We provide two examples, one on nanocrystalline pure copper and one on nanocrystalline HT-9 steel, where in-situ TEM-annealing experiments are compared to bulk material annealing experiments. The in-situ TEM and bulk annealing experiments demonstrated different results on pure copper but similar output in the HT-9 steel. The work entails discussion of the results based on literature theoretical concepts, and expound on the inevitability of comparing in-situ TEM annealing experimental results to bulk annealing when used for material thermal stability assessment.This work highlights some limitations of thermal stability analysis via  Microstructural stability assessment of materials exposed to thermal loads is an essential criterion in climbing the technology readiness level and qualifying materials for applications in industry. Possible microstructural instabilities  can alter the mechanical properties of materials ,2. For ein-situ transmission electron microscopy (TEM)/heating experiments has evolved, where thin specimen (preferably below 100 nm) are heated inside the TEM microscope while observing the changes in morphology. These experiments were used on different materials and the thermal stability of these materials in the pristine conditions and irradiated conditions  are concluded in some cases based on the in-situ TEM/heating observations [As a rapid characterization technique to assess thermal stability of NC and UF grained materials, performing rvations ,13,14,15in-situ TEM/heating and bulk heating (with ex-situ characterization of morphology) and demonstrate differences on how in-situ TEM/heating experiments can show a limited picture of NC materials behavior under thermal loads. The work on these selected materials of some distinct microstructures has allowed one to discuss several mechanisms suggested in literature regarding the stability of NC and UF grained materials in their bulk and thin film forms and clearly elucidate some limitations of in-situ TEM/heating experiments. The goal of this paper is not to reveal the grain growth kinetics in these NC materials, but rather to increase awareness of the limitations of in-situ TEM experiments, discussed from literature concepts, and to elucidate how data analysis of in-situ of this type of experiments should always be compared to ex-situ bulk heating results in some cases.In this work, we study two material systems  under In-situ TEM/heating and bulk heating (under vacuum furnace) experiments were performed on pure NC Cu and HT-9 steels. The main elements in the HT-9 steel, which is a ferritic/martensitic steel, were Cr ~12%. 0.5% W, 0.3% V, 1% Mo, 0.55% Ni, 0.25% Si, 0.55% Mn, 0.2% C and the balance was Fe. The NC materials were prepared via large strain machining (LSM) [in-situ TEM heating experiments were performed using a built-in furnace holder and a ramp rate of 23.3 degrees/min to 700 °C (or ramping time of 30 min). Bulk heating experiments were performed inside a vacuum chamber with a similar ramping rate. Investigation of morphologies before and after heating was performed using TEM and electron backscattered diffraction (EBSD). Morphology characterization was performed on thin (<100 nm thickness) and thick (10 s of µms thickness) regions of the TEM samples and on the bulk heated samples.ng (LSM)  at RT wiin-situ TEM experiments on the NC Cu sample as a function of time are shown in ex-situ heating at 400 °C and for a similar ramping and annealing time, the NC Cu sample showed large grain growth as demonstrated from the EBSD results in in-situ TEM annealed sample demonstrated grain growth in some parts of the thin region (while other grains showed no grain growth) (Bright-field micrographs from the  growth) b while t growth) d. The coex-situ heating  [in-situ TEM predicted the bulk material behavior. However, it is evident that NC materials that are stable in bulk forms are also stable during in-situ TEM experiments, but the opposite may not be true.In the case of the NC HT-9, the morphology of the sample consisted of elongated and equiaxed grains. The itu data , some grain size g,h and nressure)  which stressure) . The EDXressure) . In a NCressure) . These rressure) ,37 demonin-situ TEM/heating experiments, which have to be performed on thin areas that are usually below 100 nm thicknesses. Grain size stagnation and secondary grain growth which can occur in NC and UF materials have to be investigated and if shown to exist, then subsequent conclusions can only be drawn in regards to the thin film form of the investigated material.The two examples provided in this work and the corresponding comparison and illustration of established theories in literature provide sufficient evidence that vigilant analysis and comparison with bulk heating experiments should be performed to establish kinetics and conclusions regarding thermal stability of NC and UF materials via


The World Health Organization declared COVID-19 a global pandemic last year. While a clear impact of COVID-19 on the declining stroke volume has been reported, its overall impact on stroke presentation and clinical outcomes has not been established. The purpose of this study was to assess the impact of COVID-19 on acute ischemic stroke volume, presentation, treatment, and outcomes at comprehensive stroke centers.  A retrospective review of patients with a discharge diagnosis of acute ischemic stroke from the Get With The Guidelines database was performed from January 1, 2019, to July 1, 2020. The following time periods were defined: Pre-COVID (January/February), Peak-COVID (March/April), and Post-COVID (May/June). Bivariate analyses were performed comparing the 2020 and 2019 time periods to determine differences in stroke volume, presentation, treatment, and outcomes. P < 0.001). Patients were more likely to present after 24 hours from last known well during the 2020 Peak-COVID period (P = 0.03). However, there was not a significant difference in the rate of treatment with either the tissue plasminogen activator (tPA) or mechanical thrombectomy during the Peak-COVID period. Interestingly, relative treatment rates increased during the 2020 Post-COVID period to 11.4% (P = 0.01).  Stroke volumes were significantly lower during the Peak-COVID period in 2020 compared to that in 2019, with an absolute decline of 49.5% ( The overall ischemic stroke volume decreased during the pandemic, and patients had a tendency to present later, beyond eligible treatment windows. However, rates of treatment, patient demographics, and stroke outcomes did not significantly change when compared to the prior year. In early March 2020, the World Health Organization (WHO) declared the coronavirus disease of 2019 (COVID-19) a global pandemic. Since then, hospitals and surgical departments around the world were forced to restructure the way that healthcare is delivered. This was particularly true when it came to the delivery of emergency care for stroke and cerebrovascular diseases. Numerous organizations including the American Heart Association/American Stroke Association (AHA/ASA) disseminated guidelines on procedural safety optimization while simultaneously, reports suggested an increased risk of thromboembolic complications due to the disease and COVID-19-related stroke data being increased –11. Due Several publications in the literature have reported a decrease in the presentation of patients with acute ischemic stroke and a decrease in the number of stroke interventions. In particular, a large French registry demonstrated a decreased number of ischemic stroke patients along with a decreased number of mechanical thrombectomies, increased time from imaging to groin puncture, and slower speed of transfer . SimilarSignificant concerns have been raised that delayed emergency care may negatively affect the stroke severity at presentation with the worse National Institute of Health Stroke Scale (NIHSS) and extended symptom onset times that could disqualify patients from treatment with IV-tPA and/or EVT, thus leading to overall worse outcomes of stroke patients. While a clear impact of COVID-19 on declining stroke volume exists, its overall impact on the stroke presentation characteristics and, importantly, clinical outcomes has not been established. Furthermore, the period after the initial COVID-19 surge in the post-COVID-19 months has not been described. The purpose of this study was to assess the impact of the COVID-19 pandemic on acute ischemic stroke volume, presentation, treatment, and outcomes at comprehensive stroke centers during the 2020 Pre-, Peak-, and Post-COVID time periods, compared to the prior year (2019).A retrospective review of consecutive patients in the Get With the Guidelines (GWTG) stroke database from two comprehensive stroke centers in the New York City metropolitan area was performed from January 1, 2019, to July 1, 2020. Inclusion criteria were adult patients (≥18 years old) with the discharge diagnosis of acute ischemic stroke. Exclusion criteria were discharge diagnosis of hemorrhagic stroke or transient ischemic attack or patients with in-hospital stroke onset. Institutional review board approval was obtained with waiver of consent given at the retrospective data collection.Data was extracted from the GWTG stroke database and included stroke volume by month, patient age and sex, stroke severity (NIHSS), time from last known well (LKWA) to hospital arrival, mode of patient arrival , treatment type , and outcomes (discharge disposition and length of stay).st–February 29th. The Post-COVID period was defined as the decline after the first wave of the pandemic from May 1st to June 30th when the daily new COVID case volume started to drop [The first positive-tested