Multicentric Castleman’s disease is a life-threatening disorder involving a systemic inflammatory response and multiple organ failure caused by the overproduction of interleukin-6. Although renal complications of Castleman’s disease include AA amyloidosis, thrombotic microangiopathy, and membranoproliferative glomerulonephritis, membranous nephropathy is relatively rare. We experienced a case of secondary membranous nephropathy associated with Castleman’s disease.The patient was a 43-year-old Japanese man who had shown a high zinc sulfate value in turbidity test, polyclonal hypergammaglobulinemia, anemia, and proteinuria. A physical examination revealed diffuse lymphadenopathy, an enlarged spleen and papulae of the body trunk. A skin biopsy of a papule on the patient’s back showed plasma cells in the perivascular area and he was diagnosed with multicentric Castleman’s disease, plasma cell variant. Kidney biopsy showed the appearance of bubbling in the glomerular basement membranes in Periodic acid methenamine silver stain and electron microscopy revealed electron dense deposits within and outside the glomerular basement membranes. Since immunofluorescence study showed predominant granular deposition of IgG1 and IgG2, he was diagnosed with secondary membranous nephropathy associated with Castleman’s disease. He was initially treated with prednisolone alone, however his biochemical abnormalities did not improve. After intravenous tocilizumab (700 mg every 2 weeks) was started, his C-reactive protein elevation, anemia, and polyclonal gammopathy improved. Furthermore, his urinary protein level declined from 1.58 g/gCr to 0.13 g/gCr. The prednisolone dose was gradually tapered, then discontinued. He has been stable without a recurrence of proteinuria for more than 6 months.Tocilizumab might be a treatment option for secondary membranous nephropathy associated with Castleman’s disease. Castleman’s disease (CD) consists of a lymphoproliferative disorder that was first reported by Castleman et al. . CD is cThe patient was a 43-year-old Japanese man who had shown a high zinc sulfate value in turbidity test in a health checkup 4 years previously. He was examined by a local doctor who pointed out hypergammaglobulinemia and anemia. He had taken colchicine and febuxostat to treat gout 3 years previously. After presenting mild hematuria and proteinuria 2 years previously, he was referred to our hospital for further examination and treatment. The patient’s height was 170 cm, and his body weight was 85 kg. His blood pressure was 129/83 mmHg, his pulse was 95 beats/minute, and his body temperature was 36.2 °C. He sometimes experienced night sweat. A physical examination revealed diffuse lymphadenopathy, an enlarged spleen and papulae of the body trunk. The laboratory data are shown in Table An excisional submental lymph node biopsy showed diffuse interfollicular plasma cell infiltration Fig. a. ImmunoKidney biopsy revealed 2 instances of global sclerosis in 19 glomeruli. Hematoxylin and eosin (HE) staining showed that there was no inflammatory cell infiltration in the glomeruli Fig. . Based oThe patient was initially treated with prednisolone alone; however, his biochemical abnormalities did not improve. Thus, intravenous tocilizumab (700 mg every 2 weeks) was started. After treatment with tocilizumab, the patient’s C-reactive protein (CRP) elevation, anemia, and polyclonal gammopathy improved Fig. . FurtherWe experienced a rare case of MN that was associated with MCD. The patient’s high zinc sulfate level was considered to be related to CD. In a kidney biopsy specimen, PAM staining revealed a bubbling appearance in the glomerular basement membranes and electron microscopy revealed electron dense deposits within and outside the glomerular basement membranes; these findings were compatible with a diagnosis of MN. Since the IgG depositions were predominantly composed of IgG1 and IgG2, the MN in the present case was thought to be secondary rather than primary.Regarding renal complications of CD, 9–71% of CD patients are reported to have elevated creatinine or blood urea nitrogen . Anothern = 2; plasma-cell type, n = 3; mixed type, n = 1; and unknown type, n = 2 (Table We searched the PubMed database for relevant studies using the following search term: “castleman kidney” or “castleman membranous nephropathy” or “membranous nephropathy angiofollicular lymph node hyperplasia”. CD-associated MN was rare, in addition to the present case, only 7 cases were identified in the relevant English literature Table  7, 12–1–112–17. Previous reports suggested the efficacy of tocilizumab in reducing proteinuria as a renal complication of CD , 17, 18.In conclusion, we experienced a case of CD-associated MN in which tocilizumab was effective for reducing proteinuria. Further reports should be accumulated to determine why patients with CD can present secondary MN as a renal complication, and why tocilizumab can reduce proteinuria in CD-associated MN.


