Chest X-ray (CXR) is the first-line investigation for lung cancer in many countries but previous research has suggested that the disease is not detected by CXR in approximately 20% of patients. The risk of lung cancer, with particular symptoms, following a negative CXR is not known.To establish the sensitivity and specificity of CXR requested by patients who are symptomatic; determine the positive predictive values (PPVs) of each presenting symptom of lung cancer following a negative CXR; and determine whether symptoms associated with lung cancer are different in those who had a positive CXR result compared with those who had a negative CXR result.A prospective cohort study was conducted in Leeds, UK, based on routinely collected data from a service that allowed patients with symptoms of lung cancer to request CXR.Symptom data were combined with a diagnostic category (positive or negative) for each CXR, and the sensitivity and specificity of CXR for lung cancer were calculated. The PPV of lung cancer associated with each symptom or combination of symptoms was estimated for those patients with a negative CXR.In total, 114 (1.3%) of 8996 patients who requested a CXR were diagnosed with lung cancer within 1 year. Sensitivity was 75.4% and specificity was 90.2%. The PPV of all symptoms for a diagnosis of lung cancer within 1 year of CXR was <1% for all individual symptoms except for haemoptysis, which had a PPV of 2.9%. PPVs for a diagnosis of lung cancer within 2 years of CXR was <1.5% for all single symptoms except for haemoptysis, which had a PPV of 3.9%.CXR has limited sensitivity; however, in a population with a low prevalence of lung cancer, its high specificity and negative predictive value means that lung cancer is very unlikely to be present following a negative result. Findings also support guidance that unexplained haemoptysis warrants urgent referral, regardless of CXR result. The relatively poor survival rates of people with lung cancer in countries such as the UK, compared with other countries, may be partly due to the disease being diagnosed in its later stages.3 draws on the positive predictive values (PPVs) of symptoms, which have been derived from cohort and case-control studies.4–6 NICE guidance recommends CXR for several symptoms in patients aged >40 years.3 In turn, findings on CXR that are suspicious of lung cancer warrant an urgent referral, which means the individual should be seen by a specialist within 2 weeks; this is known as a 2-week wait or TWW referral. As a result of its relatively high PPV — reported at 2.4% by William Hamilton4 — the presence of unexplained haemoptysis is considered justification to initiate a TWW referral irrespective of CXR results.In the UK, guidance from the National Institute for Health and Care Excellence (NICE) on lung cancer diagnosis7 estimated that CXR detects lung cancer in approximately 77%–80% of cases in the year before diagnosis; in addition, audits8,9 have suggested that negative CXR results in patients later diagnosed with lung cancer may contribute to a delay in diagnosis. The study presented here utilised routinely collected data for patients aged >50 years who presented to a service that allowed them to request a CXR  if they had any symptom that, according to NICE,3 warrants CXR.Although guidance for GPs is clear on when to consider a CXR and the action to take if an X-ray is abnormal, what GPs should do when CXR does not indicate grounds for TWW referral is less clear. A recent systematic reviewdetermine the sensitivity and specificity of CXR for lung cancer in patients with symptoms aged >50 years, who requested the investigation;estimate the risk (PPVs) of being diagnosed with lung cancer within 1 year and 2 years following a negative CXR result for a range of symptoms and symptom combinations; anddetermine whether the symptoms associated with lung cancer are different in those who had a positive CXR result compared with those who had a negative CXR result.The study aimed to:10In this prospective cohort study, the authors utilised routinely collected data that had been obtained between January 2011 and October 2016 from an SR-CXR service at Leeds Teaching Hospitals NHS Trust (LTHT), England. LTHT is a large trust, with radiology and lung cancer services, which serves a population of approximately 780 000.11 The relevant symptoms were cough, haemoptysis, dyspnoea, chest pain, weight loss, and change in voice. Patients who had received chest radiography in the previous three months were not eligible for SR-CXR. The data that were routinely collected included symptoms prompting CXR and smoking status, as well as the resulting CXR report.The SR-CXR service was set up as a component of an early-diagnosis campaign for lung cancer in Leeds, which was initially funded by the National Awareness and Early Diagnosis Initiative. The service allowed patients aged >50 years who had symptoms warranting investigation with CXR, as outlined in the NICE guidance, to have this investigation without requiring a GP referral.Patients who had a history of lung cancer prior to attending for SR-CXR were excluded, as were those who were diagnosed with an intrathoracic malignancy other than lung cancer in the 2 years following SR-CXR.12 These are summarised in Supplementary Table S1. To confirm satisfactory inter-reviewer reliability using this coding system, two authors independently coded a sample of 100 CXR reports; this yielded a Cohen’s κ score of 0.92, indicating a high level of agreement between reviewers.The written reports of all SR-CXRs were coded as positive or negative, based on the criteria used in a Royal College of Radiologists national audit.3 patients who had thrombocytosis (defined as a platelet count of >400 x 109/L) were identified if they had a full blood count (FBC) in the 1 year or 2 years prior to SR-CXR from the LTHT’s pathology system.A study database was created using symptom and smoking data from the SR-CXR service, which was supplemented with the codes allocated for each CXR report. For patients who underwent >1 SR-CXR during the study, each SR-CXR was considered a separate event. Demographic data were obtained from LTHT’s electronic patient records. In order to reflect NICE guidance,By cross-referencing with a database of all patients who received a multidisciplinary team-approved diagnosis of lung cancer in LTHT between 2011 and 2018, patients who were diagnosed with lung cancer between 1 year and 2 years following SR-CXR were identified. The presence or absence of a diagnosis of lung cancer on the database within 1 year and 2 years following SR-CXR was used to determine whether a diagnosis of lung cancer had occurred.The incidences of patients diagnosed with lung cancer within 1 year and 2 years following a negative CXR were calculated, followed by the incidences of lung cancer within 1 year and 2 years by each symptom and symptom combination; these are equivalent to the observed PPVs of each symptom or symptom combination. Patients who reported multiple symptoms were included in calculations for individual symptoms and also for symptom combinations.Estimates of PPVs adjusted for age, sex, and smoking status were obtained from the marginal distributions of separate logistic regression models predicting lung cancer diagnosis within 1 year of a negative CXR for each symptom and symptom combination. Adjusted PPVs were derived for diagnosis within 2 years using the same method. Comparing the average risk for people with a symptom with the average risk for people without that symptom allowed a percentage estimate of the additional risk of cancer for those with each symptom or symptom combination to be derived. All patients in the study population had symptoms, so non-smoking, female patients aged 50–55 years were used as a reference category as they had the lowest risk of lung cancer.A further logistic regression model predicting lung cancer diagnosis within 2 years was constructed for each symptom and symptom combination; this included the interaction between the indicator for that symptom/symptom combination and CXR result, as well as the main effects for both variables. These interactions explored whether the association of each symptom and symptom combination with lung cancer diagnosis differed between patients with a positive CXR and those with