COVID-19 case in the New York City region was documented on March 1, 2020, which defined the starting point of the Peak-COVID period. The Pre-COVID period in year 2020 was defined prior to the first positive-tested COVID-19 case as January 1 to drop . TherefoIn the bivariate analyses, all variables were analyzed as categorical variables. Age was grouped as 18–39, 40–59, 60–79, and 80+. NIHSS was assigned as a categorical variable as either 0–9, 10+, or unknown. LKWA was categorized into 5 groups (measured in hours): 0–4.5, 4.5–6, 6–24, >24, or unknown. Length of stay (LOS) was dichotomized as 0–7 or 8+ days, and discharge disposition was defined as home, expired, or other (rehabilitation or long-term care facility).t-test for continuous variables, chi-square tests for categorical variables, and Fischer's exact tests when cell numbers were less than 5.Bivariate analyses were performed comparing the 2020 time periods  with the 2019 time periods to account for monthly and seasonal variation. Additional analyses were performed in 2020 comparing the Pre-COVID to the Peak-COVID and Post-COVID time periods to account for possible yearly variation. Furthermore, mean LOS was analyzed and compared between 2020 and 2019 time periods as a focused comparison between the 2020 time periods. Data analyses were performed using Student's P value of <0.05 was considered statistically significant.Logistic regression models with stepwise selection were used to determine the patient factors associated with in-hospital mortality during the following time periods: (1) 2020 Peak-COVID time period and (2) control time period . Age , and sex variables were treated as dichotomized variables for the purposes of the regression analysis. A st–June 30th compared to 596 stroke patients during the same months in 2019. P < 0.001). There were 100 patients who presented with acute ischemic stroke during the 2020 Peak-COVID time period compared to 198 stroke patients in 2019, representing almost half (50.5%) of the stroke volume.Overall, there were a total of 440 stroke patients who presented in 2020 from January 1P = 0.042) and Peak-COVID (P = 0.03) time periods. In particular, a higher proportion of patients presented in the late time window > 24 hours after symptom onset, while a smaller percentage of patients presented during the eligible treatment windows for IV-tPA (0–4.5 hours) and EVT (0–24 hours). During the 2020 Post-COVID period, there were no significant differences in the LKWA, with similar proportions of patients presenting with LKWA 0–24 hours, compared to 2019.P = 0.09).The mode of patient arrival was similar during the 2020 Peak-COVID time period compared to the same time period in 2019. There was a trend towards fewer patients being transferred (22% in 2020 versus 34% in 2019), though it did not reach statistical significance. During the Post-COVID time period, there was a trend towards more patients arriving via EMS (35.5% in 2020 versus 22.7% in 2019) with relatively fewer patient transfers; however, this was not significant (P = 0.07). There was also a statistically significant increase in patients undergoing EVT during the 2020 Post-COVID time period (11.4%) compared to the same time period in 2019 (3.9%) (P = 0.01). Although the NIHSS and LKWA were similar in 2019 and 2020 Post-COVID time periods (May–June of respective years), relatively more patients were treated with EVT in 2020 (11.3%) than in 2019 (3.9%).Although the proportion of patients treated with IV-tPA did not significantly differ between Pre-COVID, Peak-COVID, and Post-COVID time periods in 2020 and 2019 , there wP = 0.05).Stroke outcomes were assessed by discharge disposition and LOS. There was no significant difference between the proportion of patients discharged home in 2020 Pre-COVID, Peak-COVID, and Post-COVID time periods compared to those in 2019. Overall, mortality was similar particularly during the Peak-COVID period (12% in 2020 versus 10% in 2019). LOS was similar in 2020 and 2019 Pre-COVID, peak-COVID, and Post-COVID time periods. The average LOS during the 2020 Peak-COVID time period was 7.4 days compared to 9.7 days in 2019, representing borderline statistical significance (P = 0.03). There was no significant difference in the mode of arrival as well as discharge disposition. Mean LOS was statistically longer during the Pre-COVID time period than during the Peak-COVID and Post-COVID time periods, with 48.7% of patients staying for longer than seven days in the Pre-COVID time period compared to 35% and 31.9% during the Peak-COVID and Post-COVID time periods, respectively (P = 0.004). Mean LOS in the Pre-COVID time period was 10.37 days compared to 7.20 days during the Peak-COVID and Post-COVID time periods averaged (P = 0.0002).Within 2020, comparing the Pre-COVID, Peak-COVID, and Post-COVID time periods revealed similar age, sex, and stroke severity as seen in We developed two separate stepwise logistic regression models for in-hospital mortality for the control (January–June 2019) and the 2020 Peak-COVID time periods. As shown in The impact of COVID-19 on declining stroke volume has been reported by multiple studies in the literature. However, some publications have emerged regarding the association of COVID-19 increasing the risk of thromboembolic disease, particularly acute ischemic stroke , 15. EarStudies have suggested that the decline in stroke volume during the COVID-19 pandemic and the complexity behind the management of these patients may lead to a temporal impact on the presentation and treatment in patients with AIS , 9. One Otherwise, stroke demographics including severity of stroke at presentation measured by NIHSS were relatively similar among the different time periods considered in the analyses. Interestingly, there was a trend towards a decreased number of transfers during the Peak- and Post-COVID periods compared to the Pre-COVID period in 2020 as well as in 2019. The potential decrease in transfer volume is likely due to the practice in the initial pandemic period to limit interfacility transfers to mitigate the risk of infection transmission. Length of stay and in-hospital mortality were used as surrogates for patient outcomes. In-hospital mortality and discharge location were not significantly different when comparing the 2020 Peak-COVID and control time periods. Furthermore, our stepwise regression analysis demonstrated that drivers of in-hospital mortality were similar in the 2020 Peak-COVID and control time periods. Length of stay trended toward being lower during the Peak-COVID and Post-COVID periods. This may have been due to a healthcare-wide initiative to discharge patients in a timely manner to make beds available for incoming patients due to overcapacity limits.As far as we are aware, this study is the first study to address the Post-COVID time period after the initial surge of COVID-19 in the New York metropolitan area. Interestingly, we did find that the rate of stroke patients treated with EVT increased during the Post-COVID time period when compared to the same months in 2019 and earlier in the 2020 Pre-COVID period. The rate of EVT treatment was significantly higher in the 2020 Post-COVID time period compared to either 2019 or 2020 earlier periods (11.4% compared to 2%–7% other time periods in 2020). This is remarkable in light of numerous reports describing the presence of “COVID strokes” where young individuals were presenting with atypical thromboembolic events , 19. ImpClearly, COVID-19 has demonstrated a multifactorial impact on the stroke chain of care and acute ischemic stroke workflows. It is likely that the numerous reports of COVID-19-associated stroke and increase in thromboembolic disease make up a small proportion of overall stroke patient volume . While tThis study has several limitations, including its retrospective nature and a limited sample size from a narrow geography of only two thrombectomy-capable stroke centers in the New York metropolitan area which limits the generalizability of these findings. The small sample size may have contributed to the differences in the logistic regression model from the 2020 Peak-COVID and control time periods. However, the findings do reflect results from high volume COVID-19 centers and, despite its small sample size, do have findings with important implications. Our study did not use patient-reported outcome information, such as the Modified Rankin Score. Nevertheless, we used discharge disposition as a surrogate of functional outcome given that several studies support discharge disposition as a good predictor of longer-term stroke outcomes , 24.Although stroke volume decreased in 2020 during the COVID-19 pandemic, stroke patients that presented for medical care had similar characteristics of age, sex, NIHSS, and mode of arrival as in the previous year. Although LKW to arrival time was significantly different in the Peak-COVID period with a higher proportion of patients presenting >24 hours, the stroke treatment delivery rates were similar to those of the prior year. However, the percentage of treated patients increased in the 2020 Post-COVID time period. The mean LOS was significantly shorter during the Peak- and Post-COVID periods, compared to the prior year. Importantly, our analyses revealed that the main drivers of in-hospital mortality remained fairly consistent over the time periods analyzed in our study. Numerous groups have advocated for increasing public awareness regarding the association of COVID-19 to delayed stroke treatment and clinical outcomes. Thus, public education is critical, especially during this pandemic, to enhance the timely diagnosis of acute ischemic stroke so that treatment outcomes are optimized.