Insomnia symptoms during late pregnancy are a known risk for postnatal depressive symptoms (PDS). However, the cumulative effect of various risk factors throughout pregnancy has not been explored. Our aim was to test how various insomnia symptoms  and other risk factors  in early, mid-, and late pregnancy predict PDS.Using data from the FinnBrain Birth Cohort Study and logistic regression analyses, we investigated the associations of distinct insomnia symptoms at gw 14, 24, and 34 with depressive symptoms  3 months postnatally. We also calculated separate and combined predictive models of PDS for each pregnancy time point and reported the odds ratios for each risk group.Of the 2224 women included in the study, 7.1% scored EPDS ≥ 11 3 months postnatally. Our predictive models indicated that sleep latency of ≥ 20 min, anxiety in early pregnancy, and insufficient sleep during late pregnancy predicted the risk of PDS. Furthermore, we found highly elevated odds ratios in early, mid-, and late pregnancy for women with multiple PDS risk factors.Screening of long sleep latency and anxiety during early pregnancy, in addition to depression screening, could be advisable. Odds ratios of risk factor combinations demonstrate the magnitude of cumulating risk of PDS when multiple risk factors are present.The online version contains supplementary material available at 10.1007/s00127-021-02101-0. Postpartum depression (PPD) affects approximately 8% of women in European countries , and up Perinatal psychiatric disorders are closely related to disturbances in sleep. According to a recent review , 42–100%Most of the studies addressing the connection between sleep during pregnancy and postnatal depression have been cross-sectional , have haWhy should we be interested in investigating maternal insomnia symptoms that already begin during the early stages of pregnancy? First, maternal insomnia symptoms are highly prevalent, even in the first months of pregnancy, and sleep quality typically continues to deteriorate as pregnancy progresses . InsomniHere, we investigated whether different insomnia symptoms from early pregnancy onwards predict increased postnatal depressive symptom (PDS) levels, using a large population-based sample of over 2000 women. Although depression in pregnancy predicts postnatal depression, not all mothers with postnatal depression have prenatal depressive symptoms. Moreover, not all women with insomnia symptoms during pregnancy are depressed, despite insomnia being one of the symptoms of depression. We constructed predictive models separately for early, mid-, and late pregnancy symptom profiles to better understand which insomnia symptoms, in combination with other known risk factors, at different time-points during pregnancy are the most predictive of PDS. We hypothesized that postnatal depressive symptoms can be predicted by prenatal risk factors such as anxiety during pregnancy, multiparity, and history of depression, and that adding sleep parameters would enhance the model fit. Furthermore, we hypothesized that insomnia symptoms already occurring during early and mid-pregnancy, and especially the accumulation of several insomnia symptoms and other risk factors, would increase the risk of PDS.www.finnbrain.fi) recruited between December 2011 and April 2015 from three maternity welfare clinics in the South Western Hospital District and Åland Islands area in Finland. The study recruitment and protocol are described in detail elsewhere [n = 3095) at T1, 90.0% (n = 2784) at T2, 84.3% (n = 2609) at T3 and 73.0% (n = 2260) at T4. The women who responded at 3 months postpartum and within accepted time ranges were eligible for this study, leaving a final sample of 2118 mothers at T1, 2152 at T2, 2095 at T3, and 2224 at T4. Complete data were obtained from all time-points for 1504 women.This study was part of the Finnish FinnBrain Birth Cohort Study , 1 = high/vocational , and 2 = university degree (over 15 years of education); income level was categorized as ≤ €1000, €1001–2000 and > €2000 per month; smoking was dichotomized as no or yes if the mother had reported smoking in either questionnaires or registries and somatic diseases/disorders  were dichotomized as no or yes. Missing values were replaced by an individual mean in the corresponding scale when no more than 33% of answers in the scale were missing.The Edinburgh Postnatal Depression scale (EPDS)  is a tenThe anxiety subscale of Symptom Checklist 90 (SCL-90) , 26 was The Basic Nordic Sleep Questionnaire (BNSQ) is a self-report scale of sleep during the previous month . The queThe Athens Insomnia Scale (AIS) is a self-report questionnaire for sleep disturbances based on ICD-10 criteria . In thisp values and therefore the conclusions (Online Resource 3).Our aim was to investigate the risk of PDS, and we therefore selected logistic regression models as the primary statistical method rather than linear regression models. Logistic models also enabled us to construct cumulative risk estimates that could be visualized in heat maps, which is not possible with linear regression. However, as linear regression models have been commonly applied in analyzing the risk of PDS, we also performed sensitivity analyses using corresponding linear regression models to confirm the stability of the The following dichotomized sleep variables (representing the different manifestations of insomnia symptoms) in T1–T3 served as the explanatory variables: sleep latency ≥ 20 min, night awakenings ≥ 3×/night, early morning awakenings ≥ 3×/week, ‘poor/rather poor’ sleep quality, short sleep ≤ 6 and ≤ 7 h, insufficient total sleep time, decreased well-being, and functional capability during the day.To assess whether insomnia symptoms during pregnancy increased the risk of postnatal depressive symptoms (EPDS score ≥ 11 at T4), simple logistic regression analyses (Online Resource 1) were first conducted separately at the three pregnancy time-points (T1–T3) and for each sleep variable to identify significant predictive factors to be used in the next stage of statistical modelling. The models were adjusted for known confounding factors: mother’s age, parity (primi- vs multipara), educational level (three classes), income level (three classes), somatic illnesses/conditions,concurrent depressive symptoms (EPDS ≥ 11 at each pregnancy time point T1–T3), concurrent anxiety (SCL ≥ 10), and history of depression. These analyses were conducted with SPSS (version 25).n = 1504). We selected the best model by assessing the best predictive value and the smallest Akaike information criterion (AIC), so that the variables were removed from the model until no further reduction could be achieved [Next, we constructed a second series of logistic models (predictive models) to predict the risk of postnatal depressive symptoms (EPDS ≥ 11 at T4). First, we constructed the best predictive models for each time point T1, T2, and T3)  estimates and summarized the results in three separate heat maps representing the cumulative risk at