing ACE2 towards the periphery. As mucociliary clearance can inhibit the latter two processes, compromised mucus production or transport can be a risk factor for COVID-19 pneumonia.From the throat where high viral loads can be found even in patients with mild symptomsInhalation of air after a bystander cough could theoretically induce pneumonia without first developing an active infection in the extrathoracic airways, because material emitted by coughing directly reaches the acinar airways according to our models. Considering the viral load of the deposited mass, however, such direct induction of COVID-19 pneumonia requires either very large viral loads in the particles inhaled or very long exposures to particles with moderate viral loads. The latter underlines the importance of ventilation in rooms of infected people.Another example for prolonged exposure is the continuous re-inhalation of own coughs of infected patients, which may contribute to the progression of the disease. Therefore, reducing the re-inhalation of own coughs could significantly prolong, or even block the onset of further, more severe phases of COVID-19. Therefore, using a tissue or cloth in order to absorb droplets and aerosol particles emitted by own coughs of infected patients before re-inhalation is highly recommended even if they are alone in quarantine.26. However, the uncertainty of the results also depends on uncertainties of the input parameters we applied. In the following, effects of the input parameters  will be discussed.As the main tool applied in this work was a computational model, it is important to discuss the limitations of the study and the Stochastic Lung Deposition Model. The model itself has been validated earlier by comparing the deposition fractions obtained by five different airway deposition models including the Stochastic Lung Deposition Model3) and tidal volume (750 cm3) were taken from the Human Respiratory Tract Model of the International Commission on Radiological Protection, it can be argued that these values are much larger than the clinicians’ daily experience. For this reason, the simulations were repeated for smaller values, for a functional residual capacity of 2300 cm3, and a tidal volume of 500 cm3. This change decreases the mass deposition fractions in extrathoracic, acinar, and peripheral airways by 10%, 18% and 26%, while increases the mass deposition fractions in the bronchial (7%), large (10%), and small airways  to the person infected directly after his or her coughing. More distant in space or time, the particle size distribution will be different due to evaporation and gravitational settling. In addition, our assumption that the virus concentration is independent on the particle size is not reasonable farther from the cough. This is because evaporation affects the virus concentration differently in particles of different sizes, which may explain why higher virus concentrations can be observed in smaller particles in general36.While we focused on one of the most important transmission routes of COVID-19, i.e. coughing of a bystander person, it is important to note that it is not the only way of spread of the disease, as sneeze and talk can also infect others35. While these modes and viral loads in the corresponding locations could have been considered in order to obtain an estimate on the average viral load in the material coughed in a more sophisticated way, it would not necessarily lead to more accurate data. Instead, we used not only the median, but also the maximal viral load measured in throat swab samples in order to cover a wide range of potential viral loads in droplets and aerosol particles. Therefore, the conclusions of the study are not weakened by the fact that the processes identified by Johnson et al.35 were not taken into account.The virus concentration in droplets and aerosol particles also depends on the location where the material coughed are generated. Johnson et al. identified three different processes of aerosol/droplet generation associated with three size distribution modes: bronchiolar fluid film burst, laryngeal, and oral cavity modes, with the latter two playing important roles in coughing31 provides a precious window for prevention of pneumonia and ARDS by blocking or significantly reducing the transport of the virus towards the acinar airways. Therefore, disinfection of the mucosa of the upper airways may help to avoid or prolong the progression of the disease. In addition, using a tissue or cloth in order to absorb droplets and aerosol particles emitted by own coughs of infected patients before re-inhalation is highly recommended even if they are alone in quarantine.In order to quantify the deposition distribution of cough-generated droplets and aerosol particles containing SARS-CoV-2, we applied the Stochastic Lung Deposition Model. It was found here that the probability of direct infection of the acinar airways due to inhalation of particles emitted by a bystander cough is very low. As the number of viruses deposited in the extrathoracic airways is about 7 times higher than in the acinar airways, we concluded that COVID-19 pneumonia must be preceded by SARS-CoV-2 infection of the upper airways in most cases. The one week difference observed in several patients between the onset of their initial mild symptoms and precipitous clinical deterioration


Patient preference is key for medication selection in chronic medical conditions, like type 2 diabetes, where there are many different drugs available. Patient preference balances potential efficacy with potential side effects. As both aspects of drug response can vary markedly between individuals this decision could be informed by the patient personally experiencing the alternative medications, as occurs in a crossover trial. In the TriMaster , randomised double-blind, three-way crossover trial patients received three different second or third line once-daily type 2 diabetes glucose-lowering drugs . As part of a prespecified secondary endpoint we examined patients’ drug preference after they had tried all 3 drugs. 448 participants were treated with all three drugs which overall showed similar glycaemic control . 115 patients (25%) preferred pioglitazone, 158 (35%) sitagliptin, 175 (38%) canagliflozin. The drug preferred by individual patients was associated with a lower HbA1c  and fewer side effects . Allocating therapy based on individually preferred drugs, rather than allocating all patients the overall most preferred drug (canagliflozin), would result in more patients achieving the lowest HbA1c for them (70% v 30%) and the fewest side effects (67% v 50%). When precision approaches do not predict a clear optimal therapy for an individual, allowing patients to try potential suitable medications before they choose long term therapy could be a practical alternative to optimising treatment for type 2 diabetes. An alternative approach is to have a short course of the different drugs as occurs in n-of-1 or crossover trials where the patient tries multiple drugs before making a decision on which they prefer long term3. This means their decision is informed by personally experiencing the short-term benefits and side effects on each medication to help inform their treatment choice.Patient preference is a central part of therapy selection when there are multiple suitable alternative medications for a medical condition. When expressing a preference, the patient needs to balance the likely therapeutic outcome with likely side effects. However, both therapeutic response and side effects can vary greatly between individuals so it is difficult for a patient and their physician to identify which treatment will be optimal for them. Precision medicine aims to use a person’s biomarkers or clinical features to help target the therapy they are likely to respond best to5. When determining the best glucose-lowering therapy for a patient with type 2 diabetes, decisions are made balancing the glycaemic and non-glycaemic benefits of the different treatments against the potential side effects. Key factors identified by patients as important for their treatment choice include glycaemic control, weight change, risk of hypoglycaemia and gastrointestinal side effects7. A major problem when counselling patients is that both benefits (glycaemic and non-glycaemic) and side effects vary greatly between individuals and so it is not possible to give a precise prediction to the individual patient.Treatment guidelines for type 2 diabetes emphasise the importance of taking patient choice into account7. Direct within-person comparison for determining preference of diabetes drugs, through crossover trials, has been less well studied, in part because cross-over studies are rare, with parallel group randomised controlled studies being preferred for regulatory approval. There are some examples in the literature of open-label crossover trials comparing preferences for injectable therapies12, but only one study has examined preferences of oral therapies (daily vs. once-weekly DPP4-inhibitors)13. There have been no randomised double-blind crossover trials to date of patient preferences for the major second and third line glucose lowering therapies (those given after metformin), which represents a key treatment dilemma in type 2 diabetes.Information on patient preferences for specific diabetes treatments is limited. The majority of work in this area to date is based on choosing between hypothetical alternatives (discrete choice experiments or time trade off experiments), where participants are asked to rate the importance of certain attributes relating to their diabetes treatment and management14. The primary analysis of the trial addressed clinical predictors of glycaemic therapy response and is reported separately15. Here, we report a pre-defined secondary analysis examining preference of participants after they had tried all three therapies in the cross-over study , sitagliptin (100mg) and canagliflozin (100mg)er study , and pro15. In total, of the 525 participants who were randomised to one of 6 drug sequences in the study, 458 (87%) tried all three study drugs, and all but one participant provided information ranking the drugs in order of preference. 