Isolated rapid eye movement (REM) sleep behavior disorder (iRBD) is a prodromal stage of Lewy-type synucleinopathies (LTS), which can present either with an initial predominant parkinsonism (Parkinson’s disease (PD)) or dementia (dementia with Lewy bodies (DLB)). To provide insights into the underlying pathogenic mechanisms, the lipoprotein and protein glycosylation profile of 82 iRBD patients, collected before and/or after their conversion to an overt LTS, and 29 matched control serum samples were assessed by nuclear magnetic resonance (NMR) spectroscopy. Data were statistically analyzed to identify altered metabolites and construct predictive models. Univariant analysis detected no differences between iRBD patients with an LTS compared to controls. However, significant differences were found when the analysis distinguished between iRBD patients that manifested initially predominant parkinsonism (pre-PD) or dementia (pre-DLB). Significant differences were also found in the analysis of paired iRBD samples pre- and post-LTS diagnosis. Predictive models were built and distinguished between controls and pre-DLB patients, and between pre-DLB and pre-PD patients. This allowed a prediction of the possible future clinical outcome of iRBD patients. We provide evidence of altered lipoprotein and glycosylation profiles in subgroups of iRBD patients. Our results indicate that metabolic alterations and inflammation are involved in iRBD pathophysiology, and suggest biological differences underlying the progression of LTS in iRBD patients. Our data also indicate that profiling of serum samples by NMR may be a useful tool for identifying short-term high-risk iRBD patients for conversion to parkinsonism or dementia. The biological basis underlying this clinical heterogeneity remains poorly understood.Neurodegeneration associated with aggregated α-synuclein in the form of Lewy bodies and neurites  manifests clinically as a parkinsonism, usually associated with late onset dementia )4. Although iRBD patients have no overt neurological diseases, long-term prospective studies show that over 90% of them develop an LTS within 14 years after iRBD diagnosis, and the remaining disease-free patients show short-term risk markers for developing an LTS6. As such, iRBD is a clinical marker of the prodromal stage of LTS7, and provides an excellent opportunity to study LTS at early stages. This is important for management at clinical onset, development of early disease-modifying interventions, and selection and stratification in future neuroprotective trials.Isolated rapid eye movement (REM) sleep behavior disorder (iRBD) is a parasomnia characterized by vigorous dream-enacting behaviors and loss of REM sleep muscle atonia11 and inflammatory processes13. Nuclear magnetic resonance (NMR)-based tests are a robust method to characterize lipoproteins and protein glycosylation profiles in intact serum samples14, which have been used to study metabolic changes18 and systemic inflammatory processes20. However, this type of approach has not been explored before in the context of iRBD.iRBD and LTS have been associated with cardiovascular risk factorsHerein we evaluated the lipoprotein and protein glycosylation profiles of serum samples from iRBD patients by NMR and assessed whether the baseline profile identifies the future development of an LTS, and whether that manifests initially with parkinsonism or dementia.p value = 0.0373). There were also no differences in the age at iRBD nor LTS diagnosis, assessed by mean ranks comparison and corrected for multiple comparison. Moreover, there were no differences of time between iRBD and LTS diagnosis, sample collection and LTS diagnosis (pre-groups), and LTS diagnosis and sample collection (post-groups). There were only age differences at sample collection between controls and post-LTS , controls and post-DLB samples , pre-LTS and post-LTS samples , and pre-DLB and post-DLB samples . Comparably, there were differences in the time between iRBD diagnosis and sample collection between pre-LTS and post-LTS , and pre-DLB and post-DLB samples . These differences are inherent to the timeline of iRBD and LTS onset. Similar results were obtained when considering only the paired samples from each group , and 29 from controls (Table 21 (considered as controls)  compared to control subjects (431 ± 115 µmol/L), with a false discovery rate (FDR)-corrected p value of 0.006 , and post-LTS (which comprise post-DLB and post-PD) iRBD patients. No significant differences were found between pre-LTS or post-LTS iRBD patients compared with controls  higher levels of area glycB, and lower levels of medium LDL-P and LDL-TG were found in iRBD patients after the diagnosis of an LTS compared to the same patients before the diagnosis  towards lower levels of LDL-TG, LDL-Z, medium LDL-P, and large HDL-P, and higher levels of area glycB were found in iRBD patients after the diagnosis of DLB, compared to the same patients before the diagnosis  iRBD patients for which we had paired samples, from before and after their diagnosis of LTS, were compared. Significantly , a sensitivity of 53.33%, and a specificity of 96.55% at a cut-off of y > −0.233. Second, an equation was obtained distinguishing between pre-DLB and pre-PD patients that included small HDL-P and HDL-Z , a sensitivity of 72.22%, and a specificity of 86.67% at a cut-off of y > 0.201. Finally, we applied the model that distinguished between pre-DLB and pre-PD patients to 33 iRBD patients that remained disease-free at the end of the study . This allowed the prediction that six individuals would classify as pre-DLB patients, while the rest would classify as pre-PD patients  or dementia (pre-DLB) at onset.22, altered DAT-SPECT23, and pareidolias24, the latter being more associated to DLB. Also, few biochemical peripheral biomarkers have been suggested to identify iRBD from healthy controls, including impaired ghrelin excretion25, serum N-glycan composition26, and serum proteins, such as dopamine β-hydrolase12. Nevertheless, all these marker studies compared iRBD patients with controls, without considering that the patients might be a heterogeneous group including individuals close to phenoconversion to an LTS manifesting primarily with parkinsonism or dementia.Currently, existing short-term conversion biomarkers in iRBD to LTS include hyposmiaarea glycB could be considered as a biomarker for iRBD patients that develop a dementia onset LTS. This measurement corresponds to the amount of N-acetylneuraminic acid in glycoproteins. N-acetylneuraminic acid, the most abundant form of sialic acid in human cells, has a negative charge and hydrophilicity, which enable its structural and modulatory roles, such as blood cell charge repulsion or neural plasticity27. Sialylation changes have been associated with infectious diseases, stress, inflammation, autoimmune diseases, cancer, and neurodegeneration28. As such, changes in area glycB have been described as an inflammatory marker associated with insulin resistance and adiposity29, obesity20, and rheumatoid arthritis19 (reviewed in ref. 30). In our data set, although there were no differences in the prevalence of cardiovascular risk factors , area glycB was decreased in pre-DLB patients compared to controls, but then there was a trend to increased area glycB in post-DLB compared to pre-DLB condition. This parameter is not modified in any of the analysis considering our PD cohort, implying possible biochemical differences in the patients manifesting these two different phenotypes. A few recent studies reported differences in peripheral cytokines between iRBD patients and healthy controls33, in particular, increased serum/plasma TNF-alpha levels in iRBD compared to controls. Furthermore, their data showed higher levels at baseline in those patients who later converted to LTS compared to those iRBD non-converters. Although there are no studies specifically on the inflammatory profile of iRBD patients before developing dementia onset LTS, peripheral inflammation has been seen in early stages of manifest DLB35. This phenomenon could be associated with the observed trend to increased inflammatory markers during the neurodegeneration that spans from iRBD to DLB conversion, although the post-DLB levels are like those in control individuals. Further studies are needed to elucidate the role of peripheral immune changes in the pathogenesis of neurodegeneration in the prodromal stage of LTS.Here, using two different analysis methods, we observed that the parameter area glycB, and lower levels of medium LDL-P and LDL-TG after the diagnosis of an LTS. Higher levels of area glycB would point towards a more inflammatory profile when patients have an overt neurological condition, as also suggested by our previously discussed results. On the other hand, lower levels of medium LDL-P and LDL-TG would point towards a lower cardiovascular risk37, which is in disagreement with previous findings associating iRBD with higher cardiovascular risk factors8. It should be noted that these three parameters showed the same trend when analyzing the DLB, but not the PD group. Since DLB patients (n = 12) outnumbered the PD patients (n = 7) in the paired samples, it is possible that these changes are not common for both LTS manifestations, but rather specific for DLB. Studies with a larger cohort are needed to address these ambiguities and draw functional conclusions about the results.The analysis of samples from the same iRBD patients before and after their LTS diagnosis revealed higher levels of We built a model to distinguish between iRBD patients that will eventually evolve into an LTS with an initial parkinsonism or dementia. The identification of a predictive biomarker for the conversion of iRBD patients to parkinsonism or cognitive impairment is important for the clinical management and prognosis of both conditions. When applying our model to iRBD patients without an overt neurological condition (33 iRBD-only samples), we classified six individuals as pre-DLB patients while the rest would classify as pre-PD patients. Remarkably, five of the putative pre-PD patients, were classified as such with a sensitivity of 100%. Hence, we will examine the potential future clinical utility of our model by following closely the clinical evolution of these five individuals in the ensuing years at our institution.HDL-Z) and the number of small HDL particles . The parameter HDL-Z, which measures HDL particle profile and its heterogeneity, is inversely associated with cardiovascular risk38. Nevertheless, these differences are not statistically significant in a univariate analysis and there are no studies to confirm or deny the differences in cardiovascular risk between iRBD patients that will initially manifest parkinsonism or dementia, which should be further tested in future validation studies in independent iRBD cohorts.We have found that the putative prediction to distinguish iRBD patients that will manifest parkinsonism or dementia was mainly based on the changes in two inversely related parameters: the mean size of HDL particles  showed different gender distributions. Still, the models here described correspond to comparisons without any statistically significant gender bias.This study has some limitations. First, the metabolic profiling was done in serum samples from non-fasting individuals, which adds variability and represents a confounding factor that could modify the results. Still, we have used stringent statistical cut-off values to avoid false-positive and -negative results. Also, one would expect that the putative alterations would be present in all groups under