T1  versus women who had dropped out (n = 1004). Women who had more depressive symptoms (EPDS ≥ 10), more anxiety (SCL > 10), and a lower educational and income level at T1 were more likely to drop out (p < 0.05), whereas there was no difference between the groups concerning history of depression. Furthermore, more women who had sleep onset difficulties , woke up ≥ 3/night , or had poor sleep quality  at T1 dropped out of the study before T4.We performed attrition analysis of women who participated at T4 and at least once at T1, T2, or T3 .n = 157) of the participants scored EPDS ≥ 11 at 3 months postnatally (T4). Descriptive data concerning changes in sleep are reported in detail elsewhere (Online Resource 4) [The main background characteristics of the participants are described in Table ource 4) .Table 1CEach insomnia symptom and time point (T1–T3) was studied separately to evaluate the risk of PDS. These results are presented in Online Resource 1. At T1, the following insomnia symptoms were related to T4 depressive symptoms after adjustments: sleep latency ≥ 20 min , night awakenings ≥ 3×/night , an insufficient total sleep time , decreased well-being , and a decreased capability to function . At T2, only insufficient total sleep time , decreased well-being , and a decreased functional capability  remained significant. At T3, sleep latency ≥ 20 min , poor sleep quality , short sleep ≤ 7 h , and an insufficient total sleep time  predicted depressive symptoms at T4.To investigate how insomnia symptoms at T1–T3 predicted the risk of postnatal depressive symptoms after taking into account the selected background variables and measurement results from each time point, we constructed predictive models for each time point separately, as well as cumulative models Table . In the In the best cumulative model (T1 + T2 + T3), AIC was 633.88 and the variables best explaining postnatal depressive symptoms (T4) were multiparity, history of depression, sleep latency ≥ 20 min at T1, anxiety at T1, a decreased functional capability at T2, insufficient sleep at T3, and EPDS ≥ 11 at T2 and T3.n = 3) with all seven risk factors  when compared to women without any. Model T1 was able to explain approximately 14.9% of increased depressive symptoms at T4 . Similarly, the ORs for PDS using measurements from both early and mid-pregnancy .ORs related to various combinations of explanatory factors were extracted separately for each time point. In models T1 and T1 + T2, we used all other risk factors except income level in the heat maps. We found that in early pregnancy . The model calculated without the sleep item was able to predict PDS as well as the other models. For example, the Nagelkerke for the T1 + T2 + T3 model was 0.213.p values remained unchanged.In a second sensitivity analysis, we compared linear (Online Resource 3) and logistic (Online Resource 1) regression models using EPDS as a continuous/dichotomized dependent variable and dichotomized insomnia symptom variables as the dependent variables. The following variables were significantly associated with postnatal depressive symptoms only in linear regression models: night awakenings > 3×/night and poor sleep quality in early pregnancy, sleep latency, sleep quality, and short sleep < 7 h in middle pregnancy, and early morning awakenings, decreased well-being, and decreased functioning in late pregnancy. All the other Our study had two aims: to study the association of maternal insomnia symptoms during pregnancy with postnatal depressive symptoms (PDS) and to create a cumulative model of risk factors for PDS. We demonstrated that not only insomnia symptoms in late pregnancy, but also those in early pregnancy relate to PDS, even after adjusting for concurrent depressive symptoms. To the best of our knowledge, the latter finding has not been reported before, hence emphasizing the importance of already evaluating sleep from the beginning of pregnancy. When we investigated the accumulation of risk by calculating the odds ratios for early, mid-, and late pregnancy and for distinct risk factor combinations, women with multiple risk factors had a markedly increased risk of PDS.Our findings concerning insomnia symptoms during late pregnancy versus depressive symptoms 3 months postnatally are in agreement with previous studies , 33. HowAlthough sleep problems are a diagnostic criterion in depression, sleep disruption has been shown to precede depression in non-pregnant populations –40, withRisk calculators have been used for years in somatic medicine and clinical practice. However, to the best of our knowledge, there are no published reports using heat maps to demonstrate the risk of postnatal depressive symptoms. Most of risk factors used in our models  have been reported in the previous literature . As expeMeaney et al.  have higDepressive symptoms can change during pregnancy, as reported by Korja et al. . HoweverIt is noteworthy that while our predictive models were aimed at using background information and sleep to predict PDS, not all potential known risk factors were accounted for, and hence, these models are not fully comprehensive. In the future, factors such as marital satisfaction, the relationship with one’s own attachment figures, and the life-time history of trauma or abuse could be included in the risk analysis. The Antenatal Risk Questionnaire  or a simThe strengths of this study were the normative population, the relatively large sample size, and the longitudinal study design with an acceptable drop-out rate when compared to other studies. Importantly, our study design considered clinical implications, which was also a major strength.p < 0.001), woke up > 3×/night (p < 0.001), or had poor sleep quality (p < 0.001) dropped out of the study before T4. Thus, our results might not be generalized to the whole population, as the attrition was higher among women at increased risk. However, the detected associations could have been even stronger if these women had continued to participate in this study. In the future, objective sleep registration could provide complementary information. Another limitation was that EPDS has an item concerning sleep. However, our sensitivity analysis (Online Resource 2) repeated the analyses using the EPDS scale without the sleep item and confirmed that the models were able to predict PDS similarly to the original models.One limitation is the attrition in our study. When the women who had returned the questionnaire at 3 months postpartum were compared to those who did not return it, we found attrition to be higher among those women who had higher depressive symptoms in the early pregnancy. Moreover, significantly more women who at T1 had sleep onset difficulties  has also been shown to be a promising treatment option for pregnant women, whether delivered in person  or in diSupplementary file1 (DOCX 34 KB)Supplementary file2 (DOCX 27 KB)Supplementary file3 (DOCX 36 KB)Supplementary file4 (DOCX 16 KB)Below is the link to the electronic supplementary material.