4.742 patients were screened between 22 November 2016 and 24 January 2020 and 525 participants randomised to the TriMaster studyAt baseline, when participants were asked their priorities for an additional glucose-lowering therapy they were most likely to rate lower blood glucose level and feeling better as very important issues with possibility of hypoglycaemia, weight gain, and side effects seen as less important .Participant characteristics recorded after receiving each of the drugs are shown in Overall, there was equipoise between the three drugs. There was no overall difference in mean HbA1c, pioglitazone 59.6 , sitagliptin 60.0 , canagliflozin 60.6 mmol/mol (p=0.2) The lowest mean number of side effects was reported for sitagliptin. Weight was lower when treated with canagliflozin. Pioglitazone was more tolerable, with lower rates of discontinuation. When looking at within participant comparisons of the on-treatment characteristics for the three drugs, more people had their lowest HbA1c on pioglitazone, their lowest number of side effects on sitagliptin, and their lowest weight on canagliflozin .2=3.9, p=0.14 for comparison with equal preference across the three periods).There was little evidence of an order effect. 167 (37%) preferred their first drug, 148 (33%) preferred the second, 133 (30%) preferred the third  chose the preferred drug because of “feeling better”, whereas 164 (38%) chose it due to “lack of side effects”, with 44 (10%) unclassifiable. Canagliflozin was most likely to be chosen because of “feeling better”, whereas pioglitazone was most likely to be chosen because of “lack of side effects” .After being fed back their HbA1cs and weight, 125/457 (27%) changed their preferred drug. Most patients (76%) changed because of the HbA1c result  while 29% changed because of weight . There were clear differences between the drugs , with weThere were marked differences in the glycaemic outcomes and side effect profiles in the groups of patients who preferred specific drugs .Of the 441 participants with at least one valid HbA1c, on final ranking, after being fed back their HbA1c results, 309  preferred the drug that resulted in the lowest HbA1c for them , and theOn final ranking, 301/448  chose the drug with the lowest number of side effects for them , and theWeight was less of a deciding factor in patient choice, with pioglitazone associated with the highest weight on therapy, regardless of their preference see . 203/44815. We have shown that individual patients choose different drugs, and their preferred choice is usually associated with the drug which results in the lowest HbA1c and least side effects for them. If patients were prescribed their preferred drug, 70% would be on the drug with the lowest HbA1c and 67% on the drug with the lowest number of side effects. In contrast if all had been allocated canagliflozin (the drug most likely to be preferred in this study) only 30% would be on the on the drug with the lowest HbA1c and 50% on the drug with the lowest number of side effects. This supports a patient-centred approach to precision medicine in type 2 diabetes where a patient who has no clear clinical indication for a specific drug class would have a short-term trial of potential glucose–lowering drugs before deciding on which therapy they would prefer to take long term. This “try before you choose” approach (n-of-1) could be applicable to other chronic diseases such as hypertension16).TriMaster was a crossover study examining patient preference for second and third line glucose-lowering therapy in type 2 diabetes, enabling patients to directly compare their own lived experience of three different drugs7.Most patients’ preference was for the drug resulting in the best glucose control. The drug with the lowest HbA1c was preferred by 53% of participants even prior to being given information on their HbA1c on the three different therapies. After being fed back HbA1c and weight, 70% preferred the drug with the lowest HbA1c. This is consistent with patients feeling better with good glucose levels and being educated that a lower HbA1c is desirable, and is in line with a recent systematic review of “discrete choice experiment” studies of patient preference in diabetes, where control of blood glucose (reflected in HbA1c) was identified as the most important attribute relating to patient preference7, with 84% of patients rating weight as important in a recent meta-analysis7. However, these studies are based on evaluating options in hypothetical scenarios, rather than the patients comparing their own lived experience on the three drugs. At baseline in our study, prior to starting any of the drugs, the proportion of participants who rated weight as important or very important was 86%, similar to the previous studies7. After trying all three therapies, the patients were able to compare weight against glycaemic benefits and other side effects directly and overall, it was less important for their final preference. This outcome might reflect the relatively modest changes seen in weight in the study, potentially due to the relatively short treatment with each drug. In discrete choice studies where participants were asked to place value on glucose control compared with weight, the differences were mainly seen when considering >3kg weight loss18, whereas the weight change on the three drugs in this study was lower than this . The weight differences in our study differ slightly compared with meta-analyses of trial estimates of weight change for the three drugs . This could reflect that the majority of trials are longer duration than 4 months.An unexpected result was that weight change was less important to the participants when choosing the preferred treatment. When given information on their weight on all three therapies only 45% of participants chose the drug resulting in the lowest weight for them. This is in contrast to other studies which have indicated that weight is an important attribute in treatment choice2 . To date, they have most commonly been used in neuropsychiatric disorders, asthma and pain management21. In type 2 diabetes an n-of-1 trial of potential alternative classes of glucose-lowering medication has been suggested as an optimal approach for making therapeutic decisions3. However, there have been no crossover preference trials in type 2 diabetes for different classes of oral medication and the approach has not featured in consensus management guidelines despite the emphasis on patient-centred medication choice4. The five crossover studies where patient preference was reported for type 2 diabetes treatment mainly investigated altered methods of dosing of the same therapy12 or alternative medications in the same class13. The only crossover preference study of different classes of medication was an unblinded comparison in 62 subjects offered an oral DPP4i and an injected GLP-111.N-of-1 trials and crossover trials have clear advantages in allowing within-person comparisons of outcomes, and have been proposed as being the ultimate way to achieve “personalised medicine” as it uses the individual’s experience of the alternative medications to inform their choice22.N-of-1 or crossover trials are particularly attractive for studies that test precision medicine approaches that aim to target the optimal treatment for a patient based on their baseline characteristics. N-of-1 trials are appropriate for stable long-term chronic conditions, where the response to a therapy can be observed and quantified relatively quickly. The within-person comparisons they afford means they have considerable advantages over parallel group trials in terms of reduced variability and sample size. However, these trials are longer due to the sequential administration of therapies and careful consideration of study design is required to ensure sufficient washout to minimise potential carryover effects. In addition, statistical analysis can be challenging, particularly when dealing with withdrawals or missing data, as conventional imputation approaches such as ‘last one carried forward’ are not appropriate15, allowing individuals to try several different drugs before choosing their preferred drug, could be integrated with other precision medicine strategies. Precision medicine aims to target the optimal treatment for a patient based on their baseline characteristics, and we have shown in type 2 diabetes, that simple clinical features can be used to help identify the drug likely to have the best glucose-lowering response for an individual 23. Using clinical features either to stratify patients or predict probability of response or side effects is an excellent approach for determining the best treatment option for an individual. However, there will still be cases where treatment decisions will be unclear as there may be little difference in predicted glucose-lowering response between different drugs; or the best choice of predicted drug for glycaemic response may carry the greatest risk of side effects (as seen with TZDs and risk of weight gain25). In these situations, where there is no clear optimal choice, the patient could choose after a brief therapeutic trial of the possible alternative medications.The approach used in TriMaster27.Our study has not examined the long-term outcome of the try-before-you-choose approach. The next step would be a clinical trial comparing directly allocating therapy to a patient with an approach where patients can experience alternative drugs before choosing their preferred option. Further study is needed to assess if the n-of-1 approach, when applied in type 2 diabetes, results in long-term benefits that have been seen in n-of-1 studies in other diseases including increased