a negative CXR.Statistical analysis was undertaken using IBM SPSS Statistics (version 25).In total, 9367 SR-CXRs were performed during the 70-month study period. Of all records, 342 were excluded due to errors in the completion of patient information at the time of attending for SR-CXR, which meant that patient and CXR records could not be identified. An additional 16 patients were excluded because they had had lung cancer diagnosed prior to attending for SR-CXR, and a further 13 were excluded because they were subsequently diagnosed with intrathoracic malignancies other than lung cancer. The majority of these excluded cases were mesothelioma; however, due to data-suppression requirements to maintain the anonymity of patients, it was not possible to enumerate individual malignancies. Following the stated exclusions, the study population was 8996; characteristics of the study population are outlined in A total of 114 patients (1.3%) were diagnosed with lung cancer within 1 year of having the SR-CXR, of whom 86 (75.4%) had a positive SR-CXR result; the remaining 28 (24.6%) had a negative SR-CXR result . NegativObserved cancer incidence for patients with a negative SR-CXR for 1 year and 2 years following SR-CXR was 0.35% (95% CI = 0.22 to 0.48) and 0.71% (95% CI = 0.53 to 0.89), respectively  and 2. OThe 1-year and 2-year observed PPVs of symptom combinations for the entire study population, as well as the 2-year adjusted and observed PPVs for those who had a negative SR-CXR, are detailed in Supplementary Figures S1–S4.P-values >0.05).In total, 4135 and 5524 patients had FBCs obtained in the 1 year and 2 years prior to SR-CXR, respectively; of these, 217 and 395 had thrombocytosis. In all analyses of thrombocytosis with other symptoms, the lower 95% CIs for PPVs were all ≤0 or could not be calculated due to insufficient cases; as such, inclusion of thrombocytosis did not add any discriminative utility in this study. The 1-year PPVs for the entire study population for thrombocytosis were 1.03 (95% CI = 0.00 to 2.45) when in combination with cough, 2.17 (95% CI = 0.00 to 6.39) in combination with chest pain, and 6.67 (95% CI = 0.00 to 15.59) in combination with weight loss (data not shown). There was no evidence that symptoms’ associated risk of lung cancer differed according to the result of the SR-CXR  of 99%, CXR could be considered well suited to its role as a first-line investigation in a low-prevalence setting.The large sample size of the study population is a strength of this study, along with its prospective design. However, the study population had a low prevalence of lung cancer with only 154 (1.7%) diagnosed with the disease within 2 years, of whom 57 (37.0%) had had a negative SR-CXR result. This meant that insufficient cases were present to calculate PPVs for several symptom combinations. In addition, the lack of a control group meant the calculation of adjusted PPVs was calculated using a within-study comparator based on the patients at lowest risk of developing lung cancer.In determining which study participants developed the disease, the authors assumed that patients did not move outside of the region after having their SR-CXR and that diagnoses were recorded in LTHT. It is possible that this approach underestimated the prevalence of lung cancer if some patients moved and were subsequently diagnosed elsewhere.13–15 However, a small proportion of cancers develop more rapidly;16 therefore, the assumption that a lung cancer that was not detected on SR-CXR constitutes a ‘false negative’ result requires some qualification, particularly at the 2-year interval.It is also possible that some cancers diagnosed within 1 year and 2 years following SR-CXR were not actually present at the time of imaging. Although the natural history of undetected lung cancer is necessarily unknown, estimates derived from screening studies suggest that, in a large proportion of cases, lung cancer develops over some years prior to detection.3The study population had a CXR at their own request. There could be differences between the study population and the patient population who are referred for CXR by a GP. It is possible that the prevalence of lung cancer in the study population is lower than in patients who report their symptoms to a GP and are then referred for a CXR. If this is the case, the pre-test probability of lung cancer would be lower and result in lower PPVs and sensitivity, and higher NPVs and specificity, than in the referred population. In addition, although people in the study population were deemed eligible for CXR because they had symptoms listed in current NICE guidance, it is important to acknowledge that this guidance was based on GP appraisal of patient symptoms and did not envisage patient-requested investigations. The study population was also limited to individuals aged >50 years, while NICE guidelines suggests investigation for those with symptoms aged >40 years.4–6 and also suggests that haemoptysis remains an important symptom, even following a negative CXR.This study has confirmed the finding of previous studies that haemoptysis is the symptom most strongly associated with a subsequent diagnosis of lung cancer,4 undertook a case–control study that linked cancer registry data to GPs’ paper and electronic health record symptom records; that study did not report CXR results. Symptom PPVs for the population of the study presented here were much higher than those reported by Hamilton, although it is not known whether the populations are directly comparable; patients’ decisions to self-request a CXR may reflect a greater underlying concern that serious disease is present, compared with those presenting to a GP with symptoms prior to a decision having been made about investigation with CXR, or those who mention symptoms to a GP while attending for other reasons.Hamilton17 were not replicated in this study. The relatively small numbers of patients diagnosed with lung cancer and the number of those with thrombocytosis is likely to have rendered this study underpowered to detect such an association. Another possibility is that the population selected for a FBC and the population that requested a CXR in this study — that is, those selected for testing by their GP or those who opted to undergo a CXR themselves — were at already-elevated risk of serious disease compared with the wider population.18 This related mechanism of test selection bias may have obscured the capacity to differentiate elevated risk of thrombocytosis as well as patient self-selection to undergo CXR.Previous findings that thrombocytosis is associated with an increased risk of lung cancerThis study provides evidence to support existing guidance that advocates urgent referral for unexplained haemoptysis. The study also suggests that CXR’s present role as a first-line test for lung cancer in symptomatic patients is appropriate.Clinicians should understand that although a diagnosis of lung cancer is uncommon after a negative CXR, the high NPV of the modality reflects the low prevalence of lung cancer among those who have symptoms that are associated with the disease, since most of these symptoms are common and non-specific. Therefore, it is important to remember that CXR does not detect over a fifth of cases of lung cancer and that diagnosis of lung cancer following a negative CXR, though unlikely, remains possible. This insight supports the use of a safety netting approach even following a negative CXR, by advising patients to represent if their symptoms persist or worsen within a particular timeframe.Clinicians can, with confidence, inform patients who have not had haemoptysis that a diagnosis of lung cancer following a negative CXR is very unlikely and that the benefits of immediate further investigation are, in most cases, unlikely to justify the harms, costs, and inconvenience.As the precise level of risk that might be considered acceptable will vary between individuals and clinicians, a shared decision-making approach is prudent when considering what action to take when symptoms continue, despite a negative CXR. Clinicians should remember that, even in patients who appear to be at low risk, a negative CXR does not eliminate the possibility of lung cancer and, in some cases, further investigation should be considered if symptoms persist or evolve.