Sarcopenia often co‐occurs with osteoporosis in cross‐sectional studies. However, this association has rarely been studied in prospective studies. This study aimed to investigate the association between sarcopenia categories—along with its individual components—and incident osteoporosis in both middle‐aged and older men and women from the UK Biobank study.A total of 168,682 participants  were included in this prospective study. Categories of sarcopenia (pre‐sarcopenia and sarcopenia), and its individual components, were defined according to the EWGSOP2 criteria (2019). Associations with incident osteoporosis by sex were investigated using Cox‐proportional hazard models adjusted for socio‐demographic, lifestyle and health‐related factors, and morbidity count. Associations between categories of sarcopenia and incident osteoporosis were also investigated by age‐groups and subtype of osteoporosis .women: 1.36 [95% CI: 1.22 to 1.51] and HRmen: 3.07 [95% CI: 1.68 to 5.59]), followed by slow gait speed (HRwomen: 1.30 [95% CI: 1.17 to 1.45] and HRmen: 1.70 [95% CI: 1.43 to 2.02]), were associated with a higher risk of incident osteoporosis in both sexes. Low grip strength was associated with a higher risk of incident osteoporosis in men (HR: 1.38 [95% CI: 1.15 to 1.65]), but not in women. No significant interaction between the exposures and incident osteoporosis by age groups were identified.After a median follow‐up of 7.4 years, 6296 participants were diagnosed with osteoporosis. When the analyses were adjusted for a range of relevant confounding factors, pre‐sarcopenia was associated with 1.3‐times higher risk of osteoporosis in men (HR: 1.30 [95% CI: 1.03 to 1.63]) but not in women, and sarcopenia was associated with 1.66‐times increased osteoporosis risk in women (HR: 1.66 [95% CI: 1.33 to 2.08]) but not in men compared with people without sarcopenia or pre‐sarcopenia. A similar magnitude of associations was found in osteoporosis without pathological fractures but weaker for those with pathological fractures. Within the individual components, low muscle mass (HROur findings demonstrated that pre‐sarcopenic men and sarcopenic women had a higher risk of developing osteoporosis even after adjustment for a large range of potential confounders. Considering that sarcopenia could be prevented, health interventions to improve physical capability may delay or prevent the onset of osteoporosis. Osteoporosis is the result of changes in bone turnover that reduces bone mineral density (BMD), increase bone fragility, and predispose to fragility fractures along with a higher burden of morbidity and mortality.Although several risk factors have been linked to a higher risk of osteoporosis, the evidence has not been unequivocal.n < 5000) or focused mainly on older adults. Also, to our knowledge, there are no studies which have investigated the prospective association between sarcopenia and incident osteoporosis per se. Therefore, this study aimed to investigate the association between sarcopenia categories—along with its individual components—and incident osteoporosis in both middle‐aged and older men and women, using data from UK Biobank, a large prospective cohort study.Previous studies have reported that sarcopenia and osteoporosis often co‐occur.http://www.ukbiobank.ac.uk).Over 500,000 participants (5.5% response rate), aged 37 to 73 years, were recruited from the general population between 2006 and 2010 to be part of UK Biobank.http://biobank.ndph.ox.ac.uk/showcase/showcase/docs/primary_care_data.pdf. Therefore, the analyses of incident osteoporosis cases were restricted to the 228,481 participants with linkage to primary care records. Follow‐up was censored at the primary care data end‐date for the relevant country or the date of incident osteoporosis. Osteoporosis was defined as M80  M81  or M82 (osteoporosis in diseases classified elsewhere) using the International Classification of Diseases, 10th revision (ICD‐10).Incident osteoporosis cases were ascertained through linkage of primary care records. Diagnosis of osteoporosis was primarily based on dual‐energy X‐ray absorptiometry (DXA) scan results. However, women >75 years that experienced a fragility fracture may be diagnosed with osteoporosis prior to a DXA scan. Currently, this information was available only for 45% of the UK Biobank cohort  until May 2017 for Scotland, September 2017 for Wales, and August 2017 for England. The detailed linkage procedures relating to primary care records are available at 2 in men and <5.5 kg/m2 in women. Grip strength was measured using a Jamar J00105 hydraulic hand dynamometer. The mean of the right and left values was derived and expressed in absolute units (kg). The cut‐off points applied to define low grip strength were <27 kg in men and <16 kg in women.Muscle mass index was derived from skeletal muscle mass (kg) divided by height (m) squared using the total body composition measured via bioimpedance  by trained nurses. To estimate skeletal muscle mass, the Janssen equation was utilized.n = 87), sarcopenia and severe sarcopenia were pooled together (hereafter referred to as sarcopenia). We followed this approach to avoid unreliable and unpowered hazard ratios (HR) estimates. The pre‐sarcopenia and sarcopenia groups were mutually exclusive. For this study, only white European participants were included because of the ethnic differences in the reference values for sarcopenia.Using these three physical capability markers, sarcopenia was classified in accordance with the EWGSOP2 statement as pre‐sarcopenia, defined as low grip strength only ; sarcopenia, defined as low grip strength plus low muscle masshttp://www.ukbiobank.ac.uk).Age at baseline was calculated from dates of birth and baseline assessment. Area‐based socioeconomic deprivation was derived from postcode of residence, using the Townsend score.http://www.ukbiobank.ac.uk/). This work was conducted under the UK Biobank application number 7155.UK Biobank was given favourable opinion by the North West Multi‐Centre Research Ethics Committee (Ref: 11/NW/0382). The study protocol is available online (Descriptive characteristics are presented as means with standard deviations (SD) for quantitative variables, and as frequencies and percentages for categorical variables by sex.n = 3472). Only participants with complete data available for the three physical capability markers used to define later categories of sarcopenia, the covariates included in the analyses, and incident osteoporosis were included. Follow‐up time was used as the time‐dependent variable.Associations between categories of sarcopenia (pre‐sarcopenia and sarcopenia) and incident osteoporosis were investigated using Cox‐proportional hazard models stratified by sex. Non‐sarcopenic individuals  were used as the reference group. The results are reported as HR and their 95% confidence intervals (95% CIs). The proportional hazard assumptions were checked using Schoenfeld residuals. Associations between the three individual