study since all samples were taken in non-fasting conditions, as well as the fact that variability between individuals is greater than the one expected by fastingStrengths of our study include (1) the analyses of 101 samples from iRBD patients, which can be considered a large number in the iRBD field, (2) that the iRBD diagnosis was confirmed by video-polysomnography in all cases, (3) that some samples were obtained from the same patients longitudinally before and after conversion to LTS, and (4) that machine learning techniques were used to build predictive models rather than using simple statistical comparisons between groups.In conclusion, this study provides evidence of altered lipoprotein and protein glycosylation profiles in iRBD patients. Our results support that metabolic alterations and inflammation are involved in iRBD pathophysiology, and suggest biological differences underlying the progression of LTS in iRBD patients. We found that the baseline profiling of plasmatic lipoproteins and protein glycosylation in serum samples could potentially distinguish between iRBD patients that will eventually evolve to an LTS with either an initial development of parkinsonism or dementia. This information in combination with other clinical  and neuroimaging  markers could allow to better identify subjects with a high risk for short-term conversion to an overt LTS. Although this study requires an independent validation with larger and longitudinal cohorts to confirm the putative predictive value of the models here described, it provides an approach to a more accurate classification of iRBD patients in subtypes or variants with similar prognosis and underlying biology. If confirmed, this approach will help the design of future intervention studies to target more homogeneous subsets of LTS at early stages.39. Patients and controls were Caucasians of Spanish origin. In all patients, iRBD was diagnosed by increased electromyography activity in the four limbs and in the chin associated with abnormal behaviors during REM sleep . After iRBD diagnosis, patients were periodically followed at least every 6–12 months by a neurologist. Blood sample donation occurred during these routine visits in non-fasting conditions. If PD or DLB were suspected in the routine visits, iRBD patients were then examined by movement disorders or dementia expert neurologists to confirm the diagnosis. Diagnosis criteria were those accepted for PD1 and DLB2. When iRBD patients converted to PD or DLB, they were asked to donate a blood sample, either again if already participating in the study (paired samples) or for the first time if newly recruited.Polysomnographic-confirmed iRBD patients were recruited prospectively between 1996 and 2015 at the center for sleep disorders of the Neurology Service from the Hospital Clínic of Barcelona, SpainSamples from a total of 82 iRBD patients were obtained. From these, the following age- and sex-matched groups were distinguished: (i) 33 individuals that remained disease-free at the end of the study (July 2019) (iRBD-only), (ii) 33 iRBD individuals that had no overt neurological disease at sample collection but later on converted to LTS (pre-LTS), among which there were (ii-a) 15 iRBD individuals that later on converted to DLB (pre-DLB), and (ii-b) 18 iRBD individuals that later on converted to PD (pre-PD), and (iii) 35 iRBD individuals that at sample collection had already converted to an LTS (post-LTS), among which there were (iii-a) 20 individuals that had already converted to DLB (post-DLB), and (iii-b) 15 individuals that had already converted to PD (post-PD). From the 33 iRBD patients who had no overt neurological disease at sample collection (ii), 19 converted later  and a second sample was then collected and included within the 35 samples from the iRBD group with a diagnosed LTS (iii). Thus, from these 19 patients paired samples were available before and after the LTS diagnosis. We also included 29 samples from sex- and age-matched healthy controls without evidence of neurological or sleep disorders, which were selected from a database of healthy controls made of non-consanguineous attendants and volunteers who donate samples for research studies at our institution. All samples were processed and stored by the same person to avoid differences that could affect the results.Demographic data are presented in Table The ethical committee at the Hospital Clinic de Barcelona, Spain, approved the study and all participants gave written informed consent. Samples were registered at the biobank of IDIBAPS (S080327-01NL). Usage of the blood samples for research into disease biomarkers was approved by the Hospital Clínic Research Ethics Committee (HCB/2014/1065) and the Vall d’Hebron Hospital Research Ethics Committee (PR(AG)370/2014).g for 10 min at 4 °C. Serum volumes of 2 ml were removed from supernatant, aliquoted in polypropylene CryoTubes , flash frozen, and stored at −80 °C.Five to 10 ml of blood were collected in tubes without anticoagulant , preserved for 30 min at room temperature, and centrifuged at 1500 × 1H NMR spectroscopy to estimate the size (-Z) of the three main types of lipoproteins (very low-density lipoprotein (VLDL), low-density lipoprotein (LDL), and high-density lipoprotein (HDL)), and the concentration (-P) of particles of three subtypes  of the main types of lipoproteins, as well as the lipid content [cholesterol (-C) and triglycerides (-TG)] of the three main classes, together with intermediate-density lipoproteins (IDL)40. The glycoscale uses the same technique to determine the presence of glycosylated proteins in serum, which is an indicator of systemic inflammatory processes20. More specifically, it determines N-acetlyglucosamine and N-acetylgalactosamine bound to protein (glycA), N-acetylneuraminic acid bound to protein (glycB), and any of the three acetyl groups not bound to protein (glycF). From these three peaks, it distinguishes their area (associated with concentration) and the ratio hight/width (describing the peak shape).The lipoprotein profile and the presence of glycosylated proteins were measured in the 130 serum samples at the same time using the Liposcale® and the Glycoscale tests , respectively. The Liposcale® test is a CE marked and previously reported method based on 2D diffusion-ordered p value < 0.05). Results are expressed as median ± interquartile range.Differences between groups were assessed for those informative comparisons in terms of disease pathophysiology and with a diagnostic or prognostic value. These include control vs. pre-LTS, pre-DLB, pre-PD, post-LTS, post-DLB and post-PD, pre-LTS vs. post-LTS, pre-DLB vs. pre-PD, post-DLB vs. post-PD, pre-DLB vs. post-DLB, and pre-PD vs. post-PD. Since individuals in the iRBD-only group are a heterogeneous mixture of possible pre-LTS and individuals who might not convert during their lifetime, we excluded comparisons between them and any other group for being non-informative. The differences between comparisons were determined by a Wilcoxon signed-rank test combined with the Benjamini–Hochberg procedure to correct for false discovery rate (FDR) for any given comparison .Paired samples  were (re)analyzed independently using the paired and non-parametric test Wilcoxon matched-pairs signed-rank test. Correction for multiple comparisons was done by controlling the FDR with the Benjamini–Hochberg procedure . Next, elastic net (R function cv.glmnet) was used on all variables from the chosen comparisons for variable selection and model building41. The elastic net mixing parameter was tested from 0.1 to 0.9, at intervals of 0.05, and a 10-fold cross-validation was performed. The model with the lowest mean cross-validated error was kept, and a receiver operating characteristics curve was made. Internal validation of the model was achieved by bootstrapping42, using the R function vboot.glm. Internal bootstrapping validation logistic model was done with 1000 bootstrap samples, and 10-fold cross-validation with 100 replicates. The AUC from each model was corrected by subtracting the optimism value obtained by bootstrapping, and only those models with a corrected AUC > 0.75 were kept.Variables that allowed distinguishing between all groups were determined by non-parametric Kruskal–Wallis test correcting for multiple comparisons by controlling the FDR with the Benjamini–Hochberg procedure. Those variables significant with an FDR-corrected 43 and GraphPad Prism version 8.0.1 for Windows .Data analyses were performed in RFurther information on research design is available in the Supplementary Table 1REPORTING SUMMARY


Cell-free DNA (cfDNA) serves as a footprint of the nucleosome occupancy status of transcription start sites (TSSs), and has been subject to wide development for use in noninvasive health monitoring and disease detection. However, the requirement for high sequencing depth limits its clinical use. Here, we introduce a deep-learning pipeline designed for TSS coverage profiles generated from shallow cfDNA sequencing called the Autoencoder of cfDNA TSS (AECT) coverage profile. AECT outperformed existing single-cell sequencing imputation algorithms in terms of improvements to TSS coverage accuracy and the capture of latent biological features that distinguish sex or tumor status. We built classifiers for the detection of breast and rectal cancer using AECT-imputed shallow sequencing data, and their performance was close to that achieved by high-depth sequencing, suggesting that AECT could provide a broadly applicable noninvasive screening approach with high accuracy and at a moderate cost. Plasma cell-free DNA (cfDNA) is an intensively investigated biomarker that has been widely used for noninvasive cancer evaluation and prenatal testing –4. BecauComputational approaches have been designed to improve the measurement of genomic or transcriptomic spectra generated from low-coverage sequencing data, particularly from single-cell RNA-seq data and single-cell ATAC-seq data –15. ThesAlthough existing methods may fail to account properly for TSS coverage data, they highlight the potential to capture accurate TSS coverage profiles and extract data structures from shallow sequencing data. One of the most popular such methods, using autoencoder frameworks, may apply to TSS coverage data due to its flexibility and scalability , 14. An Here, we introduce the Autoencoder of cfDNA TSS (AECT) coverage profile, a method of denoising TSS coverage profiles generated by shallow cfDNA sequencing. A set of pre-processing steps for cfDNA sequencing data, including GC bias adjustment and copy number normalization, are also integrated. The effectiveness of AECT was validated using multiple datasets. Outperforming other tools designed for single-cell sequencing data, AECT generated comparable accuracy of TSS coverage profiles as high-depth cfDNA sequencing data, and it was sufficiently powerful to uncover the latent biological features in shallow cfDNA sequencing generated from healthy donors and tumor patients. In sum, AECT greatly improves the performance of shallow sequencing-based cancer detection and sheds a light on the clinical use of cfDNA sequencing at an acceptable cost.AECT is a deep neural network autoencoder, implemented with the Keras framework and TensorFlow in the backend. It uses TSS profiles as its input layer and predicts imputed profiles as the output layer. By default, five fully connected hidden layers with 128, 64, 32, 64, and 128 neurons are used to compress and reconstruct the data using the MSE loss function . The recA few related studies review to our study. A set of deep learning-based denoising model for