Self-assembly of iron(II) phthalocyanine (FePc) molecules on a Ge(001):H surface results in monolayer islands extending over hundreds of nanometers and comprising upright-oriented entities. Scanning tunneling spectroscopy reveals a transport gap of 2.70 eV in agreement with other reports regarding isolated FePc molecules. Detailed analysis of single FePc molecules trapped at surface defects indicates that the molecules stay intact upon adsorption and can be manipulated away from surface defects onto a perfectly hydrogenated surface. This allows for their isolation from the germanium surface. In fact, the hydrogenation is never perfect and some surface defects could be identified within the surface –65. ThesAfter deposition of FePc molecules onto a Ge(001):H surface at room temperature, we observe single molecules distributed over the surface, as well as extended molecular islands, as shown in 60 [2 [2 [We begin with the analysis of the molecular islands. First we note that in the case of smaller islands, a slight shift of the FePc island with respect to the underlying Ge(001):H substrate can be seen during scanning; this is shown by a white arrow in 60  and for 60 [2 . As indi60 [2  to 1.16 60 [2 . This is60 [2 , and mor60 [2 [2 , the forIn order to analyze in more detail the properties of the FePc islands, we consider now high-resolution imaging analysis. Already within the STM image in For clarity, the anticipated appearance of a single molecule is marked in 2 [At this point, it is worth mentioning that the observed structure does not correspond to any known FePc crystal phase. This indicates the influence of the substrate–molecule interactions on the crystal formation in the monolayer. The bulk α-FePc phase is characterized by columns within which the separation between the centers of the nearest FePc molecules reaches approximately 3.79 Å . While t2 . For claIn order to acquire information on the electronic properties of the FePc molecules within the islands on the Ge(001):H we have performed STS measurements. It is worth noting that the STM appearance of the individual flat-lying FePc molecules is different from that of the molecules in the FePc island. This finding suggests that the molecules are probably trapped at some surface defects and that the interaction is responsible for the modification of the STM-recorded contrast. One can also note that some molecules are clearly unstable during STM imaging. This makes the identification of the FePc molecules uncertain. In order to unambiguously identify single molecules, a single molecule manipulated and moved onto a perfectly hydrogenated Ge(001):H surface. This is shown in The appearance of the single FePc molecules described above and the fact that we did not record any means of Fe intercalation point to the adsorption of intact molecules. In contrast, on the Si(111):H surface, FePc molecules are reported to lose their central metal atom at room temperature .We have demonstrated that FePc molecules deposited on a Ge(001):H surface at room temperature self-assemble into extended monolayer islands comprising upright-oriented FePc molecules. The STS data recorded on the islands indicated a transport gap of approximately 2.70 eV, which is in good agreement with previously reported values for isolated molecules. Since the Ge(001):H surface contains atomic-scale defects, a fraction of FePc molecules was found flat-lying and immobilized at these defects. Such molecules could be displaced laterally by means of STM manipulation and placed onto a perfectly hydrogenated Ge(001):H surface, which provided sufficient isolation from the underlying germanium substrate. Based on high-resolution STM images, we have proposed a simplified model of the layer structure, which resembles molecular columns present in phthalocyanine crystals.+, 600 eV, 1020 K). The Ge sample was hydrogenated using a custom-built hydrogen cracker following the procedure described in [The whole experiment was performed in an UHV system equipped with a low-temperature STM manufactured by Omicron Nanotechnology GmbH. The Ge samples used in the experiments were cut from undoped wafers . After insertion into the UHV system the samples were sputtered and annealed for 15 min :H.


Despite the introduction of new insulin analogs, insulin pumps, and continuous glucose monitoring (CGM), young children with type 1 diabetes mellitus (T1D) remain vulnerable to episodes of hypoglycemia because of their unpredictable eating and activity patterns and high degree of insulin sensitivity. Caregivers and young children living with T1D learn to fear hypoglycemia because it is uncomfortable, unpredictable, and dangerous. Up to 60% of caregivers of young children with T1D report moderate to severe levels of fear of hypoglycemia, and caregiver fear of hypoglycemia relates to lower quality of life for families and suboptimal child glycemic control. Yet, until recently, there have been no studies reporting on a targeted intervention to treat caregiver fear of hypoglycemia in families of young children.The aim of this project is to conduct a randomized clinical trial of an innovative, video-based telehealth intervention to treat fear of hypoglycemia in caregivers of young children with T1D versus a relevant, age-appropriate attention control intervention.We created the Reducing Emotional Distress for Childhood Hypoglycemia in Parents (REDCHiP) intervention by merging age-appropriate T1D education and behavioral parenting strategies with cognitive behavioral therapy strategies that are effective for reducing fear and promoting adaptive coping. REDCHiP uses 10 video-based telehealth sessions that are a combination of group and individual sessions. We will recruit up to 180 families of young children with T1D to participate in this clinical trial from two pediatric diabetes clinics located in the midwestern and southern United States. Once families have been enrolled, we will randomize caregivers based on child age (age 2-3 years or 4-5 years), child sex, and family CGM use to participate in the REDCHiP or attention control intervention. Families will complete 3 assessment visits that coincide with study entry, end of treatment, and 3-month posttreatment. At each assessment visit, we will collect questionnaire data from caregivers, accelerometry data from caregivers and children, CGM data from children, and a blood sample to measure glycated hemoglobin levels from children.Recruitment began in July 2019, and enrollment is ongoing. The first wave of intervention delivery began in December 2019. We anticipate completing enrollment in 2023. Final reporting of results will occur within 12 months of the primary completion date.If the REDCHiP intervention is efficacious, next steps will be to examine multiple implementation strategies to determine how best to disseminate the intervention to pediatric diabetes clinics around the world.ClinicalTrials.gov NCT03914547; https://clinicaltrials.gov/ct2/show/NCT03914547PRR1-10.2196/17877 CognitiWe previously pilot-tested REDCHiP using a video-based telehealth approach. A total of 36 families completed the pilot intervention with low attrition , high attendance , and high caregiver-reported satisfaction . QualitaWe used two approaches to determine the content of our attention control intervention. First, parents of young children (ages 1 to 6 years) provided input by reviewing a list of potential topics and rating each topic on a 3-point scale according to its degree of relevance to them . We then selected the topics identified as very relevant by the majority of caregivers. Second, we asked 5 experts in young child development and clinical psychology to review our initial list of topics and the list of topics caregivers provided and provide recommendations for additional developmentally and age-appropriate topics. The final list of sessions includes topics relevant to young children , positive parenting strategies, and early literacy; caregivers do not learn about T1D-related topics. To complement the REDCHiP format, the attention control intervention consists of 7 group and 3 individual sessions that are each 30 to 60 minutes in duration .We carefully designed the trial to reduce the impact of several potential challenges. First, we anticipated a challenge in recruiting an adequate sample because in very young children, T1D occurs at a prevalence of 0.29 per 1000 patients . TherefoThis is a multisite trial and follows National Institutes of Health guidelines to establish and operate within a single institutional review board (IRB) for final study monitoring. All research personnel will complete certification in responsible conduct of research, good clinical practice, and safe handling of biological samples. To minimize risk for participating families, we will inform all potential families of the purpose, procedures, and amount of time required to participate in the trial. We will minimize risk of breach of confidentiality by using a Health Insurance Portability and Accountability Act (HIPAA)-compliant telehealth platform and reviewing a group confidentiality agreement at the first session. We will minimize the risk of child pain or emotional discomfort when collecting blood samples by allowing families to use their own lancet and/or coordinating the sample collection to occur just after a clinic-based finger stick. Finally, we will protect caregivers by reviewing their responses on the study surveys within 24 hours of completion and contacting those who report concerning levels of depressive or anxiety symptoms to provide information on treatment resources. The trial was registered at ClinicalTrials.gov [NCT03914547].The REDCHiP intervention builds on our previous research examining fear of hypoglycemia in caregivers of young children with T1D and fills a critical gap in behavioral intervention research in these