physical capability markers  and incident osteoporosis were investigated using the same analysis. For each component, the normal range of the physical capability maker defined by the EWGSOP2 was used as the reference group.We ran five models including an increasing number of covariates: model 1  included socio‐demographic covariates (age and deprivation); model 2 additionally included lifestyle and health‐related factors: smoking, physical activity, alcohol intake and consumption of red and processed meat, body fat, and morbidity count at baseline . Model 3, as model 2, but additionally adjusted for serum vitamin D levels, use of corticosteroids and H2 blockers, falls, fractures over the previous 5 years, and menopause and hypogonadism in women and men, respectively. These covariates were chosen as they are potentially causal for sarcopenia and osteoporosis. Two sensitivity analyses were also performed: model 4, as per model 3, but using a 2‐year landmark period which excluded participants who experienced events within the first 2 years of follow‐up (1323 women and 290 men) to minimize potential reverse causation; and model 5, as per model 3, but further adjusted for calcium and protein intake . Additionally, the association of categories of sarcopenia with subtypes of osteoporosis outcomes  was also investigated.The sex‐specific cumulative crude hazard rate of incident osteoporosis and categories of sarcopenia was estimated using the Nelson–Aalen estimator. Finally, to investigate whether the associations between categories of sarcopenia and incident osteoporosis differed by age, the models were re‐run stratified by the following age categories: (i) approximately when menopause or hypogonadism start (≥45 and <45 years as well as ≥55 and <55 years), (ii) using different definitions for aging (≥60 and <60 years as well as ≥65 and <65 years).Stata 16 statistical software (StataCorp LP) was used to perform all analyses.n = 2080), osteoporosis at baseline (n = 3472), non‐white ethnicity  or incomplete covariate data , 168,682 participants (48.8% women) had data available on all essential variables . Non‐significant associations were identified between categories of sarcopenia and osteoporosis with pathological fractures.Sex‐specific associations between sarcopenia categories and incident osteoporosis are shown in FigureTableTableFiguresThe risk of osteoporosis in pre‐sarcopenic and sarcopenic men was 1.40‐ and 4.97‐times higher, respectively, in comparison to non‐sarcopenic men in the minimally adjusted model (model 1). The associations were attenuated when the analysis was further adjusted for morbidity count, lifestyle and health‐related factors models 2 and 3) for sarcopenic men but remained significant for pre‐sarcopenic men (HR: 1.30 [95% CI: 1.03 to 1.63]) , but not in women. Based on model 3, low muscle mass was the physical capability marker associated with the highest risk of incident osteoporosis in both sexes.Of the three physical capability markers used to define categories of sarcopenia, slow gait speed and low muscle mass were independently associated with 1.30‐ and 1.36‐times higher risk of incident osteoporosis in women and 1.70‐ and 3.07‐times higher risk of incident osteoporosis in men, respectively .UK Biobank provided the opportunity to test our hypothesis in a large and well characterized general population‐based cohort of middle‐aged and older adults. Consequently, analyses could be adjusted for multiple potential confounders. Moreover, incident osteoporosis was ascertained through linkage primary care records. However, UK Biobank is not representative of the UK population in terms of socio‐demographic, lifestyle and prevalent disease. Therefore, while risk estimates can be generalized,In conclusion, our findings demonstrated that pre‐sarcopenic men and sarcopenic women had a higher risk of incident osteoporosis even after adjustment for a large range of potential confounders. Since sarcopenia could be prevented, early public health strategies aimed at improving physical capability may help to prevent or delay some cases of osteoporosis. Randomized trials would help address this question.None declared.UK Biobank was established by the Wellcome Trust medical charity, Medical Research Council, Department of Health, Scottish Government and the Northwest Regional Development Agency. It has also had funding from the Welsh Assembly Government and the British Heart Foundation. FP‐R receives financial support from the Chilean Government for her PhD (ANID‐Becas Chile 2018 – 72190067).F.P.‐R., J.P.P., F.K.H., and C.C.‐M. contributed to the conception and design of the study, advised on all statistical aspects, and interpreted the data. F.P.‐R. performed the literature search. F.P.‐R. performed the analyses with support from J.P.P, F.K.H., and C.C.‐M. All authors critically reviewed this and previous drafts. All authors approved the final draft for submission, with final responsibility for publication. J.P.P, F.K.H., and C.C.‐M. contributed equally to this work and are joint senior authors. F.P.‐R., F.K.H., J.P.P., and C.C.‐M. are the guarantor.The Corresponding Author has the right to grant on behalf of all authors and does grant on behalf of all authors, a worldwide licence to the Publishers and its licensees in perpetuity, in all forms, formats and media (whether known now or created in the future), to (i) publish, reproduce, distribute, display and store the Contribution, (ii) translate the Contribution into other languages, create adaptations, reprints, include within collections and create summaries, extracts and/or, abstracts of the Contribution, (iii) create any other derivative work(s) based on the Contribution, (iv) to exploit all subsidiary rights in the Contribution, (v) the inclusion of electronic links from the Contribution to third party material wherever it may be located; and, (vi) licence any third party to do any or all of the above.The lead author, (the manuscript's guarantor), affirms that the manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned  have been explained.Figure S1. Diagram – Participants according to the different classification by idividuals capability markers and categories of sarcopenia by sex.Figure S2. Cumulative hazard plot of osteoporosis incidence by categories of sarcopenia and follow‐up time in women.Figure S3. Cumulative hazard plot of osteoporosis incidence by categories of sarcopenia and follow‐up time in men.Table S1. Baseline characteristics by categories of gait speed and sex.Table S2. Baseline characteristics by categories of grip strength and sex.Table S3. Baseline characteristics by categories of muscle mass and sex.Table S4. Associations between categories of sarcopenia with incident osteoporosis by sex.Table S5. Associations between categories of sarcopenia with subtypes osteoporosis incidence by sexTable S6. Associations between categories of sarcopenia and incident osteoporosis by age groups and sex.Click here for additional data file.