single cell sequencing data, including DCA , DeepImpThe samples used in the study have been described in previous studies , 17. TheSingle-end sequencing data were generated from the Ion Proton platform . After low-quality sequencing reads were removed, high-quality reads were aligned to the hg19 human reference genome using TMAP (v5.4), and PCR duplication was removed. Because AECT uses the read frequencies of each TSS region, AECT users could also use hg38 as reference genome. GC-bias correction and copy number change normalization were performed with the deepTools correctGCBias algorithm , 19, andp-values were calculated using the Wilcoxon rank-sum test and then were adjusted to the FDR, using the Holm procedure. TSSs with FDR < 0.1 were selected as altered TSSs for downstream functional analyses, and gene-set annotation and functional enrichment analysis (on GO database) were performed using Metascape with default parameters , and lambda values were determined by 10-fold cross-validation. For each model, 1,000-times bootstrapping was used to test the robustness of the candidate genes chosen by the model. To reduce overfitting, we constructed the final prediction model using 100 candidate genes that were most often seen in the bootstrapping. The performance of the classifiers was evaluated on the training cohort and validation cohorts using receiver operating characteristics generated by the pROC R package .For ease of comparison, we used the latest version of MAGIC (v1.5.5) , DCA v0, DeepImpGC content bias influences the number of reads that are mapped to a genomic region, confounds the quantification of TSS coverage profiles, and is a major cause of batch effects in cfDNA sequencing data . To addrWe developed AECT to impute precise TSS coverage . The AECAs a proof-of-principle and to explore the properties of our approach, we applied AECT to mimic low cfDNA-sequencing data. Five high-depth sequenced samples (>400 M reads) , includiWe also used four representative algorithms designed for single-cell sequencing imputation, namely, MAGIC , DeepImpTo test whether AECT captured common features in the real data, we sequenced the cfDNA of 159 healthy donors with an average of 8.6 M reads (range 7.2 to 10.6 M). PCA was performed on GC-adjusted TSS profiles, and samples were into two groups by donor sex to reflect the differences in TSS profiles between males and females . Howeverp < 2.2 × 10−16, Wilcoxon rank-sum paired test), which implies a reconstruction of gene–gene relationships (r| > 0.8), suggesting an over-adjustment in the TSS profiles  and increased the detection numbers of breast cancer-associated TSSs with AUROC > 0.7 . Another 45 healthy women donors were also included in the analyses . To reduOC > 0.7 , while mBRCA1, which did not change significantly between breast cancer and non-cancer samples in the raw data , showed significantly lower TSS coverage in breast cancer patients after imputation , which is consistent with the high expression level of BRCA1 in breast cancer tissues  showed lower TSS coverage in Her-2(+) subtype and luminal B subtype with Her-2 expression  and identified more altered TSSs  and NODE .Publicly available datasets were analyzed in this study. This data can be found here: SRA , Medical Scientific Research Foundation of Guangdong Province of China (B2017006), and China Postdoctoral Science Foundation funded project (2019M662998).KL was employed by the company Guangzhou XGene Co., Ltd., and G-JO, R-TW, and SX were employed by the company Guangzhou Darui Biotechnology Co., Ltd. The remaining 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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Background: New-onset atrial fibrillation (AF) after ablation of typical atrial flutter (AFL) is not rare. This study aimed to investigate the predictive value of electrocardiographic parameters on new-onset AF post-typical AFL ablation.Methods: A total of 158 consecutive patients  with typical AFL were enrolled between January 2012 and August 2017 in this single-center study. Patients with a history of AF before ablation were excluded. ECGs during sinus rhythm (SR) and AFL were collected. The duration of the negative component of flutter wave in lead II (DFNII), proportion of the DFNII of the total circle length of AFL (DFNII%), amplitude of the negative component of flutter wave in lead II (AFNII), duration (DPNV1), and amplitude (APNV1) of negative component of the P wave in lead V1, and P wave duration in lead II (DPII) during sinus rhythm were measured.Results: During a median follow-up of 26.9 ± 11.8 months, 22 cases (13.9%) developed new-onset AF. DFNII was significantly longer in patients with new-onset AF compared to patients without AF . AFNII was significantly lower , DPII , and DPNV1 was significantly longer  in patients with new-onset AF. In the multivariate analysis, DFNII  and DPII  were found to be independently associated with new-onset AF after typical AFL ablation.Conclusion: Parameters representing left atrial activation time under both the SR and AFL were independently associated with new-onset AF post-typical AFL ablation and may be useful in risk prediction, which needs to be confirmed by further prospective studies. New-onset AF after ablation of typical AFL is not rare.Some ECG parameters such as P wave duration and amplitude and fragmented QRS complex under SR are predicators of AF after AFL ablation.Electrocardiogram parameters during both the AFL and SR were useful in predicting the risk of new-onset AF in typical patients with AFL post-ablation.FNII) and P wave duration in lead II during SR (DPII) were independent risk predictors of the occurrence of AF.Duration-related parameters such as duration of the negative component of flutter wave in lead II  is a frequently encountered clinical tachyarrhythmia, especially occurring in elderly people. Given the high success rate and low risk, radiofrequency catheter ablation (RFCA) is currently the first-line treatment of AFL. However, previous studies showed that even in patients without previously documented atrial fibrillation (AF), the risk of new-onset AF after AFL ablation was relatively high –4, rende2DS2-VASc) score  , obesityc) score . Other cc) score , ECG parc) score , 13, lefc) score , and lefc) score  were alsc) score –18, the c) score . Therefoc) score .Thus, ECG parameters, which could be easily obtained, were widely used for predicting new-onset AF after AFL ablation. Currently, P wave duration  and amplFrom January 2012 to August 2017, patients with typical counterclockwise-AFL (CCC-AFL) referring for ablation in the Nanjing Medical University Hospital were consecutively enrolled. The ECG definition of typical AFL (CCW-AFL) was previously reported . ExclusiFNII); (2) the proportion of DFNII of total CCW-AFL circle length (DFNII %); (3) the amplitude of the negative component of flutter wave in lead II (AFNII); (4) the total amplitude of the flutter wave in lead II (AFII); (5) the amplitude of negative component of P wave in lead V1 during SR (APNV1); (6) the duration of negative component of P wave in lead V1 during SR (DPNV1); and (7) duration of P wave in lead II during SR (DPII). P wave terminal force of V1 (PtfV1) was also calculated. Adobe Acrobat XI Pro  was used to do the measurement by two independent experienced cardiologists who were not involved in this study. The value provided by each cardiologist was an average value of three different beats. The final value was the average of the results by each cardiologist. The values would be remeasured to reach a consensus by both the cardiologists under at least one the following circumstances to control the interobserver variation: (1) The amplitude difference under SR was over 0.05 mV; (2) The amplitude difference under AFL was over 0.1 mV; (3) The duration difference under sinus rhythm was over 0.04 s; and (4) The duration difference under AFL was over 0.08 s.Standard 12-lead ECGs during CCW-AFL and SR were obtained  before procedure. Antiarrhythmic drugs (AADs) were discontinued for at least 5 half-lives before ECG collection. If ECG during SR or AFL could not be obtained after enrollment, the patients were asked to provide the most recent ECG free from any AADs influence (administration of AADs within 1 month before the ECG acquisition). An obvious deflection point between each flutter wave was treated as the baseline according to a previous study . The ECGAntiarrhythmic drugs were discontinued for at least 5 half-lives before procedure, except the ß-blockers for rate control. Electrophysiological study was performed under local anesthesia. Spontaneous or induced CCW-AFL was recorded. Activation mapping was performed by using a three-dimensional mapping system [Carto system  or EnSite NavX system ] and cavotricuspid isthmus-dependent CCW-AFL was confirmed with entrainment mapping. Under the guidance of the three-dimensional mapping system, an irrigated catheter was used to perform linear ablation between the tricuspid annulus and the inferior vena cava. Bidirectional block across the line was adopted as ablation endpoint.No AADs were prescribed after the procedure. Patients were regularly followed every 3 months with ECG and 24-h Holter recordings within the first year. ECGs were obtained when patients experienced any palpitation within the first year and thereafter. CCW-AFL recurrence was defined as AFL longer than 30 s during follow-up after 3 months of blanking period. New-onset AF was defined as any episode of AF lasting over 30 s during follow-up period.t-test or the chi-squared test. Two-sided p < 0.05 was considered as statistical significant. To identify factors associated with new-onset AF post-AFL ablation, the multivariate logistic regression was performed (forward LR method). The DeLong's test was used to compare C-statistic between models and the net reclassification improvement and integrated discrimination improvement were done to compare reclassification.Data were expressed as mean ± SD for continuous variables and as absolute frequency and percentage for categorical variables. All the statistical analysis was done by using the Statistical Package for the Social Sciences (SPSS) version 20.0  and the R software version 4.0.3 . The differences between groups were compared by using the Student's 2DS2-VASc score, PR interval, and the HATCH score  in this study. Ablation endpoint was achieved in all the patients. During a mean follow-up period of 26.9 ± 11.8 months, six patients (3.8%) had AFL recurrence and need a second ablation. Among all the patients, 22 patients (13.9%) developed new-onset AF. Mean duration of AF seen on Holter was 213 ± 48 s, 19 patients had symptomatic AF and 17 patients needed AADs or ablation for AF. All the patients were divided into two groups according to new-onset AF. The baseline characteristics were shown in CH score .FNII , DPII , and DPNV1  were significantly prolonged in patients with new-onset AF. AFNII was significantly reduced  in patients with new-onset AF. No significant difference was found between two groups regarding APNV1 . DFNII, AFNII, DPNV1, DPII, the CHA2DS2-VASc score, and LAD were adjusted in the multivariate regression analysis and DFNII and DPII were independent predictors of new-onset AF after CCW-AFL ablation  small sample size; (2) the amplitude of the negative component in lead V1 was sometimes too small, leading to deviated measurement; and (3) when projecting on the surface ECG, activation during different rhythm has different blinding areas. Thus, the predictors during SR can be supplemented by AFL predictors.This study presents evidence to show that ECG