understudied families. Moreover, the group-based telehealth approach is relatively novel for pediatric T1D interventions and may be easily scalable if the trial results confirm efficacy. We plan to disseminate the results of this trial to the pediatric diabetes community and broader medical community through national and international presentations at relevant scientific meetings and peer-reviewed manuscripts. We believe that there will be greater use of telehealth to deliver behavioral treatments for families of youth with T1D in the future because of the increasing adoption of telemedicine parity laws across the United States  and incrFor this randomized clinical trial, we will recruit 180 families of young children with T1D. Inclusion criteria are child aged from 2 to 5.99 years, T1D diagnosis ≥6 months, and use of an intensive insulin regimen . Exclusion criteria are caregivers of children on a conventional insulin regimen, children who have an allergy or sensitivity to the adhesive and/or skin preparation used for a CGM, children with a comorbid chronic condition , and caregivers who do not speak English.We will recruit into the study at least one caregiver  who is primarily involved in the child’s daily T1D management. Each site will apply standardized recruitment procedures approved by a single IRB to achieve the target sample. We will use a combination of in-clinic and telephone recruitment. Families who express interest in participating via telephone will complete IRB-approved telephone informed consent procedures including an approved eConsent developed in the Research Electronic Data Capture (REDCap) system ,62. FamiThis study will use a 2-arm randomized attention control design with 90 families recruited to each of the REDCHiP and attention control intervention conditions. We will stratify caregivers based on their child’s sex, child age (2 to 3 years versus 4 to 5 years) and CGM use (CGM or no CGM) and randomize to condition using blocks of eight. Recruitment and randomization will occur simultaneously across all participating sites using site-specific randomization envelopes prepared by the study biostatistician. This strategy will allow us to populate groups using caregivers from any site, thereby improving our recruitment efficiency and minimizing possible clinic effects.1c level. Finally, we will determine if caregivers need to borrow any equipment to participate in the intervention . After study visit 1, caregivers will begin participating in video-based telehealth sessions according to their group assignment . Caregivers will participate in 10 video-based telehealth sessions administered during 13 weeks via a HIPAA-compliant telehealth platform that permits multiparticipant video teleconferencing so that each caregiver can both see and hear other caregivers. During weeks 14 to 15 of the trial, all families will engage in study visit 2 to complete posttreatment surveys online, collect accelerometry data from children and caregivers, collect child CGM data and finger stick blood sample to measure child HbA1c, and recover any loaned study-related devices. Approximately 12 weeks after study visit 2, all families will complete study visit 3, which will involve final data collection: online surveys, child CGM data, and a finger stick blood sample to measure child HbA1c  how to place the research-grade accelerometer on their child’s nondominant wrist  or ankle (to measure child sleep) and how to place the accelerometer on their own nondominant wrist to measure caregiver sleep. We will also teach caregivers how to upload glucometer and insulin pump data from home to a central study database using a commercially available data aggregating system (Glooko). In cases where caregivers cannot use the data aggregating system because of problems with device compatibility, we will collect .csv files. To measure children’s daily glucose levels, we will place a FreeStyle Libre Pro (Abbott Laboratories) CGM sensor on the child’s upper nondominant arm. We will collect a finger stick blood sample from children using a reliable mail-in kit to measure a baseline HbAld HbA1c .1c kits) or recruit families who self-refer based on word of mouth or ClinicalTrials.gov. All participating caregivers and children will receive compensation for study visits 1 through 3. As needed, the study will use prepaid postage boxes for families to return study-related devices  to further reduce the burden on participating families.Of note, the study protocol will enable families to complete study visits either in their home, at the diabetes clinic, or at another location . Additionally, our protocol includes strategies to retain families in the study in the rare event that they move away  to examine child glycemic control and glycemic variability, respectively. To test for treatment outcomes, we will model study visit 2 scores as a function of visit 1 scores, condition (REDCHiP versus attention control intervention), and selected covariates  in mixed models that include a random intercept to account for clustering of participants within group cohorts. We will accommodate nonnormal outcome variables with log transformation, modeling with an appropriate generalized mixed model or nonparametric test. We will test the effect of condition based on whether the 95% confidence interval for the condition coefficient includes zero . To determine long-term treatment effects, we will test for sustained improvement on outcome variables by modeling scores at study visit 3 as a function of visit 1 scores and condition (REDCHiP versus attention control intervention). To control the overall rate of type I errors, we will conduct these analyses only for variables with a statistically significant condition effect in our primary analysis of treatment outcomes.We will measure parenting stress and caregiver fear of hypoglycemia using the Pediatric Inventory for Parents  and the ij = rXij + ui + ES×Conditioni + εij, where Yij is the posttreatment score for the jth participant in the ith cluster, r is the within-cluster correlation between baseline and posttreatment scores (set to .72 based on pilot data), Xij is the ijth participant’s baseline score, ui is the normally distributed random intercept for the ith cluster, ES is the standardized effect size (set to 0.6 standard deviations based on pilot data), Condition is an indicator , and ε is a normally distributed error term. The variances of ui and εij were set to yield a within-condition intraclass correlation coefficient of .10. We fit a mixed model with a random cluster intercept and baseline score and condition as predictors to each of 1000 simulated data sets. To compute estimated power we took the average number of data sets for which the condition was statistically significant in a 2-sided test at α=.05. Based on the indicated parameter values and sample size, estimated power was 83%. For 40 groups averaging 3.6 families each, estimated power was 90%. Recognizing this trial will include a control group, which could attenuate our REDCHiP effect, if we conservatively reduce the standardized effect size to 0.4 standard deviations, estimated power is 85%.We anticipate recruiting 180 families, allowing for a 20% attrition rate and resulting in a final goal of completing the trial with at least 144 families. We assessed power using a simulation study; each simulated dataset contained 40 clusters  of 4 to 5 participants each. We simulated posttreatment scores using the following model: YRecruitment began in July 2019, and enrollment is ongoing. The first wave of the intervention began in December 2019. We anticipate completing enrollment in 2023. Final reporting of results will occur within 12 months of the primary completion date.This trial will focus on families of young children with T1D and exclude children who are on conventional insulin therapy, which could limit some of its generalizability. However, we anticipate little negative impact from this decision because intensive insulin therapy is now the gold standard regimen for individuals with T1D. An examination of maintenance effects of REDCHiP up to 3 months’ posttreatment will occur, but we will not be able to draw inferences regarding longer term effects. Our trial includes numerous outcome measures, which increases the risk of missing data. We have attempted to reduce the risk of missing data through device use  and online questionnaire access and data sharing. We intend to give caregivers free and accessible technology support before and during telehealth visits in order to reduce any disruptions due to difficulties with the telehealth platform. We intend to stratify our samples according to child sex, age, and CGM status to control for any effect of these variables in our final models. Yet there may be other demographic variables we will not be able to control for a priori that moderate caregiver fear of hypoglycemia, and we may need to adjust for those in our final analyses. In addition, we intend to identify whether young children are using a hybrid closed loop or predictive low-glucose management system via caregiver report or electronic health record review and may adjust for these variables in our final analyses.Our experience studying fear of hypoglycemia in families of young children with T1D reveals that this is a common fear for caregivers that is related to poorer quality of life and suboptimal child glycemic control. Our initial pilot study revealed promising effects for using cognitive behavioral techniques and a group-based telehealth approach to reduce caregiver fear of hypoglycemia. If this larger, multisite trial confirms the efficacy of REDCHiP and the acceptability and feasibility of our telehealth delivery method, future directions will focus on widespread dissemination of the REDCHiP intervention and implementation across a variety of clinic settings.