patient empowerment, improved adherence, and reduced costs which have been seen in other diseasesTo offer an n-of-1 type approach to patients in clinical practice would require some practical modifications to the formal clinical trial we propose. In clinical care, it would not be possible for patients to be blinded, so choice of medication could be influenced by prior beliefs of the patient or clinician regarding the benefits and risks of the therapies, but this would still allow patients to make decisions informed by their own personal experience. Given the short time periods on each drug, this approach is best for determining initial efficacy and short-term side effects. It would not be so helpful for uncommon and rare serious side-effects such as diabetic ketoacidosis, so these would need to be discussed with the patient at the time of their final preference. This n-of 1 approach will require changes in approach from healthcare professionals, but could be fitted to time with normal routine review periods (3-4 months) after initiating new therapy.7 have largely used approaches where participants are asked to place value on a set of attributes based on hypothetical scenarios, so do not necessarily reflect the real-life balance of benefits and risks. A further strength is that TriMaster was a double-blind study, so patients and healthcare professionals could not be influenced by prior beliefs and information on the potential benefits and side effects of each drug.A major strength is that this is a crossover study examining patient preference for therapy in type 2 diabetes, enabling patients to directly compare their own lived experience of the three different drugs. Previous studiesThere are limitations to our study. Since the study started, the landscape has changed with new evidence of cardiovascular and renal benefits of SGLT2-inhibitors and the cardiovascular benefits of GLP-1 inhibitors; this would be factor influencing therapy choice in patients with pre-existing cardiovascular or renal disease. However, in our study 81% of patients did not meet criteria for agents with specific cardiovascular or renal benefits, so for those at low risk of cardiovascular disease the equipoise of choice of different therapies still stands. A further limitation is that each treatment period was only 4 months, so might not have been long enough to participants to experience the full effect of the medication on glycaemic control and side effects. We only examined one drug in each class and the side effects and/or efficacy profile of other DPP4i and SGLT2i could be different. Finally, the population in our trial was predominantly (95%) self-reported White ethnicity. Further work is needed to determine whether the findings from our work are similar in other ethnicities.28 do not predict a clear best drug, we propose that patients could be offered a brief trial of potential alternative therapies to allow them to decide which they prefer.In our randomised crossover trial of three oral glucose-lowering therapies in type 2 diabetes (TriMaster), we showed variability in patient preference for different drugs. Most patients preferred the drug that gave them lowest HbA1c and least side effects. In the absence of a specific indication for a particular drug, or where prediction modelsThe study was approved by the UK Health Research Authority Research Ethics Committee South Central—Oxford A  and the ISRCTN registry (12039221).14, addressed clinical predictors of glycaemic therapy response, and is reported separately15. This work analyses patient preference in participants who tried all three therapies, a pre-specified secondary endpoint for the trial, and provides additional exploratory analysis of the associations with the patient’s preferred choice. The statistical analysis plan for the main trial is available for download at https://ore.exeter.ac.uk/repository/handle/10871/125162. ClinicalTrials.gov registration: NCT02653209; ISRCTN registion12039221.The primary analysis of the trial, as detailed in the study protocolParticipants were identified in primary care and from existing research cohorts in the UK. People with type 2 diabetes were eligible if aged 30-80 years on stable doses of metformin alone, or metformin and a sulfonylurea with HbA1c >58mmol/mol (>7.5%) and ≤110mmol/mol (≤12.2%). All participants gave written informed consent and were compensated for travel expenses only. Ethnicity was self-reported by participants using standard 2011 UK Office for National Statistics codes.Between 22 November 2016 and 24 January 2020, 742 patients were screened for eligibility with 525 randomised to one of the 6 possible treatment orders for the three therapies and asked to take each therapy for 16 weeks. HbA1c, weight, participant’s experience of the therapy, and side effects were recorded at the end of each treatment period. The 16-week treatment period was designed to allow an “on-treatment” washout for the first month, with each end-of-period HbA1c reflecting the previous 8-12 weeks on therapy. HbA1c results were only considered valid if the participant had been on the therapy for at least 12 weeks and they had at least 80% adherence, as measured by pill count at the end of each treatment period. Participants reported whether they had experienced any of a pre-defined list of 16 side effects associated with the three therapies : ABC, ACB, BAC, BCA, CAB, CBA. Drugs were blinded by over-encapsulation  with allocations blinded to the participants, study team, study researchers, and study statistician.Randomisation was carried out at the baseline visit as described in the study protocolSample size was determined for the main primary analysis reported separately . This study presents exploratory analysis relating to the secondary endpoint of patient preference. Confidence intervals are reported throughout to show precision of our estimates.Important considerations for future glucose-lowering therapies prior to randomisation were assessed at baseline with a 1-4 Likert scale from ‘very important’, ‘important’, ‘unimportant’ to ‘very unimportant’.Prior to each treatment period, participants were provided with a paper ‘reporting card’ to collect any noteworthy experiences or events. Completion was optional, they served as memory aide for the 16-week period. At the end of the treatment period study visit, participant experience questionnaires were completed by the participant themselves. Ahead of the final study visit, copies of documents from all three treatment periods were provided along with a summary showing whether at the time they had felt the drug had made their lives more difficult/no difference/less difficult, and whether they would be willing to take it again. Participants were asked to consider their experiences with a view to expressing a preference at the visit.At the final study visit, participants were asked to rank each of the drugs in order of preference on a scale of 1 to 3 (1 being most preferred to 3 being least preferred). Participants were asked to rank the drugs twice: once before they received information on their HbA1c result and weight on each of the therapies and once again after being fed back these results. Main preference results are shown as both mean rank (in line with the secondary endpoint in the SAP) and preferred drug. As our main analysis aimed to explore reasons for patient preference, analysis was mainly focussed on examining associations with preference as a binary variable (preferred v not preferred drugs), as in real life patients would only continue with taking one drug. Therefore, participants that could not decide on a preferred drug were excluded from analysis. Participants were asked, for each of the study drugs, whether they would continue taking them long term.Participants were asked for their reasons behind their ranking. Participants were still blinded to drug allocations when assigning their first ranking, so reasons were provided relating to the order they were given . Two researchers (BS and CA) independently coded the free text responses, without information on the final drug allocations, only which of the first, second or third drugs was the preferred. Free text was coded as either: 1) “feeling better” if they stated their preferred drug was chosen for having a positive effect on their health/wellbeing; 2) “lack of side effects” if the main reason for their preference was due to the preferred drug not having side effects that the other drugs did; or 3) unclassifiable. The two sets of codes were then compared, and discrepancies resolved through discussion with a third researcher (MS).Summary statistics are reported as mean (for continuous variables) or frequency and percentage , with 95% confidence intervals, unless otherwise stated. Characteristics  of the participants on the three drugs were compared using mixed effects models, to allow for repeated measures, with drug coded as a dummy variable and participant as the random effect. Chi-squared tests were used to compare differences in proportions against the null hypothesis of equal preference (i.e. 1/3). Mixed effects models were used to determine the difference in outcome  between preferred and non-preferred drugs , adjusting for study period (dummy variable) with participant as the random effect to allow for repeated measurements. All analysis was carried out using a validated version of Stata v16.1.A TriMaster PPI group was established in 2015 from participants of earlier pilot studies and worked alongside the existing Peninsula Research Bank PPI group to assist in study design and acceptability for the trial. The group contributed to the design and content of data collected on participant experience of the study drugs, to participant information sheets and were represented on the Trial Steering Committee. Both groups reviewed study documents ahead of regulatory submission. At study conclusion a participant group was invited to feedback on their experience to the CI team.