parameters both during the SR and CCW-AFL have predictive value in new-onset AF after CCW-AFL ablation. However, no significant difference was found between AF and non-AF groups with respect to left atrium diameter. The possible explanation might be that patients enrolled in this study were without documented AF, indicating a relatively early stage of left atrial disease with “structurally normal” left atrium. However, even an early stage of left atrial disease could result in abnormal left atrial electrical activation, which could be reflected on surface ECG. From this point of view, ECG indicators might have advantages in predicting occurrence of AF in the early stage of left atrial disease. Interestingly, we found that independent predictive values were all time duration-related indicators. However, parameters related to the amplitude had no independent predictive value according to the results of this study. We speculate that the structural remodeling of atrial disease includes hypertrophy and dilation. Hypertrophy usually manifests as an increased amplitude, whereas dilation usually manifests as a decreased amplitude and prolonged activation. Therefore, different types and stages of atrial disease might result in different changes in amplitude, which may weaken the predictive value of the amplitude. However, electrical activation of left atrium would be prolonged regardless of the types and stages of left atrial disease, resulting in a more important role of duration-dependent parameters in AF prediction. In addition, among those amplitude-dependent predictors, ACombined ECG indicators during both the CCW-AFL and SR are the direct and easily accessible parameters for predicting newly developed AF post-AFL ablation. With this, those patients who are at high risk of AF occurrence could receive both the AFL ablation and PVI in the same procedure to avoid a second AF ablation or need to have a close follow-up to receive early AF management. Developing a scoring system to stratify the patients with CCW-AFL and to further verify its predictive value is very necessary.First, this was a single-center study with a relatively small sample size. The predictive value of the ECG predictors needs to have further large-scale prospective studies. A predictive scoring system can be established and the positive predictive value can be verified. Second, despite regular follow-up, asymptomatic paroxysmal episodes of AF were difficult to exclude, leading to underestimation of the incidence of new-onset AF. As long-term event monitors such as a 30-day event monitors/Holter were unavailable, subclinical preexisting AF was difficult to exclude. Third, the incorporation of other clinical characteristics such as BNP and fibrosis assessment could make the scoring system more accurate. Fourth, as some ECGs collected a period before enrolment was measured, it was difficult to exclude the potential influence of AADs on ECG parameters under recall bias. Last but not least, MRI imaging, atrial refractory periods, and left atrial electroanatomical mapping were not performed to better verify the underlying mechanisms.Parameters representing left atrial activation time under both the SR and AFL were independently associated with new-onset AF post-typical AFL ablation and may be useful in risk prediction, which needs to be confirmed by further prospective studies.The original contributions presented in the study are included in the article/The studies involving human participants were reviewed and approved by Ethics Committee of the First Affiliated Hospital of Nanjing Medical University. The patients/participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.This work was funded by Beijing Xinlian Zhicheng Cardiovascular Health Public Welfare Foundation (grant number 303103505CA20).MC reports receiving lecture fees from Biosense Webster, Abbott, Medtronic, Boston Scientific, Bayer, and Boehringer Ingelheim. The remaining 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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


In New York City (NYC), pneumonia is a leading cause of death and most pneumonia deaths occur in hospitals. Whether the pneumonia death rate in NYC reflects reporting artifact or is associated with factors during pneumonia-associated hospitalization (PAH) is unknown. We aimed to identify hospital-level factors associated with higher than expected in-hospital pneumonia death rates among adults in NYC.Data from January 1, 2010–December 31, 2014 were obtained from the New York Statewide Planning and Research Cooperative System and the American Hospital Association Database. In-hospital pneumonia standardized mortality ratio (SMR) was calculated for each hospital as observed PAH death rate divided by expected PAH death rate. To determine hospital-level factors associated with higher in-hospital pneumonia SMR, we fit a hospital-level multivariable negative binomial regression model.Of 148,172 PAH among adult NYC residents in 39 hospitals during 2010–2014, 20,820 (14.06%) resulted in in-hospital death. In-hospital pneumonia SMRs varied across NYC hospitals (0.77–1.23) after controlling for patient-level factors. An increase in average daily occupancy and membership in the Council of Teaching Hospitals were associated with increased in-hospital pneumonia SMR.Differences in in-hospital pneumonia SMRs between hospitals might reflect differences in disease severity, quality of care, or coding practices. More research is needed to understand the association between average daily occupancy and in-hospital pneumonia SMR. Additional pneumonia-specific training at teaching hospitals can be considered to address higher in-hospital pneumonia SMR in teaching hospitals. Pneumonia and influenza, often grouped together in vital statistics reports, ranked as the third leading cause of death in NYC in 2017, but the eighth leading cause of death in the United States . FurtherA necessary step in any study of quality of care and in-hospital death is controlling for immutable patient-level risk factors that make up a unique hospital case mix, such as age, severity of illness, and pre-existing conditions , 11. FaiIdentifying hospital-level risk factors for in-hospital death during PAH can guide public health interventions to address NYC’s reported disproportionate pneumonia burden. The objective of this study was therefore to identify hospital-level factors significantly associated with higher than expected pneumonia death rates in NYC hospitals among adults.Data sources included New York’s Statewide Planning and Research Cooperative System (SPARCS) and the American Hospital Association (AHA) Annual Survey Database , 16.SPARCS is an all-payer reporting system that includes patient-level data on all inpatient and outpatient hospital visits in New York State . SPARCS The AHA Annual Survey Database is compiled from the AHA Annual Survey, which collects data on >6,300 hospitals across the United States . The AHAWe identified the discharge records of all NYC residents aged ≥18 years with ≥1 PAH at a NYC hospital who were discharged during January 1, 2010–December 31, 2014. We excluded discharge records with a disposition status indicating that the patient was transferred to a different hospital for inpatient care, as we could not assess outcome for these patients across the continuum of care. If a patient had >1 PAH during the study period, one hospitalization record was randomly selected for inclusion in the analysis .A PAH was defined as a hospitalization with a principal diagnosis of pneumonia  or a secondary diagnosis of pneumonia if the principal diagnosis was respiratory failure  or sepsis  [Average daily census was defined as the average number of patients of all ages served on an inpatient basis on a single day during the year; the figure was calculated by dividing the number of inpatient days  by the number of days in the year . The totTeaching hospitals were defined as hospitals affiliated with a medical school that provide medical education. Major teaching hospitals were defined as members of the Council of Teaching Hospitals (COTH) . COTH isWe used R   for all data analyses and table and figure development .We employed a model to determine hospital-level factors associated with in-hospital pneumonia SMR. Before building the model, we calculated the pneumonia SMR for each hospital over the five-year study period. Observed PAH death rates at each hospital were calculated by dividing the actual number of PAH deaths at the hospital during the five-year study period by the number of PAH patients at the hospital during the five-year study period. Expected death rates were calculated by predicting death outcomes for each patient with a PAH in the SPARCS dataset using the patient-level prediction model and then aggregating by hospital. To determine the in-hospital pneumonia SMR by hospital, the observed PAH death rate was divided by the expected PAH death rate. An SMR >1.0 indicates that there were more observed deaths than expected deaths, an SMR <1.0 indicates that there were fewer observed deaths than expected deaths, and an SMR = 1.0 indicates that there were equal numbers of observed deaths and expected deaths.Our SPARCS data containing PAH was reported from 58 hospitals in NYC. After removing 9 nursing homes and end-of-life care hospitals, 49 hospitals remained. We then matched the AHA Annual Survey Database and SPARCS to join hospital-level predictors to our outcome of interest for the hospital-level model, in-hospital pneumonia SMR. Because hospitals in NYC frequently change names and affiliations, we used the Near tool in ArcGIS Pro  to facilitate a match between hospitals in the AHA dataset and hospitals in the SPARCS dataset . First, To identify hospital-level predictors that were independently associated with in-hospital pneumonia SMR, a bivariate analysis was conducted for each candidate predictor of in-hospital pneumonia SMR. Our candidate predictors were chosen based on a literature review and subject matter expertise. For ease of interpretation, our 60 candidate predictors were grouped into categories: executive contracts, beds and utilization, hospitals and services, insurance, Medicare and Medicaid utilization, staffing, and other. Definitions for each of the 60 predictors can be found in the AHA DataViewer Glossary .Candidate predictors with a p-value <0.05 in the bivariate analysis were retained as candidate predictors for the multivariable analysis. We used scatterplots to test whether the relationships between the candidate predictors and the outcome were linear. We used Q–Q plots to test whether the distributions of the variables were normal. To examine multicollinearity between candidate predictors in our multivariable model, we used Chi-square tests, ANOVA tests, and simple linear regression. Several multivariable negative binomial regression models were developed using combinations of variables that were statistically significant (p < 0.05) in the bivariate analysis. We compared these models based on Akaike Information Criterion (AIC) values and chose the model with the lowest AIC value.DOHMH determined that this investigation was not human subjects research and CDC determined that this investigation was public health non-research.Of 148,172 PAH among adult NYC residents in 39 hospitals during 2010–2014, 20,820 (14.06%) resulted in in-hospital death . Of all In-hospital pneumonia SMR ranged from 0.77–1.23 across 39 hospitals. Of the 39 hospitals, 20 had an SMR >1.0 and 19 had an SMR <1.0. By hospital characteristic, in-hospital pneumonia SMRs ranged from 0.83–1.22 Click here for additional data file.