This paper estimates the impact on childhood drowning rates of community-based introduction of crèches or playpens or both in rural Bangladesh for children aged 0–47 months.A baseline census of the whole population of 270,387 households in 51 unions, 451 villages from 7 rural sub-districts in Bangladesh was conducted in 2013. The baseline census determined retrospective, age-specific, and cumulative drowning incidence rates (IR) experienced in the target households in the 12 months prior to the intervention. Beginning in late 2013, creches for drowning prevention were established across the study area. Acceptance into creches was provided and written assent to attend a creche was obtained for all children aged 9–47 months in all participating unions. Playpens were provided to 45,460 of these children, of which 5981 children received only the playpens. All children were followed-up until their 48-month birthday or administrative censoring (fixed timepoint to stop observing the drowning deaths), after a two-year implementation period (2014–2016). Drowning IR were estimated for children and compared to corresponding baseline rates from 2012. Age-specific drowning IR under different “as treated” categories  were compared to the baseline rates experienced by the categorized households prior to intervention.A total of 3205 creches (average of 7 creches per village) were established, and 116,054 children aged 9–47 months were exposed to the intervention packages. Aggregated drowning IRs between age 0 and 47 were estimated per 100,000 population per year at 86.73 (95% CI: 69.67–107.97) and 43.03 (95% CI: 35.55–52.10) in the baseline and post implementation period, respectively. Risk ratios were 0.40 (95% CI: 0.28–0.57) overall, and 0.34 (95% CI: 0.13–0.90), 0.09 (95% CI: 0.02–0.36), and 0.04 (95% CI: 0.002–0.60) in children under the creche-only, aged, 1, 2, and 3 years old respectively. Inexplicably, drowning rates were statistically significantly higher post-intervention in children 0-11 months. There was no mortality reduction with playpen use , and this group may actually have had a higher risk of drowning.Creches are effective for preventing childhood drowning in rural Bangladesh for children above age 1-year, and should be considered for further scale-up. Deaths from communicable diseases have declined over the last decade globally, but the proportion of deaths resulting from injuries has steadily increased over the same period  were 82% less likely to drown compared with those who did not participate  program, is to measure the large-scale impact of a streamlined package of drowning interventions on overall and age-specific drowning mortality rates. The package comprises largely three components: a creche-only, a playpen-only, and a creche plus playpen component; each combined with the establishment of injury prevention committees to provide community education on injury prevention. A secondary goal of the study is to determine which sub-component of the intervention package may have had the most impact. It is hoped that this study will fill the knowledge gap on evidence for large-scale effectiveness and implementation of drowning prevention interventions among under-five children in LMICs.The primary goal of this current study, called The study was implemented in seven purposively selected rural sub-districts of Bangladesh – Matlab North, Matlab South, Daudkandi, Chandpur Sadar, Raiganj, Sherpur Sadar, and Manohardi , stakeholders’ advocacy meetings were led by trained project staff at the sub-district, union and village levels . The stakeholders at the meetings included political leaders, representatives appointed to sub-district and union councils, and community leaders. The meetings were conducted to sensitize the communities to the high burden of childhood drowning in Bangladesh, and to raise awareness on the need for action.Over the next period (June 2013 – November 2013), a baseline census was conducted to cover the entire study area, including 51 unions and all 1.2 million people. The baseline census collected information on social and demographic characteristics, care-seeking behavior, health outcomes, including retrospective mortality by cause for children who died between ages 0 and 47 months in the 12 months prior to the census . Ten percent of completed paper forms were randomly selected and reviewed for any inconsistencies, and validation rules were set to prevent inconsistencies and errors during the data entry for the tablet-based system. Monthly data quality assurance meetings were conducted between data collectors and supervisors to further review the paper-based data. The central database was also reviewed frequently for any inconsistencies, and these inconsistencies were corrected after discussion with the field team in real-time.Prior to the roll out of the intervention package, additional rounds of stakeholders meetings were conducted by project staff in each of the 7 sub-districts, 51 unions and 451 villages starting from October 2013. These meetings were conducted to sensitize the stakeholders to the package of interventions, and to establish injury prevention committees at the union and village levels. The union injury prevention committees (UIPCs) and village injury prevention committees (VIPCs) were established to provide ongoing community education on injury prevention, and to facilitate childhood drowning and injury prevention activities in their communities. Each UIPC comprised of elected representatives from that union, including a chairman and 10 other council members. Each VIPC included 5–7 members, comprising elected representatives, community leaders and parents of young children. All UIPC and VIPC members received a half-day orientation and training on injury and drowning prevention provided by project staff. The UIPCs and VIPCs were assigned to meet once every month to discuss and plan injury prevention activities, and their activities continued throughout the study period. Annual meetings were also held at the sub-district levels to appraise political leaders on the progress of the project and to discuss implementation issues that required political support.Based on the recommendations of each VIPC, private homes of selected female community members were identified as creches for childhood drowning prevention for children in the study area. These crèches were tuition-free daycare centers that operated between 9 am and 1 pm daily, 6 days a week, and were required to meet specific criteria. A creche should consist of a room with secured doors and windows, clean and carpeted floors, adequate light and ventilation, and be equipped with age appropriate toys and educational supplies. Each creche was supervised by two trained workers (a crèche mother and her assistant). The creche workers were trained to keep all children within reach and under direct visual contact at all times. They were also trained on early childhood development methods, injury prevention, record-keeping (including keeping track of the number of children), and how to maintain adequate health and safety standards. Each creche was staffed to accommodate 24–30 children . Area 1 comprised 3 sub-districts: Raiganj, Manohardi and Sherpur sub-districts. Area 2 comprised 4 sub-districts: Matlab North, Matlab South, Daudkandi and Chandpur sub-districts. No sub-district in Area 1 borders a river, but all of the districts in Area 2 border either the Padma or Meghna river. Area 1 was supported by one non-governmental organization (NGO) and Area 2 had a different NGO. Each NGO was responsible for data collection and delivering the intervention package in their respective area. Both NGOs had experience working in the area that they were assigned, and had a long familiarity with the local communities.All children aged 9–47 months (the age bracket with the highest risk of drowning deaths in Bangladesh)  on October 6th, 2015 and November 3rd, 2015 in Area 1 and Area 2, respectively. The censor dates marked the end of 2 years of implementing the interventions in both areas. Based on the uptake of interventions, all children aged 9–47 months in the study area could be classified under 3 different treatment categories: creche-only, playpen-only, and creche plus playpen categories; each combined with the establishment of injury prevention committees to provide community education on injury prevention at various levels.The primary goal was to estimate the change in drowning deaths. A pre-post design was used to compare fatal drowning outcomes for children overall and by age group and by study area between pre-intervention and post-intervention periods. We specifically compared cumulative incidence of drowning at baseline assessed over one-year recall from 2012 to 2013 with annualized cumulative incidence of drowning during surveillance from 2013 to 2015 after implementing the interventions.A secondary goal was to attempt to determine which components of the intervention were