Malnutrition is a usually observed condition among patients on hemodialysis and is considered one of sturdiest indicators of mortality and morbidity.The current study was performed to assess the prevalence of malnutrition, to verify whether functional status is associated with malnutrition, and to explore the probable factors related to malnutrition among a sample of hemodialysis patients at Hebron Governmental Hospital in West Bank, Palestine.A cross-sectional study was conducted on hemodialysis patients in Hebron Governmental Hospital at Hebron city/Palestine. An interview-based questionnaire was used to obtain information related to socio-demographics, dialysis, medical history, lifestyle, anthropometric measurements, dietary data, and functional status. Renal inpatient screening tool  was also utilized to screen hemodialysis patients for malnutrition. Furthermore, biochemical tests were obtained during the study period from medical files of the studied patients.A total of 153 patients, having a mean age of 50.1 ± 16.6 years, were involved in the final analysis. The results indicated that the prevalence of high risk of malnutrition (45.4 %). Moreover, high risk of malnutrition was significantly associated with occupation, and walking. It was further found that patients with high risk of malnutrition are more likely to had osteoporosis, unable to ambulate, didn’t feel that the amount of food they eat is enough. Our findings also figured out that some complications during hemodialysis session  and some hemodialysis side effects  were significantly correlated to malnutrition.A high prevalence of malnutrition was revealed among hemodialysis using renal iNUT screening tool. Improving nutritional assessment methods for patients on hemodialysis is highly needed. Findings reveals that risk of malnutrition is associated with multiple factors such as osteoporosis, occupation, walking, ability to ambulate, certain complication during hemodialysis session, and some hemodialysis side effects. Further studies are highly recommended. The incidence of chronic kidney disease is growing all over the world. Universally, patients having chronic kidney disease keeps to increase at the rate of 7 % yearly . The mosProtein-energy wasting (PEW) is a relatively prevalent health issue among patients undergoing hemodialysis (HD) therapy . Since tThe Renal Inpatient Nutrition Screening Tool  was evolved for the specialist renal ward at St George’s Hospital (SGH), as a result of a scarceness of a validated renal-specific nutrition screening tool for renal failure patients. The scoring system of iNUT was according to Malnutrition Universal Screening Tool because of nursing staff intimacy with the general tool .There is evidence that hemodialysis therapy can elevate energy expenditure by 20 % in a hemodialysis session . Other fHemodialysis therapy is usually associated with a large number of comorbidities including; secondary hyperparathyroidism, diabetes, infectious diseases, gastrointestinal disorders, and diabetes mellitus .The inception of hemodialysis is linked with a decrease in functional status among elderly , resultiIt is highly recommended to use both physical and nutritional therapy in order to enhance the outcomes and experience of patients on hemodialysis . PrecociThe main purpose of this cross-sectional study was to determine the prevalence of malnutrition among a sample of hemodialysis patients at Hebron governmental hospital, Palestine. Other purposes were verifying whether functional status of patients on hemodialysis is associated with malnutrition, and determining factors predicting malnutrition in hemodialysis patients. In general, this study will assist in applying appropriate interventions which aims to attain an optimal nutritional status in a population of hemodialysis patients.This cross-sectional design study was performed a representative sample of Palestinian hemodialysis patients in Hebron Governmental Hospital in Hebron city, Palestine.The sample size was estimated depending on the number of hemodialysis patients in each unit. G power software for sample size calculation was used with 5 % margin of error and 95 % confidence level. The sampling method used in the study is purposive sampling . The inc The study protocol, which has a reference number of KA/41/2021, was approved by the Deanship of Scientific Research Ethical Committee at Palestine Polytechnic University committee. Permissions and approval to conduct the study were obtained from the Palestinian Ministry of Health. Written informed consent was also obtained from each participant.Interview based pre-tested questionnaire was administered to hemodialysis patients during hemodialysis sessions. Data collection started on August 2020 and ended on November 2020 by a group of four nutritionists. Hemodialysis patients were briefed on the objective of the study, then, the questionnaires were administrated upon verbal consent from the patients. The collected data included sociodemographic data , medical history and laboratory values, anthropometric measurements, dialysis-related data, hemodialysis side effects, clinical assessment, and functional status.Questions regarding demographic data, including patient age, gender, marital status, and educational level, were asked for each patient. Data about lifestyle habits  were elicited from the patients.Medical history including self-reported questions about previous surgery, and the presence of comorbidities . Blood tests, including serum albumin, total protein, potassium, sodium, phosphorous, calcium, and hemoglobin, were also obtained from patients’ records.In this part of the questionnaire, the patients were