Channelrhodopsins (ChRs) are microbial light-gated ion channels utilized in optogenetics to control neural activity with light . Light absorption causes retinal chromophore isomerization and subsequent protein conformational changes visualized as optically distinguished intermediates, coupled with channel opening and closing. However, the detailed molecular events underlying channel gating remain unknown. We performed time-resolved serial femtosecond crystallographic analyses of ChR by using an X-ray free electron laser, which revealed conformational changes following photoactivation. The isomerized retinal adopts a twisted conformation and shifts toward the putative internal proton donor residues, consequently inducing an outward shift of TM3, as well as a local deformation in TM7. These early conformational changes in the pore-forming helices should be the triggers that lead to opening of the ion conducting pore. ChRs coprocess) . Spectro(CrChR2) , retinalectively . Photocyectively , but theThe recent development of the X-ray free electron laser (XFEL) technology has enabled high-resolution visualization of the time-resolved molecular conformational changes in crystals, thus profoundly advancing our understanding of biological reactions . In thisChlamydomonas reinhardtii ChR1 (CrChR1) and ChR2 (CrChR2) for the TR-SFX experiments (hereafter referred to as C1C2) , which has not been fully time-resolved in the whole cell patch clamp recordings. Afterwards, passive photocurrents rise with a time constant of 70–140 µs (rather faster at acidic pH) and change direction depending on the membrane voltage. The photocurrent remains after reprotonation of the retinal Schiff base and declines in a biexponential manner, with two voltage dependent time constants of 8–40 ms and 60–130 ms and a kinetic amplitude ratio that depends on the pH, correlating neither precisley with the P2nor the P3 intermediate. The absorption spectrum of ChR depends mostly on the microenvironment of the chromophore retinal, especially on the Schiff base environment (CrChR2 (2 intermediate.We used a chimeric construct between as C1C2) , which has C1C2) . C1C2 yias C1C2) . The daras C1C2) . The C1Cas C1C2) , revealius study , in deteus study . The res retinal , and theectively . The abser flash . The C1Cironment , and doe   crystals. The flash illumination induced a rapid elevation at 530 nm, confirming retinal isomerization (Prmation) . The ele crystal , suggeste region . Nonetheembranes . In addirmediate , in a gormediate . Therefoclight–Fcdark maps using the models, which shows a similar appearance with the difference-Fourier map and thus confirms the conformational change . The diffraction images were collected at 30 Hz, with the 470 nm pump-pulse laser at 15 Hz and the delayed time points at 1 μs, 50 μs, 250 μs, 1 ms, and 4 ms . The absl change .13–C14 double bond of the retinal, which was subsequently propagated as conformational changes to other protein regions, also involving a downward shift of TM7. Therefore, the retinal-attached residue in C1C2 probably undergoes similar conformational changes, although the primary retinal isomerization was not visualized, due to the limited resolution of the current TR-SFX experiment. Notably, at 50 μs we observed positive and negative difference densities at the putative internal proton acceptor residue, Asp292  suggest  were calculated by FFT /R + Fodark, where R represents the activation ratio of molecules and FExtra represents the extrapolated structure factor amplitude. Folight was scaled to Fodark by using the scaleit program in the CCP4 suite (Extra. The activation ratio R (the ratio of molecules in the excited state after light illumination) was determined by calculating extrapolated maps with phases of the dark state at different activation ratios in steps of 0.01, until a feature of the dark state at Cys167 disappeared. Next, 4 ms model was manually adjusted to fit the extrapolated map and refined by Refmac5 against FExtra and its estimated error. Then, each time point model was refined by Refmac5 with external structure restraints using 4 ms structure by the Geman–McClure function (http://www.cuemol.org).The structure was solved by the molecular replacement method implemented in Phenix Phaser-MR, using the model of C1C2 (PDB: 3UG9). The structure was manually modified to fit into the electron density maps, using the program Coot , and theP4 suite  before cfunction  the refi In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.Acceptance summary:This paper describes the use of time-resolved serial femtosecond crystallography to investigate light-induced changes in the conformation of channelrhodopsin. The authors identified initial conformational changes that occur upon illumination, including a shift in the position of retinal as well as additional changes in the conformation of transmembrane helices 3 and 7. Using these results, the authors propose a model for how initial conformational changes may culminate in channel opening. This work advances our understanding of the molecular mechanisms of channelrhodopsin activation and of light-induced conformational change more generally.Decision letter after peer review:eLife. Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Kenton Swartz as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Doeke Hekstra (Reviewer #1).Thank you for submitting your article "Time-resolved serial femtosecond crystallography reveals early structural changes in channelrhodopsin" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.eLife". Please let us know if you would like to pursue this option. As the editors have judged that your manuscript is of interest but revised data analysis is required before it is published, we would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). First, because many researchers have temporarily lost access to the labs, we will give authors as much time as they need to submit revised manuscripts. We are also offering, if you choose, to post the manuscript to bioRxiv  along with this decision letter and a formal designation that the manuscript is "in revision at Summary:This manuscript by K. Oda et al. describes the use of time-resolved serial femtosecond crystallography to investigate light-induced changes in the conformation of channelrhodopsin. The manuscript identifies initial conformational changes that occur upon illumination, including a shift in the position of retinal as well as additional changes in the conformation of transmembrane helices 3 and 7. The authors propose a model for how these initial changes may culminate in channel opening.Essential revisions:o-FoF density, or changes that are not readily interpretable. Reciprocal space refinement against extrapolated structure factors or reciprocal space refinement of partial occupancy models for each conformational state would be more appropriate (see below). At least one of these approaches should be included in a revised manuscript, along with appropriate statistical benchmarks such as real or reciprocal space correlation coefficients or R-factors. The reviewers also wish to emphasize that it is more appropriate to refine against structure factors corresponding to a conventional electron density (2o-FcF type) map than to a difference map. A second significant concern is the mismatch between the timescales of the QM/MM simulations and the experiments, which is not adequately explained. There are a variety of smaller technical points raised, which are discussed in detail below.Several technical points were raised during the review process, but the most significant concern centers on the approach to model refinement. The description of the refinement process in the Materials and methods section is very brief, and is missing essential details such as the choice of restraints and the exact details of which difference maps were used. More generally, the use of real-space refinement directly against difference maps is not sufficiently described, and is potentially confounded by a variety of issues. For example, motion of an amino acid side chain into a region that would otherwise be occupied by an ordered water molecule could lead to little change in 1) The authors mentioned low quantum efficiency of the retinal isomerization (~30% in C1C2), and then they built models onto the observed peaks in the difference maps. This can be problematic without validation, and the difference map signals can be misleading. In order to justify the correctness of the refined structures, the difference maps calculated between structures of light and dark states should be presented. Furthermore, more details should be included in structure refinement section, such as restraints, weighting factors, and validation metrics. Alternatively, the extrapolation approach has been adapted by several recent TR-SFX studies, such as the bR or KR2 studies ; Nature 583, 314-318, 2020). Authors may want to try these approaches. In principle, it may also be possible to refine models of different states with partial occupancy based on the known photoconversion efficiency, which would also provide useful refinement statistics to assess model quality.2) A vexing complication of the experiments is that the spectroscopic kinetics in crystal form differ markedly from those in solution. Clearly, the crystallographic data show that retinal isomerization is induced in the crystal and leads to conformational change at nearby positions. Spectroscopically, only two states are clearly distinguishable within the time scales of interest (P1/3 versus P2), reflecting the protonation state of the Schiff base. The authors speculate that they see a longer-lived P2 state in the crystal, apparently concurrent with a P1 or P3 state. This is reasonable, but it is not clear whether the authors really