the most effective—playpen-only, creche-only or creche plus playpen. Achieving this secondary goal was much more challenging because playpen acquisition (and use) and creche attendance were subject to self-selected uptake. Ultimately, parents and caregivers decided how much to use playpens and creches. Because it was a pragmatic trial, the study did not establish a group of post-intervention controls who were denied entry to creches and/or denied access to playpens. Given the limitations of drawing inferences in the presence of selective intervention uptake, we still pursued our secondary goal by comparing cumulative incidence of drowning of children under the different treatment categories with cumulative incidence of drowning of their older siblings (or younger versions of the treated children) during the 12 months prior to the baseline census. We classified households in the baseline data into categories based on the treatment  that they ultimately entered by the end of the study. We thus calculated baseline drowning rates by treatment category by examing survival patterns of the untreated older siblings and younger versions of the treated children (i.e. eligible children identified at baseline and who would ultimately receive various categories of the treatments by the end of the study).Ex-ante sample size calculation suggested that at least 24,000 children would be needed under each treatment category to have 80% power of detecting a 50% decline in drowning specific incidence rate compared to baseline rates.Although treatment with playpens and creches was not provided to children under age 9 months, we calculated drowning incidence from birth until 48 months in order to detect spillover effects of the community education on children for whom that was the main treatment. Drowning rates are known to be low during the first year of life, thus we fully expected that precision and statistical power would be lower when comparing drowning incidence across groups under the age of one.Data from the baseline census were used to estimate cumulative incidence of drowning per 100,000 population per year for all children less than 48 months in the study area, and these were categorized by sex, age group, and implementation area (Area 1 or 2) as well as the treatment eventually received by that household after the creche and playpen interventions started. Similarly, cumulative incidence of drowning per 100,000 population per year was estimated for all children less than 48 months in the study area during 2 years of exposure to the interventions using the surveillance data. The cumulative incidence of drowning from the surveillance data during intervention were further categorized by sex, age group, implementation area (Area 1 or 2) and by the treatment that was received. Comparing rates across these categories of demography and treatment condition offers a “within-household-type” comparison of drowning rates that helps to attenuate self-selection bias due to household specific factors driving selection into treatment. If there were factors leading some households to be more (or less) interested in pursuing the use of creches and playpens these same factors would be attached to the observations of drowning risk of the older siblings (or younger versions of the treated children) making up the untreated comparison groups reporting drowning events and exposure to risk in the year prior to baseline. At the time of the baseline data collection, nobody could predict which household would ultimately select into creche-only or playpen-only or both. However, ex-post, that classification could be made retrospectively and this permits the baseline data to help serve as a control group for the respective treatment groups.Unadjusted cumulative incidence ratios were estimated comparing estimates from the baseline and surveillance data. Using pooled baseline and surveillance data, Poisson regression model for panel data was used to estimate adjusted cumulative incidence ratios for children aged 9–47 months under each treatment category. The Poisson model used generalized estimating equations (GEE) with population-averaged estimates. The Poisson model adjusted for sex, age group, and implementation area (Area 1 or 2). The standard errors were adjusted for heteroscedasticity using options for robust standard errors with exchangeable autocorrelation function. All statistical analysis was implemented in Stata 14 I/C  in year 2  were established, and 5600 VIPCs members were trained. A total of 3205 creches (average of 7 creches per village) were established over the study period, and 55,790 playpens were procured and distributed; 38,971 of these playpens were wooden playpens , while the remainder were plastic playpens (distributed in Area 2). A total of 1600 creches were established in Area 1, and 1605 in Area 2.The baseline census conducted in 2013 included 92,940 children aged 0–47 months, 91% of these were children aged 12–47 months and 51% were male  in the 0–11 month age group during the intervention period. For all other age groups, the risk was reduced during the intervention period  aged 0–47 months that were assigned to a treatment category at baseline. A total of 78  occurred among households with children who ultimately received the creche-only treatment by the end of the study. No deaths occurred among those households with children who ultimately received the playpen-only or creche plus playpen category Table .The adjusted cumulative incidence ratio for the creche-only category was 0.12  comparing children who received the intervention with their older siblings (or younger versions) Table .Table 3A package of community-based interventions for drowning prevention was successfully implemented on a large-scale under real world conditions for over 100,000 children aged 9–47 months in rural Bangladesh. The package of interventions was associated with a significant reduction in the incidence of drowning deaths among children aged 12–47 months compared to baseline rates. The interpretation of findings from a pre-post comparison requires the assumption that the mortality reductions were not entirely due to a secular trend. With smaller family sizes and improvements in living standards such trends are not implausible. However, a review of very recent evidence shows that there are no signs that drowning mortality has been declining substantially in rural Bangladesh over the last 10 years  of the playpen alone (or in combination with the creche) due to a limited sample size, but did observe higher drowning rates for those children under the playpen-only package (aged 9–24 months), similar to rates reported in the literature for children aged 0 -11 months in rural Bangladesh , and be willing to take more risk in their routine decisions around drowning hazards (risk compensation) Hedlund , inadverIt is important to highlight that while both creche and playpen involved community engagement through the activities of the UIPC and VIPC, the implementation of the crèche required more active involvement of the VIPC at various phases. Through the VIPC, the community participated actively in establishing the crèches, recruiting crèche mothers and children, and providing support to the day-to-day running of the crèches. Hence, the VIPC created a platform for effective delivery of crèche services through social action. Such social actions have been previously recognized as important factors for improving service delivery and health outcomes, and for far-reaching community-wide effect (Peters et al. We found that infant deaths were unexplainably higher during the implementation period compared to the baseline. It is possible that the baseline rates were underestimated given the potential for information and recall bias at baseline relative to data collected through active surveillance over a much longer implementation period. The preponderance of rivers in Area 2 may have also played a role in these infant deaths – since the deaths disproportionately happened in Area 2. We explored gender analysis and found that the proportionate deaths were not significantly different comparing male and female infants, and we do not have any reasonable evidence to suggest that the intentional deaths of female infants or other forms of infanticide took place during the implementation period.This study has limitations. Caregivers self-selected into different treatment categories given that the study was not randomized; and a differential mortality rate may exist between children whose parents chose different intervention packages due to unobserved factors that may have been associated with higher or lower drowning risk. Hence, interpreting patterns of drowning mortality between treatment categories may be biased. Two types of self-selection bias might create two types of bias. Under adverse selection, parents at most risk due to social vulnerability and reduced risk perception would be less likely to adopt creches or