asked numerous questions including; the number of months on hemodialysis, number of hemodialysis sessions per week, duration of hemodialysis session, and nutrition consultation.Data regarding the experienced complications during hemodialysis session , and side effects of hemodialysis  were elicited from patients.Questions regarding nutrition consultation, the source of nutrition information, and whether they were given a specialized course in renal nutrition were asked in this part of the questionnaire.Renal inpatient nutrition screening tool , a validated renal-specific nutrition screening tool, used to evaluate malnutrition. The renal iNUT includes questions on height, measured weight, estimated weight loss, body mass index (BMI), renal-specific details on weight, the use of nutritional supplements, appetite, and food intake. Based on these questions every patient was given a score that reflect his nutritional status as follows: high (score ≥ 2), medium (score = 1), and low (score = 0) of malnutrition with proper action plan .Anthropometric indices including  were used to examine the nutritional status of hemodialysis patients. Body weight was weighted pre-dialysis and post-dialysis according to the standard anthropometric procedures described by Lee &Nieman ; the parData regarding weight changes during the last six months, the presence of gastrointestinal symptoms, loss of subcutaneous fat , and muscle wasting (deltoid muscle), presence of ascites or edema were collected in this section of the questionnaire.Dietary practices questions were designed to see dietary changes between non-dialysis days and dialysis days. Questions regarding the number of total meals, main meals and snacks per day, skipping meals, patient’s appetite, diet changes during dialysis days, his/her perception about the quantity of food that he/she eat, and the changes in the size of food he/ she eat in dialysis days were included in this section.The questionnaire included three self-reported questions designed to assess poor functional status, which is realized by the presence of any of the three co-morbid situations as assigned in form CMS-2728- (a) inability to ambulate, (b) need of support with daily activities or (c) inability to transfer .The Statistical Package for the Social Sciences SPSS TM, version 21 was used to analyze the collected data, 5 % alpha level and 80 % power was considered in all of the statistical tests. Continuous variables were assessed for normality of distribution graphically and via the Shapiro-Wilk Test. Descriptive analysis including the means and the standard deviations were used to analyze the continuous data, the categorical data were described in percentages and frequencies. The prevalence of malnutrition was presented in percentages. Kruskal-Wallis test and one-way ANOVA test were used to determine the relationship between continuous variables and Chi-square test was used to determine the relationship between categorical variables. Logistic regression was performed to estimate the magnitude of relationship between the response variable of malnutrition risk and the explanatory variables . Hosmer-Lemeshow goodness of fit test was done to assess how well the model fit the data.n = 73) females and 52.0 % (n = 79) males. The other patients were excluded mainly due to missing data.Figure n = 116) were married, while the other 23.7 % (n = 36) were not married “divorced, single, widow”. Nearly half of the patients 47.4 % (n = 72) received a primary education, and only 3.9 % (n = 6) of patients did not receive a formal education. It also revealed that nearly half of the nurses 53.3 % (n = 81) were living in the cities, while the rest 46.7 % (n = 71) were living either in villages or camps. The majority of participants 96.7 % (n = 147) were living with their families , while only 3.3 % (n = 5) were living alone.The mean age of hemodialysis patients was 50.1 ± 16.6 years, ranged from 18 to 85 years old. The analysis revealed that most of the enrolled patients 76.3 % (n = 51) of the participants walk, whilst nearly half of participants 48 % (n = 73) don’t walk. Furthermore, a low percentage of patients 2.0 % (n = 3) reported that they do sport either at home or at gym with a mean frequency of 2.9 ± 0.4 times per week.The findings showed that most of the participants (81.6 %) stated that they are non-smokers. It was also found that the participants have a mean time of watching television by 2.2 ± 3.7 h per day and have a mean sleeping time of 7.1 ± 1.3 h per day. Moreover, nearly 33.6 % (n = 123), followed by diabetes mellitus 46.7 % (n = 71). While a small percentage of patients reported that they had chronic obstructive pulmonary disorder, asthma, and angina by 12.5 % (n = 19), 12.5 % (n = 19), and 8.6 % (n = 13), respectively. Furthermore, it observed that the vast majority of patients 92,8 % (n = 129) stated that they had previously made a surgical operation.The results of medical history revealed that a high percentage of patients were having a hypertension 80.9 % , then headache by 30.9 % (n = 46), while the least noticed complication during the session was vomiting by 11.8 % (n = 18). And as we can see in Table n = 62), 80.8 % (n = 59), and 65.8 % (n = 48), respectively. On the otherhand, a minority of patients have stated that they were experiencing pericarditis by 6.8 % (n = 5) as dialysis side effect.Table n = 5) received nutrition consultation before hemodialysis sessions, 24.3 % (n = 37) received consultation during hemodialysis session, and 13.3 % (n = 20) received consultation post hemodialysis session. The findings also showed that most patients 79.4 % (n = 112) get the nutrition information from health