see a "P1 to P2 transition" (inclusion of shorter pump-probe delays might have been more convincing). The spectroscopic data likely miss detail of the structural transitions following photoexcitation and do not shed much light on how conformational dynamics may differ between crystal and solution.3) The comparison between refined structures and the QM/MM simulation model is not convincing. QM/MM calculation results are based on short simulations. The timescales do not match with the ones studied in this TR-SFX. It is hard to make a fair comparison. Essential revisions:o-Fo density, or changes that are not readily interpretable. Reciprocal space refinement against extrapolated structure factors or reciprocal space refinement of partial occupancy models for each conformational state would be more appropriate (see below). At least one of these approaches should be included in a revised manuscript, along with appropriate statistical benchmarks such as real or reciprocal space correlation coefficients or R-factors. The reviewers also wish to emphasize that it is more appropriate to refine against structure factors corresponding to a conventional electron density (2Fo-Fc type) map than to a difference map. A second significant concern is the mismatch between the timescales of the QM/MM simulations and the experiments, which is not adequately explained. There are a variety of smaller technical points raised, which are discussed in detail below.Several technical points were raised during the review process, but the most significant concern centers on the approach to model refinement. The description of the refinement process in the Materials and methods section is very brief, and is missing essential details such as the choice of restraints and the exact details of which difference maps were used. More generally, the use of real-space refinement directly against difference maps is not sufficiently described, and is potentially confounded by a variety of issues. For example, motion of an amino acid side chain into a region that would otherwise be occupied by an ordered water molecule could lead to little change in FAccording to the comments, we performed the reciprocal space refinement against the extrapolated structure factors, which is included below as a response to the reviewers.1) The authors mentioned low quantum efficiency of the retinal isomerization (~30% in C1C2), and then they built models onto the observed peaks in the difference maps. This can be problematic without validation, and the difference map signals can be misleading. In order to justify the correctness of the refined structures, the difference maps calculated between structures of light and dark states should be presented. Furthermore, more details should be included in structure refinement section, such as restraints, weighting factors, and validation metrics. Alternatively, the extrapolation approach has been adapted by several recent TR-SFX studies, such as the bR or KR2 studies ; Nature 583, 314-318, 2020). Authors may want to try these approaches. In principle, it may also be possible to refine models of different states with partial occupancy based on the known photoconversion efficiency, which would also provide useful refinement statistics to assess model quality.13-C14 double bond of the retinal, probably due to its intrinsic flexibility . Despite the lack of strong evidence, we speculate that the retinal adopts a kink toward TM3, based on the difference Fourier map that showed strong positive density beside the retinal, as well as the results of the QM/MM simulation. It should also be noted that a similar retinal kink and TM3 shift were visualized in recent work on another rhodopsin protein, KR2 . Therefore, we supposed that the series of conformational changes is somehow conserved in the rhodopsin family proteins, although it was not observed in the representative member, bacteriorhodopsin. We would like to include both the normal difference Fourier maps  and the extrapolated maps , because high resolution diffraction data are required to calculate reliable extrapolation maps, and our maps thus contain a lot of noise.We appreciate the reviewer’s critical comments on our study. We calculated the extrapolation maps according to the recent TR-SFX papers . First, we roughly estimated the ratio of the excited state by calculating extrapolated maps with different activation ratios until a feature of the dark state at Cys167 disappeared, and then modeled the excited states and performed the reciprocal refinement against the extrapolated maps. The obtained models confirmed the structural changes in TM3 and TM7, one of the major issues in the current manuscript . However, this approach could not provide detailed information about the retinal conformation, because it showed only a weak density around the C2) A vexing complication of the experiments is that the spectroscopic kinetics in crystal form differ markedly from those in solution. Clearly, the crystallographic data show that retinal isomerization is induced in the crystal and leads to conformational change at nearby positions. Spectroscopically, only two states are clearly distinguishable within the time scales of interest (P1/3 versus P2), reflecting the protonation state of the Schiff base. The authors speculate that they see a longer-lived P2 state in the crystal, apparently concurrent with a P1 or P3 state. This is reasonable, but it is not clear whether the authors really see a "P1 to P2 transition" (inclusion of shorter pump-probe delays might have been more convincing). The spectroscopic data likely miss detail of the structural transitions following photoexcitation and do not shed much light on how conformational dynamics may differ between crystal and solution.Conformational transitions are generally slower in crystals than in solution, and our spectroscopic study showed the same trend in C1C2. Figure 2—figure supplement 3 shows clear evidence that the P1 formation is completed within 10 μs, and the subsequent P2 formation is most prominent in 10 msec in crystals, which is slower than the corresponding steps in solution. Also, the lack of the P3 formation is consistent with the idea that the later reaction is more likely to be affected by crystal packing interactions. This information is sufficient to support the proposal that the dominant changes along the TR-SFX (occurring between 10 μs – 2.1 ms) are derived from the conformational transition from P1 to P2, although slight contamination by other intermediates (such as P3) might exist. It would be informative if we could understand the details of the early changes (including the precise population of photo-intermediates) after photoexcitation, but this is technically difficult because the spectrum changes in the tiny crystals of C1C2 are very weak. In addition, the spectroscopic studies only provide information about the micro-environmental changes around the retinal Schiff base and do not directly correlate with the global conformational changes of the protein. The previous bR TR-SFX study also suggested the lack of later conformational changes in the crystals, and suggested that the local changes in the Schiff base and the global structural changes are not necessarily coincident, especially in the later reactions. Therefore, the conclusion in the current manuscript is minimally dependent on the spectroscopic study. The observed changes in TM3 may occur during the P1 to P2 transition, and probably more quickly in solution.3) The comparison between refined structures and the QM/MM simulation model is not convincing. QM/MM calculation results are based on short simulations. The timescales do not match with the ones studied in this TR-SFX. It is hard to make a fair comparison.The molecular dynamics simulations in the QM/MM free energy geometry optimization by Cheng et al., 2018, showed the precursor state of the channel opening (early P2 state) on the timescale of tens to hundreds of nanoseconds. Although this time scale is much longer than that of the conventional QM/MM simulations ), it is still shorter than the experimental time scale of the pore formation, 2 ~10 ms . However, please note that the timescales of the QM/MM simulations do not directly represent the experimental timescale of the early P2 formation. Spectroscopic measurements showed that the time constant of the early P2 state formation coincides with that of the proton translocation from the protonated Schiff base to its counterion, glutamate , exhibiting a deuterium kinetic isotope effect ). These studies indicate that the early P2 formation is kinetically governed by the proton translocation. In contrast, the QM/MM calculations simulated the conformational changes of the protein after the proton translocation event. The proton translocation has already been well characterized experimentally. Therefore, it was omitted in the simulation to enable the modeling of the conformational changes triggered by the proton translocation, at the unprecedentedly high level of theory achieved by the combination of highly accurate ab initio quantum chemistry. The key chemical feature revealed by the QM/MM simulations was the torsional motion from a locally twisted conformation in the Schiff base, due to the increased electronic double bond character upon the proton translocation. This can be properly described by the ab initio QM treatment of the QM/MM simulations and is in excellent agreement with the current TR-SFX observation. The molecular events described in the simulations are distinctly different from those in the experiments. Therefore, although the simulation and current TR-SFX both indicated the retinal kink upon the proton transfer, the timescales of these results do not exactly match. These points are clarified in the revised manuscript .