playpens, and this would associate the intervention packages with a higher drowning hazard. On the contrary, under positive selection, vigilant parents who had already done everything in their power to lower their child’s drowning risk would selectively adopt the creches and playpens as well, and this would associate the intervention packages with spuriously lower drowning hazard. Ex ante, we cannot predict which form of selection bias would occur or which would dominate. We can only advise extreme caution in interpreting the patterns in drowning mortality when categorized into the treatment categories that parents ultimately selected. One strategy that we used to help to reduce selection bias was be to compare the pre-intervention drowning rates for each of the treatment categories ascertained by the 1 year retrospective survey at baseline to the post-intervention drowning rates that occurred during the intervention for the same household by each category. Under the assumption that the unobservable reasons that parents chose the way they did remained constant over time, this pre-post comparison would reduce the impact of selection bias in this study.Misclassification of drowning deaths under the different treatment categories and age group is possible given the variable data quality upon which the analysis was based. To minimize misclassification errors, information on the dates of birth and death were triangulated from multiple data sources for all study participants, and manually cross-checked for all incident cases. In addition, all information for incident cases under the playpen category was cross-validated by re-interviewing respondents at the time of data analysis.There were also differences in the implementation of the interventions due to different implementing agencies, and in the type of playpens used in Area 1 and 2. The study findings also suggest that the interventions may have had variable level of intensity across the study population, which is not uncommon for large-scale programs (Kilbourne et al. The findings of this study can be reasonably generalized to the non-studied unions and other rural sub-districts in Bangladesh given similarities in their contexts with the study area. One factor that might not be generally available in all parts of Bangladesh would be the presence of an implementing NGO with the capacity to convene stakeholders and engage the community members themselves in promoting the educational messages and utilization of the creches. The study findings could also be generalized to other LMICs with similar characteristics and implementation capacity to our study areas in rural Bangladesh.Community-led crèche interventions are effective at scale for reducing drowning deaths among children in a rural LMIC setting like Bangladesh and should be considered for further scale up to reduce the global burden of drowning among under-five children especially in LMICs. Playpen-only may not be effective for drowning prevention among children, and additional implementation studies are required to further explore playpen implementation and other barrier strategies for childhood drowning prevention in LMICs. More research studies are also needed to explore the dose-response effect of the creche package for different age groups and to further streamline the package to identify its simplest form. We recognize that the equivalence of the level of supervision provided by paid creche workers may not be available or affordable to most caregivers in rural Bangladesh. Therefore, it is important for government, donors and civil society organizations to recognize creches as important social services which can help save lives from drowning, and not leave the provision of these services to the market entirely, but play their part in providing these services at scale.Additional file 1: Appendix 1. Instructions to caregivers on the use of the playpens.


In the setting of hematopoietic stem cell transplantation, donor-patient HLA matching is the prime donor selection criterion. Matching algorithms provide ordered lists of donors where the probability of a donor to be an HLA match is calculated in cases where either donor or patient HLA typing information is ambiguous or incomplete. While providing important information for the selection of suitable donors, these algorithms are computationally demanding and often need several minutes up to hours to generate search results. Here, we present a new search kernel implementation for Hap-E Search, the haplotype frequency-based matching algorithm of DKMS. The updated search kernel uses pre-calculated information on donor genotypes to speed up the search process. The new algorithm reliably provides search results in <1 min for a large donor database (>9 Mio donors) including matching and mismatching donors, even for frequent or incomplete patient HLA data where the matching list contains several thousand donors. In these cases, the search process is accelerated by factors of 10 and more compared to the old Hap-E Search implementation. The predicted matching probabilities of the new algorithm were validated with data from verification typing requests of 67,550 donor-patient pairs. Matching of donor and patient human leukocyte antigens (HLA) is a primary factor for patient outcome after hematopoietic stem cell transplantation –3. Over In order to overcome this problem of missing or ambiguous data, several prediction tools—Haplo Stats at NMDP  and EasyWMDA has developed a framework for the implementation of HLA matching software  and carrOne drawback of standard matching algorithms is the poor runtime performance for large donor databases and haplotype datasets. It is desirable to accelerate the search process to make the donor selection workflow more efficient.Here, we present the completely revised search kernel for Hap-E Search that meets the challenges of searching more than 9 million registered potential stem cell donors in an efficient way.Like HapLogic and OptiMatch, Hap-E Search uses standard formulae describing relations between haplotypes and genotypes in incompletely typed individuals to determine matching probabilities. Details are given in the Since 2011, the haplotype frequency-enhanced search algorithm Hap-E Search has been used at DKMS , 18. TheNevertheless, with a continuously growing donor database and larger haplotype datasets, the tree-based approach is no longer sufficient to provide search results in adequate time, i.e., <1 min. Especially for donor searches that result in several thousand donors, search time increases substantially. With the new search kernel, we want to tune the performance of the search kernel and optimize user experience by providing search results in <1 min even for computationally demanding donor searches.Our basic approach to improve the performance of Hap-E Search is to reduce the computational load during each donor search: the workload is transferred to a preparation step that has to be performed once before the search kernel gets operational. In this preparation step as much information as possible is stored and can then be re-used in every search, thus further reducing the time a user has to wait for search results. This approach is applied to potential donor genotypes based on HLA typing, as they are required in every search and change in rare cases only. In the preparation step before the new search kernel gets operational, we compute and store genotype information for all donors in the database. Afterwards, the donor information is constantly kept updated to the current state of the donor database.*01:01 and A*01:01:01G would be assigned to different groups even though they are identical on antigen recognition domain (ARD) level. For our database, this grouping reduces the number of donors in the search kernel by 36%. More sophisticated grouping on ARD level was not implemented, as it improves the grouping effect only slightly (donor number reduction by 40% for our database) at much higher computational cost. For each representative, potential genotypes according to HLA typing results and underlying haplotype data are calculated  matching probabilities are calculated for all 10/10 (9/10) matched representative donors. The matching probabilities can be calculated efficiently see  by interIn a last step, results for representative donors are expanded to the full donor set.For donors or patients with HLA typing that cannot be represented by the used haplotype data, we approximate matching probabilities by assuming minimal frequencies for the “unknown” genotypes. The minimal frequency is dependent on the sample size used for calculation of population haplotype data. For ambiguous typing results, all possible “unknown” genotypes are weighted equally. Details are given in the There is an ongoing discussion whether HLA mismatches of loci that are homozygous for both patient and donor should be counted either as one or two mismatches. Therefore, this parameter can be changed in our search algorithm via a control variable. The default is one mismatch as specified in WMDA guidelines .