Plantar heel pain (PHP) is common and impacts negatively on physical function and quality of life. Initial treatment usually comprises analgesia and self-management advice (SMA), with referral to a physiotherapist or podiatrist recommended only when symptoms persist. Systematic reviews highlight limitations of existing evidence for the effectiveness of exercises and orthoses. The objective of the TREADON pilot and feasibility trial was to inform the design of a future main trial to compare the clinical and cost-effectiveness of self-management advice (SMA), individualised exercises and foot orthoses for PHP.This was a four-arm randomised feasibility and pilot trial with 12-week follow-up. Adults aged ≥ 18 years with PHP were identified from primary care by general practice consultation, retrospective general practice medical record review or a population survey. Participants were randomised to either (i) SMA, (ii) SMA plus individualised exercises (SMA-exercises), (iii) SMA plus prefabricated foot orthoses (SMA-orthoses) or (iv) SMA plus combined individualised exercises and prefabricated foot orthoses (SMA-combined). Feasibility outcomes were recruitment; retention; intervention adherence, credibility and satisfaction; performance of three potential primary outcome measures , Foot Function Index-pain subscale (FFI-pain), Manchester Foot Pain and Disability Index-pain subscale (MFPDI-pain)); and parameters for informing the main trial sample size calculation.Eighty-two participants were recruited. All three identification methods met the target number of participants. Retention at 12 weeks was 67%. All interventions were successfully delivered as per protocol. Adherence (range over 12 weeks 64–100%) and credibility (93%) were highest in the SMA-combined arm. Satisfaction with treatment was higher for the three clinician-supported interventions . Responsiveness (baseline to 12 weeks) was higher for FFI-pain (standardised response mean 0.96) and pain NRS (1.04) than MFPDI-pain (0.57). Conservative sample size parameter estimates for standard deviation were pain NRS 2.5, FFI-pain 25 and MFPDI-pain 4, and baseline-outcome correlations were 0.5–0.6, 0.4 and < 0.3, respectively.We demonstrated the feasibility of conducting a future main randomised clinical trial comparing the clinical and cost-effectiveness of SMA, exercises and/or foot orthoses for PHP.ISRCTN 12160508. Prospectively registered 5th July 2016.The online version contains supplementary material available at 10.1186/s40814-021-00808-0. What uncertainties existed regarding the feasibility?2)What are the key feasibility findings?To inform the design of a future main randomised trial of self-management advice, exercise and orthoses for plantar heel pain, we compared the success of three different participant identification methods, determined the number of trial arms and which interventions to test, compared the performance of three potential primary outcome measures and investigated key parameters to inform the sample size calculation. We investigated whether physiotherapists and podiatrists were able to deliver the interventions to protocol in a similar way.3)What are the implications of the feasibility findings for the design of the main study?All three identification methods met the target number of participants. Loss-to-follow-up rates were acceptable but would need to be improved in a future main trial. The interventions achieved high levels of adherence, satisfaction and credibility and were delivered successfully by both physiotherapists and podiatrists. The three potential primary outcome measures were comparable, although the Manchester Foot Pain and Disability Index had lower responsiveness. Key information to determine the sample size calculation was obtained.A future main randomised clinical trial testing these interventions is feasible and acceptable. Either the pain numeric rating scale or the Foot Function Index would be suitable as the primary outcome measure for the future main trial.Plantar heel pain (PHP) describes a range of conditions affecting the plantar heel of unknown aetiology . It is t) feasibility and pilot trial to (i) compare the success of three different identification methods, (ii) determine the number of trial arms and which interventions to test, (iii) determine the primary outcome measure, (iv) inform the sample size calculation and (v) examine potential prognostic baseline factors.Physiotherapists and podiatrists typically use lower limb exercises and/or foot orthoses to treat PHP –15, althThis was a pragmatic, four-parallel-arm, multicentre, randomised, feasibility and pilot trial undertaken in 12 general practices and 2 NHS trusts. The trial protocol is publically available .Participants were community-dwelling adults aged ≥ 18 years identified from participating general practices. Inclusion criteria were self-reported localised pain under the heel aggravated by weight-bearing, worst when first standing or after rest, especially on getting up in the morning or following periods of prolonged sitting; symptom duration of at least 4 weeks; minimum pain score of 2 on a 0–10 numeric rating scale (NRS); owning/access to a mobile or landline telephone; and being able and willing to participate and provide written informed consent. Exclusion criteria were inflammatory arthritis ; fibromyalgia; serious pathologies ; treatment for PHP by a physiotherapist or podiatrist currently or in the last 3 months; corticosteroid injection into the affected foot in the last 3 months; previous or awaiting surgery on the affected foot; allergy to common orthotic device materials ; or unwillingness/inability to undertake interventions or attend clinics, complete follow-up questionnaires in English or receive text messages/phone calls.(i)General practice consultation : general practitioners (GP) were alerted by electronic ‘pop-up’ reminders when they entered a relevant PHP diagnostic code. Patients were screened for potential eligibility by the GP. Potentially eligible, interested patients were given a participant information leaflet (PIL) and asked for written consent to be contacted by the research team. GPs provided advice and, if appropriate, pain relief medication but no other interventions. Patients were posted a trial information pack .(ii)Retrospective medical record review (MRR) : patients who had consulted their GP for foot/ankle pain in the preceding year were identified from medical records. A broad range of symptom codes was used because foot/ankle consultations are often not coded with specific diagnostic labels such as PHP . GPs scr(iii)Population survey : a postal questionnaire was mailed to all adults registered at one general practice to identify those who had heel pain but had not consulted in the preceding year. GPs screened lists to exclude potentially vulnerable patients. Patients were mailed as described in (ii).Potential participants were identified by three methods:Patients identified via (ii) and (iii) who provided consent to contact and who appeared eligible were then posted a trial information pack.Patients sent a trial information pack by any of the three methods were telephoned to explain the trial, confirm eligibility and obtain consent to participate. The completed consent form and baseline questionnaire were returned by postal mail.(i)Self-management advice (SMA) booklet: control arm(ii)SMA booklet plus individualised exercise (SMA-exercises)(iii)SMA booklet plus pre-fabricated foot orthoses (SMA-orthoses)(iv)SMA booklet plus individualised exercise and pre-fabricated foot orthoses (SMA-combined)On receipt of the completed consent form and baseline questionnaire, participants were randomly allocated to an intervention by administrators using the Keele Clinical Trials Unit secure randomisation system. Allocation was concealed from the researchers. Stratified block randomisation was undertaken (fixed block size 4), blocked by treatment site. Participants were randomised on an equal basis to one of the following interventions:Owing to the nature of the interventions, participants could not be blind to treatment allocation. Due to slow recruitment initially, and with agreement from the Trial Steering Committee and funder, randomisation to the SMA control arm ceased following allocation of 11 participants to this arm. Subsequent participants were randomised on a 1:1:1 basis to the three clinician-supported intervention arms to ensure sufficient data were collected to assess the feasibility of these interventions. Participants were informed of their allocation in writing.Participants were posted a bespoke SMA booklet about PHP, which included stretching exercises reproduced with permission from the Versus Arthritis PHP exercise sheet and supplemented with specific advice and information including self-help messages about pain relief, footwear, rest and weight loss .Participants were given the SMA booklet. The treating clinician (a physiotherapist or podiatrist) assessed point tenderness under the heel by pressing the thumb into the underside of the heel  and medial longitudinal arch, and documented foot posture using the Foot Posture Index-6 (FPI-6) . They coParticipants were given the SMA booklet and were assessed as described above. The orthotic device was chosen based on the degree of static rearfoot eversion assessed by the FPI-6 . Pain NRS were collected weekly for 12 weeks by text message or brief telephone call. Other outcomes were collected by postal questionnaire at 12 weeks. Non-responders to the questionnaire were sent a reminder text message/postcard after 10 days and a second questionnaire after a further five days, and then telephoned by a research nurse to collect key outcomes. Non-responders unable to be contacted by telephone were mailed a brief minimum data questionnaire. Participants randomised to the clinician-supported intervention arms recorded adherence, engagement with the intervention and adverse events in a weekly paper diary.The feasibility of the patient identification methods was assessed by comparing the number identified, eligible and recruited, rates of recruitment and retention, and by evaluating the cost and effort of each method.Participant-reported engagement with and adherence to each intervention was assessed including a global intervention adherence scale , number of weeks adherence, the duration and frequency of completing exercises and/or wearing foot orthoses and reasons for non-adherence. Participant-reported intervention credibility and satisfaction were assessed. Fidelity of intervention delivery and participant attendance at intervention sessions were collected using clinician-completed case report forms. Participating clinicians’ evaluations of intervention delivery, including acceptability of the FPI-6, views on the number and duration of intervention sessions, ease of generation of individualised exercise regimen sheets and use of the orthotic devices were assessed via a short online survey and facilitated workshop at the end of the trial.(i)PHP 0-10 pain intensity NRS. Further evaluation was undertaken exploring time to participants’ report of being ‘significantly better’ defined as a NRS score of 0 or 1 recorded over two consecutive weeks.(ii)Change in the Foot Function Index pain subscale (FFI-pain) from baseline to 12 weeks, a validated nine-item self-administered questionnaire , 31.