The KERALINK trial tests the hypothesis that corneal cross-linking (CXL) treatment reduces the progression of keratoconus in comparison to standard care in patients aged 10–16 years. This article describes the statistical analysis plan for this trial as an update to the published protocol. It is written before the end of the patient follow-up, while the outcome of the trial is still unknown.2 at 18 months adjusted for K2 at baseline examination. K2 is the value of the steepest corneal meridian as measured on Pentacam topography. Secondary outcomes are keratoconus progression, time to keratoconus progression, visual acuity, refraction, apical corneal thickness and adverse events. Patient-reported effects will be explored by questionnaires. We describe in detail the statistical aspects of KERALINK: the outcome measures, the sample size calculation, general analysis principles, the planned descriptive statistics and statistical models, and planned subgroup and sensitivity analyses.KERALINK is a randomised controlled, observer-masked, multicentre trial in progressive keratoconus comparing epithelium-off CXL with standard care, including spectacles or contact lenses as necessary for best-corrected acuity. Keratoconus is a disorder of the shape of the cornea in which the normally round dome-shaped clear front window of the eye (cornea) thins progressively leading to a cone-like bulge. This impairs the ability of the eye to focus properly, causing reduced vision which requires spectacle or contact lens wear or, in a minority of patients, eventually corneal replacement by a transplant for best vision. The primary outcome measure is the between-group difference in KThe KERALINK statistical analysis will provide comprehensive and precise information on the relative effectiveness of the two treatments. The plan will be implemented in May 2020 when follow-up for the trial is completed.2016-001460-11. Registered on 19 May 2016.EudraCT,  In its early stages, keratoconus causes worsening of vision: spectacle correction provides good visual acuity in early disease only, until increasingly irregular astigmatism requires correction with rigid contact lenses for best vision. Lenses may not be well tolerated for significant periods of the day because of the irregular shape of the cornea and the common association of keratoconus with severe allergic eye disease. Without lenses these individuals can effectively be blind.Keratoconus is characterised by thinning and distortion of the cornea that results in visual loss from complex refractive error and corneal opacification. The prevalence in Europe is 1:12,000, rising to 1 in 450 in South Asians, with an estimated 50,000 affected individuals in the UK.Cross-linking (CXL) is a procedure conceived to increase the stiffness of the cornea and stop progression of keratoconus. Epithelium-off cross-linking is a procedure that involves surgical removal of the outer layer of the cornea before administration of riboflavin eye drops and exposure of the cornea to ultraviolet (UV) light. The KERALINK prospective, multicentre, parallel-group, observer-masked randomised controlled trial (RCT) will investigate the efficacy and safety of the established epithelium-off CXL in the paediatric age group, in which no RCT has been undertaken. Full details of the background to the trial, the interventions under study and its design are in the published trial protocol .This article describes the statistical analysis plan (SAP) for the KERALINK trial. Analyses will begin in May 2020 after completion of the 18-month follow-up for the last patient, data cleaning checks and data lock. However, if the last patient is observed to have progression in the study eye at the 18-month visit, a further confirmatory visit will take place in July 2020 and analysis will begin soon after. The analysis of the primary outcome will be independently programmed from the cleaned derived dataset by a statistician who did not perform the main analysis, and in parallel by the trial statistician.The primary objective of the KERALINK trial is to compare CXL versus standard care at 18 months from randomisation and to investigate whether CXL is efficacious in stabilising the progression of keratoconus and safe in children and young people aged 10–16 years.KERALINK is a two-arm, prospective, multicentre, parallel-group, observer masked RCT.2 or Kmax on Pentacam corneal topography  between two examinations done using the same scanning technique at least 3 months apart;Age 10–16 years with keratoconus progression confirmed in one or both eyes using Pentacam or other topography devices. Progression for eligibility is defined as an increase of at least 1.5 D in KPatients and their parents/guardians must be sufficiently fluent in English to provide assent and informed consent and to complete the patient-reported outcome measures;Patients must be willing to attend for follow-up visits.Advanced keratoconus as determined by apex corneal scarring;Apex corneal thickness < 400 μ;2) > 62 D and maximum corneal curvature (Kmax) > 70 D on Pentacam topography at screening;Steepest corneal meridian  conducted the randomisation to minimise allocation bias within the trial.Patients were allocated in a 1:1 ratio to the CXL and standard care arms. Due to the nature of the intervention, neither the trial participants nor the treating clinicians or site staff were masked to the treatment allocation. However, optometrists performing outcome assessments were unaware of treatment allocation. The Principal Investigator (PI) or treating clinicians were not aware of the primary outcome values measured during the follow-up assessments.At randomisation, patients were allocated to receive either CXL or standard care.2 for a 5.4 J/cm2 total energy dose.For CXL, surgery is performed under general or local anaesthesia as applicable, followed by standard management. After removal of the corneal epithelium and administration of riboflavin drops, UV light is administered according to standardised parameters of 10 mW/cmStandard care includes refraction testing with the provision of glasses and/or specialist contact lens fitting. Glasses or contact lenses are to be provided for one or both eyes as required for best corrected visual acuity. Those patients who develop advanced disease and poor spectacle- and lens-corrected visual acuity during the course of the trial will be offered corneal transplantation. Full details of these interventions can be found in the trial protocol .2 in the study eye at 18 months post-randomisation using standard Pentacam imaging. K is the corneal power at a given point on Pentacam topography, measured in D. K2 is the value of the steepest corneal meridian and more representative than Kmax, which is the corneal power at the steepest point in the cornea. For each patient, the eye with the more advanced keratoconus  at the time of randomisation will be defined as the study eye for the primary analysis, unless that eye has previously been treated by CXL or corneal transplantation. The 18-month power will be used if it is taken in a window of ±28 days.The primary outcome measure is the difference between the two arms in K2 from baseline (at randomisation) to 18 months after randomisation or requirement for change from spectacle to rigid contact lenses correction of vision, as the latter precludes reliable topography measures;Keratoconus progression (yes/no) defined as > 1.5 D increase in K2 from baseline);Time from randomisation to keratoconus progression ;Refraction ;Apical corneal thickness measurement (ultrasound);Quality of life as assessed by CHU9D and CVAQC questionnaires.The secondary outcome measures for the trial are:2 measurements from Pentacam images will be used as the indicator of disease progression. The probability is high that increases of > 1.5 D in K2 would discriminate a true change in the steepest corneal meridian from artefact. A change of this magnitude is clinically significant, indicating a likelihood of improved visual acuity with correction of the refractive change; for example, benefit from spectacle provision in an eye that previously had good unaided vision, a change in spectacle lens correction or progression from spectacle wear to contact lens correction. K2 will be measured three times by a masked observer at each trial visit and the mean value used in analyses.The K2 from randomisation will need to have this confirmed at a subsequent visit (i.e. 3 months later). Participants who have unconfirmed progression at the 18-month follow-up visit will need this confirmed at a further visit at 21 months.Taking into account the likelihood of inter- and intra-test variation in topography analysis, any patient found to have > 1.5 D increase in KIf ≥ 20 letters are read correctly at a starting distance of 4 m, the visual acuity score is equal to the number of letters read correctly + 30.If < 20 letters are read correctly at a starting distance of 4 m, the visual acuity score is equal to the number of letters read correctly at 4 m plus the number of letters read correctly at 1 m in the first six lines.If no letters are read correctly at either the 4-m distance or the 1-m distance, tests counting fingers (CF), hand motion (HM), perception of light (PL) and no perception of light (NPL) will be performed.Distance visual acuity (DVA) is recorded as the number of correct letters read in the ETDRS chart at a distance of 4 m. The ETDRS chart comprises 14 lines with five letters per line . With the ETDRS scoring system:The visual acuity score will be converted to logMAR equivalents using the formula:For patients that cannot read any letters correctly in the EDTRS chart at a distance of 1 m, assessments of CF, HM, PL and NPL will be assigned VA logMAR values of 2.10, 2.40, 2.70 and 3.00, respectively. Therefore, VA logMAR will be in the range of − 0.3 to 3.0 with lower values indicating better vision.The uncorrected versus corrected refers to whether the person has their vision assessed with glasses (or lenses) or without.The SER will be calculated by adding half of the cylinder power (cyl) to the sphere power:Refractive data will be summarised in two different ways for analysis: (1) as the spherical equivalent refractive error (SER); and (2) as the compound spherocylinder.