*01:04N, A*34:01:01G matches A*01:01:01G, A*34:01:01G, as A*01:04N is part of the G-code A*01:01:01G , this “null portion” of the multi-allele code is treated as homozygous for matching. This means that in addition to the standard matching of expressed alleles, the typing A*03:01+A*02:MRVF will also match like homozygous A*03:01+A*03:01 through its null portion A*03:01+A*02:125N , 1 million donors and 12,407 haplotypes (setting 2), and 1 million donors and 34,071 haplotypes (setting 3). All haplotypes in these settings were from the German population.All tests were executed on an Oracle Database (Oracle Version 11.2) hosted on an Intel Xeon E5620 CPU .On our productive system, searches can be processed in parallel on five dedicated oracle databases.The new search kernel was validated both on simulated data provided by WMDA and on real donor-patient data from verification typing (VT) at our donor center, which is the patient-driven high-resolution retyping of donors upfront of an HSCT.WMDA has developed a cross-validation set for the validation of haplotype based search algorithms . It consMatch predictions of the new search kernel were validated by comparing probability forecasts and actual match status of 67,550 donor-patient pairs from VT requests. Only donor-patient pairs where high resolution typing at all five match loci was available for both patient and donor after VT were used for validation. Donor typing at time of request and after VT were required to be consistent, such as to exclude typing errors from the validation samples.10/10 and 9/10 matching probabilities were calculated with the new Hap-E Search kernel using the donor typing at time of VT request and German haplotype frequencies. Matching probabilities were rounded to integer percentage values and grouped into 22 classes in probability interval steps of 5%. Samples with a predicted 10/10 (9/10) probability of 0 or 100% were grouped into separate classes, as they account for most of the cases. The portion of VT confirmed matches within each class was then compared to the predicted value. For evaluation of the 10/10 matching probabilities, samples were excluded where the donor already had a confirmed mismatch to his patient prior to VT. This left 54,065 donor-patient pairs for validation in the 10/10 match category.Preparation of donor data has to be performed once for the existing donor database before launch of the new search kernel. We compared data preparation for three different settings . In bothDepending on the setting, the time required for initial preparation ranged from 6 h 39 min to 28 h 53 min. For the settings tested, preparation time scales approximately proportional with size of donor database and number of haplotypes: For five times as many donors, preparation time increases by a factor of 4.3, while a 2.7-fold extension of haplotypes leads to an increase in preparation time by a factor of 2.3.After launch of the search kernel, the genotype data must be kept updated permanently. In all three settings, about the same time (≈200 ms) is needed per updated or added donor for this process. Preparation time per donor is longer for the update step than in the initial preparation due to database administration overhead like table constraints, indices, etc.Assuming a total of 1,000,000 new or updated donors per year , the cost of genotype data update would be <9 min per day in all three settings.We compared the performance of the new vs. the old search kernel for 50 searches as described in section comparison with the original Hap-E Search kernel. A direct comparison is difficult, as the two algorithms do not produce the same extent of search results: the new search kernel provides matching probabilities for all potential 9/10 and 10/10 matching donors, whereas the old search kernel excludes potential 9/10 donors where typing information is only available for loci HLA-A and HLA-B due to runtime issues. Therefore, the number of search results is considerably higher for the new compared to the old search kernel.Compared to the former search kernel that used a tree-based approach, the match process is speeded up considerably . The morWe compared the average search duration for 15 arbitrary patients with 5-locus high-resolution HLA typing in the three settings described in section performance in different settings . All seaAs expected, preparation of patient genotype information  is indepTotal search duration scales favorably with size of the donor database and number of used haplotypes: on average, it is doubled when the search is performed for a five times larger donor database. Average search duration increases by 50% when haplotypes are extended by a factor of 2.7. This makes it feasible to apply our approach to large and growing donor databases, and to improve calculated matching probabilities by using larger haplotype datasets.We performed the matching validation on the simulated WMDA data  to evaluate our new algorithm. Matching probabilities for all 10,000,000 donor-patient pairs were identical to the consensus data, except one. For the pair P000656–D009637 we observed a deviation of 1% in the 9/10 probability (87 instead of 88%) due to rounding, which is within tolerance of the validation criteria. Average search duration per patient within this predefined donor-haplotype setting was 4.3 s with a standard deviation of 1.5 s and a median of 3.9 s. Maximum search duration was 17.7 s, whereas the fastest search took 2.7 s.The results for the validation of matching probabilities predicted by Hap-E Search on actual donor-patient data from VT is shown in For the 10/10 match category , 93% of The data confirm the validity of our predicted matching probabilities: 21/22 data points are within 95% confidence interval for the 10/10 match category, and 17/22 data points for the 9/10 match category.This work focuses on improving the software side of the donor selection process: we present a new approach that tuned the performance of our matching algorithm Hap-E Search for a more efficient workflow within standard donor center and registry activities at DKMS.We tested data preparation and search performance for three different settings to estimate the behavior of the algorithm depending on the number of donors and haplotypes. Data preparation time scales approximately proportional to both parameters, which is beneficial especially for large donor databases. However, the duration of initial data preparation is of minor importance (as long as it does not deteriorate): it needs only to be performed once before the launch of the search kernel or after changes of the haplotype basis. This preparation can be performed in the background so that the search service does not need to be interrupted in the meantime.More importantly, the time needed for the constant update of the database was negligible with <9 min per day  in all settings.The duration of donor searches scales favorably with the number of donors in the database as well as the number of used haplotypes, which makes it feasible to apply our approach to large and growing donor databases, and to improve calculated matching probabilities by using larger haplotype datasets. The algorithm is programmed in a flexible way, so that haplotype data can easily be extended to improve the accuracy of matching probabilities.The new search kernel has been operational since September 2018 and provides fast and robust search results. Currently, the donor database contains over 9 million donors from six different DKMS entities in Germany, Poland, United Kingdom, United States, Chile and India. At the moment, we run over 3,000 new searches per month, of which ~500 are registry searches and 2,500 are searches connected to donor center activities. The majority of searches in the registry are triggered via the European Marrow Donor Information System (EMDIS) , 20. NinFor all active searches, search results are updated on a daily basis to report changes in the matching list. At the moment, we run search updates for about 2,200 registry searches (mostly EMDIS) and 180 donor center searches each night. These search updates are processed within 4.5 h.The performance improvement achieved by the new search kernel directly improves the day-to-day work of search coordinators, who are the main users of the Hap-E Search algorithm. The reduced waiting time until search results are available facilitates a smoother and more effective work flow.With the improvements of Hap-E Search 2.0, we now have a fast, reliable, and flexible matching algorithm that meets the challenges of a large and further growing donor database.The datasets generated for this study are available on request to the corresponding author.CU and JH: development of the search algorithm. CU: manuscript. AS: supervision, advise, and sponsorship.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.