(iii)Change in the Manchester Foot Pain and Disability Index pain subscale (MFPDI-pain) from baseline to 12 weeks, a validated seven-item self-administered questionnaire .Three potential primary outcome measures for the future main trial were evaluated:Completion rates of secondary outcome measures were assessed. Presence of PHP (yes/no) was assessed at baseline, in weekly text messages and at 12 weeks. First step pain (0–10 NRS), foot function  –32, healWe aimed to derive sufficiently precise estimates for the future main trial around: (i) overall adherence to intervention protocols (90% one-sided lower confidence bound), (ii) observed completion rates of the FFI and MFPDI (90% one-sided lower confidence bound), and (iii) the standard deviation (SD) for the pain NRS, FFI and MFPDI (at the level of an inflation factor of 1.1 in the point estimate of the feasibility SD) providing 80–90% confidence in attaining the nominal power of the main trial . We estiResults were analysed descriptively and there was no emphasis on hypothesis-testing. At baseline and follow-up, participants’ descriptive data were summarised using median (inter-quartile range (IQR)) or mean (SD), and frequency counts and percentages for categorical variables overall and by intervention arm.Rates of eligibility and recruitment , number of consenting/randomised participants per month) and retention  were estimated, in total and stratified by identification method. Participants’ baseline characteristics were examined and compared to ineligible and non-consenting patients and between the three identification methods.Fidelity and adherence to intervention protocols were compared between arms including attendance at intervention sessions, adherence to exercise and orthosis interventions and intervention credibility and satisfaction, summarising categorical variables using counts and percentages. Clinician survey and workshop data were analysed descriptively.Performance of the three potential primary outcome measures was assessed using response, item completion rates, floor/ceiling effects and responsiveness (standardised response mean) . AnalysiEffect size, standard deviation, baseline-outcome correlation, inter-correlation of pain NRS scores (for repeated measures) and dropouts to inform the sample size calculation for the main trial were calculated.rs ≥ 0.3) were considered suitable for taking forward as potential baseline covariates in regression-based adjustment within the main trial.Associations between baseline variables , employment status (employed/not employed), baseline general health (EQ-5D-5L)) and the three clinical pain outcomes were calculated using Spearman’s correlation; coefficients exceeding a pre-agreed cut-off  and was completed after the final 12-week follow-up.This trial was developed with research users with PHP who provided feedback on the funding application and protocol, particularly the content of the exercises and the choice of orthoses. They also advised on the potential primary outcome measures for the future main trial which were evaluated in the pilot and feasibility trial and the content of participant-facing paperwork and text messages. The information and advice provided in the SMA booklet was developed with input from four people with PHP and members of Keele’s Patient and Public Involvement and Engagement group over three 1–2 h workshops. One patient representative served on the independent trial steering committee, playing a full part in monitoring trial progress and conduct. Research users with PHP have helped to interpret the findings and advised on dissemination.LLs of 26.9%, 27.8% and 28.8%, respectively). Age and sex of participants and non-participants (n = 169) were similar . All three identification methods recruited their target number of participants: GP consultation 37/70 potentially eligible ; MRR 22/69 ; and population survey 23/112  , 44% male), giving a recruitment uptake of 32.7% , we were able to recruit our target sample size of at least 80 participants. In total, 11 participants were randomised to SMA, 23 to SMA-exercises, 24 to SMA-orthoses and 24 to SMA-combined . They attended a mean of 2.3 consultations . Trial clinicians  successfully delivered interventions in routine clinical appointments  and SMA-orthoses  arms agreed/strongly agreed they had adopted advice given than in the SMA control  or SMA plus exercise  arms. Response to weekly self-report diaries ranged from 42 to 63% across the three clinician-supported intervention arms ; cramp or soreness in the feet or calf muscle ; bruising in the feet or calf muscle ; soreness in other joints ; and other . For orthoses, counts for SMA-orthoses and SMA-combined were cramp or soreness in the feet or calf muscle ; bruising/blister/skin irritation in the feet ; soreness in other joints ; falls ; and other .There were 57 expected, non-serious adverse events in the SMA-exercises arm, 83 in the SMA-orthoses arm and 77 in the SMA-combined arm. For exercise, counts for SMA-exercises and SMA-combined were new/different foot pain/tiredness/stiffness (LLs of 60.4% and 62.7% respectively), ranging between 63 and 75% across intervention arms . Floor-and-ceiling baseline distributional concerns were small (floor: pain NRS 0-1 recorded by 2 (2.5%), FFI-pain 0-10 by 1 (2.2%) and MFPDI-pain 7-8 by 0 (0%); ceiling: pain NRS 9-10 by 4 (5%), FFI-pain 90-100 and MFPDI-pain 20-21 both 0 (0%)). Responsiveness from baseline to 12 weeks was higher for FFI-pain (0.96) and pain NRS (1.04) than MFPDI-pain (0.57) , FFI-pain 21.6 (SD 22.4) and MFPDI-pain 1.7 (SD 3.0). Furthermore, 9/82 (11%) reported being significantly better within the 12-week follow-up (9/55 (16%) of participants who received all weekly text messages)  with change in pain NRS were pain NRS (0.41) and FFI-Pain (0.31) and with change in FFI-Pain were sex (0.39) and pain NRS (0.33). No baseline variables were correlated with change in MFPDI-Pain. Stepwise (backward/forward) multivariable regression models for change in pain NRS  included baseline pain NRS, EQ-5D-5L and age (r = 0.63). A multiple-correlation coefficient of r = 0.63 was obtained from a final stepwise regression model for change in FFI-Pain including baseline FFI-Pain, EQ5D-5L and first-ever episode of heel pain.Baseline variables bilaterally correlated (The exercise interventions were slightly more expensive from UK-NHS and healthcare perspectives and from a societal perspective including work absenteeism costs only, but costs were broadly comparable across the intervention arms when work presenteeism was taken into account Table .The TREADON pilot and feasibility trial can inform the design of a future substantive trial of exercise and orthoses for the management of PHP. All three participant identification methods successfully recruited the target number of participants within the required timescale, although a larger target population was required than planned. Baseline characteristics were comparable, although population survey recruits were older and had milder symptoms, and those recruited via GP consultation more commonly had shorter duration and unilateral heel pain. Loss-to-follow-up rates were acceptable but could be improved by offering a wider range of modalities for communication with participants and data collection such as online questionnaires and e-mails in a future main trial.All interventions were feasible and acceptable to participants and clinicians. Both physiotherapists and podiatrists were able to deliver the interventions to protocol, although some physiotherapists reported lacking confidence in modifying the orthoses and additional training for physiotherapists in the main trial would be necessary. Participants in the three clinician-supported intervention arms attended a mean of 2.3 consultations, whereas the protocol permitted up to 6 sessions. Clinician training in the main trial would need to emphasise the importance of using the full number of available sessions to supervise and progress interventions and we would need to work with participating NHS physiotherapy and podiatry services to ensure this is possible. Participant-reported intervention satisfaction and credibility were high in the three clinician-supported intervention arms but lower in the SMA arm. Signal of efficacy was stronger for SMA-combined and SMA-orthoses than SMA-exercises, although adherence was lower in the SMA-exercises arm. The exercise intervention in a future main trial would need modification to incorporate strategies to enhance adherence. Interestingly, adherence to exercise was better in the SMA-combined arm than the SMA-exercises arm, and the participant burden of receiving the combined intervention did not undermine intervention adherence, satisfaction or credibility. There were no unexpected or serious adverse events.In terms of outcome measures, the completion rates, responsiveness and correlation with other change scores support using the pain intensity NRS or FFI-pain as the primary outcome in a future main trial. Although MFPDI-pain item completion was high, it is less attractive in view of limited responsiveness which has been reported previously , 42. TheStrengths of the trial include the primary care setting which ensures generalisability to most patients with PHP, and the inclusion of four intervention arms which included a self-management advice control arm and a combined exercises and orthosis arm. The interventions could be delivered by physiotherapists or podiatrists making intervention delivery feasible and scalable for a large future main trial. Limitations include lack of participant blinding, common to many trials of non-pharmacological interventions and collecting only self-reported outcome and adherence data. Because of slow initial recruitment, an amendment to the randomisation ratio was made to ensure sufficient participants were recruited to the clinician-supported interventions to allow adequate assessment of their feasibility. Although the recruitment target was reached, this required a larger pooled practice population from which participants were invited than predicted. The number of participants in each arm was small, particularly in the control arm, limiting the conclusions that can be drawn. The technical issues encountered with the text message follow-ups, whilst restricting the ability to fully assess the pain NRS outcome measure, provided insight into the practical issues concerning this data collection method. Whilst patients were involved in the development of the SMA booklet, the booklet did not include information about the association of PHP with anxiety and depression .We have demonstrated the feasibility and acceptability of conducting a future, main, randomised clinical trial to investigate the clinical and cost-effectiveness of individualised exercise and/or foot orthoses for PHP. Success criteria concerning recruitment, intervention training and delivery and adherence to, satisfaction with and credibility of interventions were met. Key information to inform participant identification, outcome measures and sample size were obtained. Both physiotherapists and podiatrists delivered both types of intervention successfully, albeit with important learning for clinician training and intervention delivery in the main trial. Descriptive analyses of clinical outcomes provide signal of efficacy and suggest these interventions are promising, and that a future large trial would be informative for clinical practice.Additional file 1. Summary flow of exercise prescriptionAdditional file 2. Protocol for prescription of pre-fabricated foot orthoses