(2)Mean refraction of compound spherocylinder will be obtained at baseline and at 18 months after randomisation by converting the data into matrix form. We will use Keating’s method based on Long’s dioptric power matrix to obtain the mean refractive treatment effect from pre-randomisation and 18-month refraction data. The matrices of refractive treatment effects in each arm will be used to test for statistical significance of a mean difference between the arms . The mulThe number of patients with refractive astigmatism will be based on refractive cylinder. An absolute cyl of 0.75D or more represents significant refractive astigmatism.a is the variance-covariance matrix for Where Biomechanical and ultrastructural studies to date have not been able to demonstrate the mechanism by which CXL stiffens the cornea. The KERALINK study will examine changes in thickness of the cornea using ultrasound as topography measurements do not provide accurate and reproducible thickness measurements.The CHU9D is a paediatric generic preference-based measure of health-related quality of life. It consists of a descriptive system and a set of preference weights, giving utility values for each health state described by the descriptive system, allowing the calculation of quality adjusted life years (QALYs) for use in cost utility analysis. It has been validated for self-completion in an adolescent population 11–17 years) –7. The q years –7This is a 25-item vision specific questionnaire designed for children . The ans2 of 1.5 D from randomisation to 18 months would be viewed as a clinically important difference . A K2 iA sample size of 46 patients would be required to detect this difference at the 5% significance level with 90% power, assuming a SD of 1.5 D. The total sample size has been increased to 60 patients (30 per group) to allow for up to 24% loss to follow-up. These estimates are based on 12- and 24-month data reported by Wittig-Silva et al., from which we estimate a pooled SD of the changes of 1.476 D.We expect that, on average, there will be a 10% loss to follow-up in both groups. In the study by Wittig-Siva et al., 19% of patients withdrew, crossed over to CXL or had a transplant by 18 months. However, 18% of patients in the control group received either CXL or a transplant. If we specifically adjust the sample size to take account of 10% loss to follow-up and up to 20% of the control arm cross-over to CXL or transplant, then our planned total sample size of 60 patients would still provide at least 80% power to detect the clinically important difference. The trial design dictates that children cannot cross over to CXL before 9 months from randomisation.Adherence to the protocol requires that: (1) the patient takes the treatment they were randomised to; (2) the patient does not cross-over to CXL before 9 months after randomisation; and (3) the patient attends all visits within the visit window as per the protocol. Patients are encouraged to continue providing follow-up measurements, even if they stop adhering to the protocol before 18 months.The main analysis will be conducted on an intention-to-treat (ITT) basis; all observed outcome data from randomised patients according to their allocated treatment arm will be used, irrespective of whether they receive the randomised treatment. Sensitivity analysis (described below) will assess the impact of missing outcome data.Additionally, if cross-over does occur, a per-protocol (PP) analysis will be performed for the primary outcome that only includes data from patients before crossing over. This analysis will exclude data for patients who do not receive the treatment they were randomised to.p value of 0.05, unless otherwise specified. There will be no formal adjustment of p values. Two-sided 95% confidence intervals will be presented for all estimates.All statistical tests will use a two-sided Baseline characteristics will be summarised by randomised treatment arm. Categorical variables will be summarised by number and percentage in each category; continuous variables will be summarised by mean and standard deviation or median and interquartile range, as appropriate. No statistical tests of differences in baseline characteristics between groups will be done, as any differences between treatment arms must be due to chance.Since randomisation is stratified by treatment centre and number of eyes progressed at randomisation, analyses of outcomes will involve adjustment for these factors  unless 2 will also be adjusted for in primary analyses where this is the outcome. Similar adjustment will be made for all continuous secondary outcome variables where a baseline measurement is recorded.Baseline KMissing baseline covariate data are not anticipated since covariates must be recorded to allocate treatment.We expect that up to 10% of patients will not provide measurements at the 18-month follow-up. Numbers and percentages of missing data at each visit (at baseline and 18 months) will be tabulated by treatment group for the primary and secondary outcomes.2, patients without baseline and at least one outcome score will consequently not be included in the analysis. Their inclusion, however, would not add any information to the analysis [All observed data will be included in the primary and secondary analyses. Missing outcome data will be assumed to be missing-at-random (MAR) conditional on the observed values of all other variables included in the analysis models, and so independent of the values of the unobserved data itself. As the primary outcome model is adjusted for baseline Kanalysis .All analyses will be carried out using Stata version 15 (or above). The results of the analyses will be reported following the principle of the ICH E3 guidelines on the Structure and Content of Clinical Study Reports  and CONSThe number of patients screened for eligibility will be presented. Reasons for non-admission into the trial will be reported.The period of data collection, including the date of the first patients’ first visit and date of the last patients’ last visit will be described. Recruitment will be presented by year and centre. The throughput of patients from those screened, those randomised and those assessed at each visit and included in the analysis will be summarised in a CONSORT flowchart . The numBaseline characteristics will be summarised in a table by treatment arm. The variables to be reported in the baseline tables are listed in the dummy tables  to strengthen confidence in the MAR assumption and provide greater power to detect a difference at the 18-month visit and test for superiority.A multilevel repeated measures linear regression model will be used to estimate the difference between the treatment groups in K2 at randomisation (continuous), treatment group (two categories: CXL and Standard care), time , and the minimisation factors centre and number of eyes progressed at randomisation. A random patient effect will be included to take account of clustering by patient. The model for K2, where yij is the K2 of patient i at time j, is:The model will include fixed effects for Kβi0 = β0 +ui0 + εij, ij ~ N and treatment = 1 if CXL and 0 if Standard care.Where, β2 + β4(time = 18 months).The primary outcome is the average difference between treatment groups at 18 months, estimated as The model coefficients will be estimated using robust standard errors, to allow for the possibility of unequal variances in the two randomised groups.2 measures may be required.The model makes assumptions about random effects distributions, correlation structure and residuals, which will all need investigation. If any assumptions are poorly met, then transformation of the KModel assumptions will be assessed and a logarithmic transformation may be used if this improves normality of the residuals.Uncorrected and best corrected visual acuity (measured as logMAR using EDTRS chart);Apical corneal thickness measurement (ultrasound);Spherical equivalent refraction (SER).Each of the following continuous secondary outcome measures on the study eye will be analysed using a separate multilevel repeated measures linear regression model:In patients for whom both eyes show progression at the time of randomisation, information from both eyes will be included in a secondary analysis of the continuous secondary outcomes. Each model will include fixed effects for treatment, time, treatment by time, baseline value of the associated outcome and the stratifying variables. A random patient effect will be included to take account of clustering by patient.The mean refractive surgical effect (RSE) will be calculated from pre-randomisation and 18-month refraction data using matrices. We will use the appropriate test for dioptric power to compare RSE between arms.2 from randomisation to 18 months or requirement for change from spectacle to rigid contact lenses for correction of vision, as the latter precludes reliable topography measures. Acknowledging inter- and intra-test variation in topography analysis, any patient found to have > 1.5 D increase in K2 will need to have progression confirmed at a subsequent visit (i.e. 3 months later);Keratoconus progression (yes/no) is defined as > 1.5 D increase in KRefractive astigmatism.Logistic regression models will be fitted to estimate the effect of treatment for each categorical variable:Time to keratoconus progression will be visually displayed using Kaplan–Meier curves; the difference between the arms in the time from randomisation to progression will be compared using Cox regression models which include the minimisation factors. The first date when progression is observed will be the date used for this analysis.The proportion of patients experiencing at least one adverse event (AE) and those experiencing at least one serious adverse event (SAE) will be summarised by treatment arm. The number and percentage of AEs and SAEs will be presented descriptively, but no formal analysis will be performed.The proportion of patients with any SAE or AE in the study eye will be compared between the two arms.The regression model for the primary outcome will be extended by adding interaction terms to investigate the effect of treatment on pre-specified subgroups. We will include interactions between treatment and number of eyes progressed at randomisation, ethnicity and family history of keratoconus/atopy to investigate whether the effect of treatment differs by each of these factors. As the trial has not been powered to detect this, the analysis will have limited power and is exploratory. We will also use forest plots to graphically display treatment effects across the subgroups. All subgroup analyses are hypothesis generating and will not form the basis of conclusions drawn from the trial.max, will be analysed as an exploratory outcome, using a similar model as the primary outcome analysis.The steepest corneal curvature measured by Pentacam, K2 and Kmax values to identify which is a more appropriate measure of clinically/visually significant progression of keratoconus.We will carry out exploratory analyses using KSensitivity analyses will be conducted on the primary outcome to assess the robustness of results to treatment cross-over or failure to receive any treatment following randomisation. In the event of cross-over from one randomised arm to the other, we will perform analyses of the primary outcome on a per-protocol basis. The per-protocol analysis will exclude any information collected from a patient after cross-over. This analysis will also exclude data for patients who did not receive the treatment to which they were randomised. Any cross-over or treatment deviations will be summarised with reasons.This update contains the pre-specified SAP for the KERALINK trial. By publishing the SAP, we aim to increase the transparency of the data analysis. The KERALINK trial will provide comprehensive and precise information on the relative effectiveness of cross-linking versus standard care in children with keratoconus.Additional file 1. Dummy tables. This file contains dummy tables which show the planned format and contents of the tables for the KERALINK final statistical report.Additional file 2. Guidelines for the Content of Statistical Analysis Plans in Clinical Trials Checklist (1) (recommended by the EQUATOR Network).


We aimed to compare the perception of health-related quality of life (HRQOL) and related factors in Spanish coeliac children and their parents, using two questionnaires, the generic KIDSCREEN-52 and the specific the Celiac Disease DUX (CDDUX), and to assess the correlation between them.Coeliac children, aged 8-18, who are members of the Madrid Coeliac Association (MCA) and their parents, answered the Spanish version of the CDDUX and KIDSCREEN-52 questionnaires via e-mail. CDDUX was answered by 266 children and 428 parents and KIDSCREEN-52 by 255 children and 387 parents. Linear regression models were fitted to evaluate the association of demographic and clinical factors with HRQOL scores. CDDUX scores were compared with the subjective perception of health status assessed by the first question of KIDSCREEN-52. The correlation between the questionnaires was analysed.We found that the main factors that negatively affected HRQOL were having social or economic difficulties associated with following the diet and having transgression-related symptoms. The maximum correlation between the questionnaires was 0.309 and − 0.254 in parents and children respectively.Although there is a poor correlation between the two questionnaires, both agreed that the main concerns of the respondents were related to the social and economic difficulties of following the diet. It would be interesting to use both types of questionnaires in order to perform a more complete assessment of HRQOL in coeliac children.Not applicable. Coeliac disease (CD) is an immune-mediated, systemic disorder due to gluten ingestion in genetically susceptible individuals . Gluten-The best way to evaluate HRQOL is using questionnaires. There are two main types, the generic and specific questionnaires. Generic questionnaires measure the daily life aspects of HRQOL in patients who have several conditions, whereas specific questionnaires focus on specific aspects related to the illness and its treatment .The two questionnaires chosen to perform our study were the KIDSCREEN-52 and the CDDUX. The KIDSCREEN-52 questionnaire was chosen because it is one of the most widely used generic questionnaires worldwide  and has Most HRQOL studies performed with coeliac children using either specific , 14, 16 The purpose of this study was to compare HRQOL outcomes and related factors  in a group of coeliac children and their parents by using both the generic KIDSCREEN-52 and the specific CDDUX questionnaires, which focus on different aspects of the disease. A second objective was to assess whether there is a correlation between the results obtained with both questionnaires.The study protocol was approved by the Clinical Research Ethics Committee of the Hospital Universitario de Fuenlabrada, Madrid, Spain. Informed consent was obtained from all parents or guardians and from all patients aged 12 years or older.This is a cross-sectional survey targeted at children from Madrid with CD aged 8 to 18 and their parents , 19. In The sample size was calculated according to the variance shown by the items in the questionnaire validation study . The samAs previously published elsewhere , 19, ourDemographic data recorded were: age, gender, parent educational level and living conditions. Clinical data collected were: age upon diagnosis, time since diagnosis, clinical presentation at onset, family history of CD, associated diseases and disease follow-up data such as adherence to treatment, difficulties in following the diet and whether symptoms reappeared when gluten was ingested.Participants were stratified by age upon questionnaire completion in: children (8-11 years old), young adolescents (12-15 years old) or older adolescents (16-18 years old), by age upon CD diagnosis (< 2–7 and >  7 years old), and by years since diagnosis .Factors related to QOL  were analysed according to the total number of answers provided by the 428 patients whose parents answered the CDDUX questionnaire and by 387 patients whose parents answered to the KIDSCREEN-52 questionnaire (41 parents only answered the CDDUX questionnaire).For the comparisons between both questionnaires, only the 387 and 255 parents and children’s matched CDDUX and KIDSCREEN-52 questionnaires were taken into account.The findings obtained in the univariate analysis using both questionnaires have been reported elsewhere , 19. ThiTwo questionnaires were used: the Spanish versions of the specific CDDUX Coeliac disease questionnaire and the generic KIDSCREEN-52 questionnaire.. The CDDUX scores were recoded into a scale from 1 to 100, with 1 being very bad and 100 very good. When using a 5-point Likert scale, a score of 1–20 is considered very bad, 21–40 is bad, 41–60 is neutral, 61–80 is good, and 81 to 100 is very good.The Spanish version of the CDDUX questionnaire, which was previously validated by our group , containThe Spanish version of the generic KIDSCREEN-52 questionnaire consists of 52 items covering 10 QOL domains , 10: “sop < 0.05). CDDUX scores (quantitative variable) were compared with the subjective perception of health status assessed by the first question of KIDSCREEN-52. To evaluate the correlation between the dimensions of both questionnaires, the Pearson correlation coefficient (r) and its statistical test were used.Kolmogorov-Smirnov (K-S) test was used to analyse the normality of variables. Results referring to the questionnaire scores in the different dimensions were provided as a mean (SD). Mean scores were compared by ANOVA according to the clinical and demographic variables included. Linear regression models were fitted to evaluate the association of the independent variables  with the dependent variables (HRQOL scores). Models were constructed including those variables which obtained a statistical significance in the univariate analysis . Significance was set at The flowchart with the population included in the study is shown in Fig. p < 0.001). (Table p < 0.001).  HRQOL CDDUX scores were 55. 5 SD 12.7) and 53.89 (SD 12.19) in children and parents respectively, with no differences detected in paired comparisons between the two groups, except in the dimension “having coeliac disease”, in which parents scored significantly higher than children (2.7 and 5p = 0.008) and having economic or social difficulties with adhering to the diet  were detected as factors related to a decrease in HRQOL scores in the total mean CDDUX scores. Children having symptoms with transgressions  and those with less than 4 years since diagnosis  scored lower in the “having CD” dimension. In addition, children younger than 2 years upon diagnosis scored better  only on the communication scale. Patients with adherence to treatment scored higher in HRQOL in the total score  and in the communication and diet dimensions  and parents’ (n = 387) CDDUX questionnaires regarding the subjective assessment of their health status (the first question of the KIDSCREEN-52 questionnaire) because the latter, in one single question, provides a general subjective idea about how respondents consider their own health status, so it is interesting to know if there is an agreement or not between the outcomes obtained with both questionnaires.We compared mean scores of the children’s (p < 0.01) in the total score and diet dimension. However, higher CDDUX scores observed in children and parents did not show a similar improvement in the health status assessment in the “having CD” dimension for children or parents nor in the communications dimension pertaining to the children  scores provided by 266 Spanish children with coeliac disease and parents (428) of children with coeliac disease using the specific CDDUX questionnaire. Supplemental digital content. Table 2. Health-related quality of life (HRQOL) scores provided by Spanish children with coeliac disease and parents of children with coeliac disease using the generic KIDSCREEN-52 questionnaire.