team workers , followed by nutritionists 11.3 % (n = 16), while the rest get the information from other sources “e.g., internet, Facebook, friends”. Furthermore, only 7.9 % (n = 12) of patients stated that they were given a specialized course in renal nutrition.Our data analysis showed that minority of patients 16.4 % (n = 29) than in males 24.7 % (n = 19). Furthermore, a high percentage of patients 87.5 % (n = 133) who had an acceptable BMI while only 12.5 % (n = 19) of patients looked malnourished, which means that the BMI is lower than 20 kg/m2. It also found that the percentage of females on hemodialysis 8.2 % (n = 6) who take nutritional supplements were significantly higher in comparison to males on hemodialysis 0.0 % (n = 0). Moreover, the analysis revealed that patients’ food intake was similar to usual days in nearly half of the patients 50 % (n = 76). Furthermore, the patients’ appetite was significantly worse in nearly 61.6 % (n = 45) females compared to males 44.3 % (n = 35). Figure n = 69) patients had a high risk of malnutrition, 36.8 % (n = 56) patients had a low risk of nutrition, and only 17.8 (n = 27) patients had no risk of malnutrition.Our statistical analysis showed that the proportion of patients who unintentionally lost weight was significantly higher in females 40.3 % n = 9 than inn = 57) of the participants had normal weight, 26.3 % (n = 40) were overweight, 28.9 % (n = 44) were obese, whilst only eleven participants 7.2 % were underweight.Generally, 37.5 %  stated that they suffered from gastrointestinal symptoms, . It also found that the loss of subcutaneous fat under eyes was normal in most patients by 82.2 % (n = 125). Furthermore, it observed that 82.2 % (n = 125) of patients had a normal wasting in deltoid muscle. Seventy-five patients 49.3 % suffered from normal oedema. Nearly half of the patients 53.9 % (n = 82) stated they had normal ascites. Our findings revealed that about half of the participants 45.0 % (n = 67) lost less than 5 % of their weight during the last six months (Table The majority of patients 82.9 % (n = 101) of patients were used to consume the same number of main meals and snack in both normal days and dialysis days. While only 15.8 % (n = 24) of patients ate more meals  during non-dialysis days compared to dialysis days. About half of the participants 52.6 % (n = 80) were used to ate less than 3 main meals in a dialysis day compared to regular day. The vast majority of patients 94.1 % (n = 143) stated that they consume less than three snacks in a dialysis day compared to a regular day. And only 2.6 % (n = 4) of participants were used to consume three snacks in dialysis days in comparison to regular days. In addition, our results reveals that the percentage of patients, who usually skip their meals, is almost 40.1 % (n = 61), and about 20.4 % (n = 31) of patients stated that they do not usually skip their meals. Furthermore, 59.2 % (n = 90) of patients reported that they skip the same number of snacks in dialysis days compared to non-dialysis days.Nearly 66.4 % (n = 99), whereas 23 % (n = 35) of patients felt that the amount of food they eat is not enough. Nighty-nine patients 66.4 % also stated that the proportion of food they consume in dialysis days is the same as in non-dialysis days, whereas sixteen patients 10.7 % reported that the portion of food they eat in dialysis days is smaller compared to the portion of food they eat in normal days. Moreover, our analysis found that nearly half of patients 47.4 % (n = 72) didn’t lose their appetite after initiation of hemodialysis sessions. Half of the patients 50.3 % (n = 76) reported that their appetite in dialysis days was the same as in normal days, while twenty-seven patients 17.9 % indicated that their appetite in dialysis days was lower compared to their appetite during normal days. The vast majority of patients 74.3 % (n = 113) have consumed full liquid diet during hemodialysis diet.The proportion of patients, who felt that the amount of food they eat is enough, is about 65.1 % (n = 97), 59.2 % (n = 90), and 48.7 % (n = 74), recpectively.Table p < 0.05). On the otherhand, malnutrition was not significantly associated with patients’ gender, marital status, educational level, type of housing, and area of living (p > 0.05) using Chi-square test. By using Kruskal- Wallis test, we explored that patients’ age, watching hours a day, sleeping hours a day, and frequency of doing exercise a week (p > 0.05). Our multivariate analysis showed that sociodemographic variables were not a significant predictors of malnutrition. On the contrary, it observed that patients who don’t walk have a double risk of being malnourished as compared to patients who walk (Exp (B): 2.2, p < 0.01, CI: 1.28–3.7) using Binary logistic regression.Table p < 0.05). Furthermore, Table Table P > 0.05). Furthermore, it was observed that nutrition consultation, source of nutrition information is not significantly associated with malnutrition (p > 0.05).The analysis revealed that malnutrition is neither associated with biochemical data nor with the number of comorbidities that a hemodialysis patient is suffering from (p > 0.05). Our multivariate analysis also revealed that functional status was not a significant predictor of malnutrition risk.Table p < 0.05) using ANOVA test. The results also indicate that hemodialysis side effects such as; itching (65.2 %), and access site complications (34.8 %) were more common in participants with high risk of malnutrition than in those with no risk of malnutrition (p < 0.05). Binary logistic regression revealed that hypotension during hemodialysis session  and access site infection (Exp (B) :3.4, p < 0.05, CI: 1.2- 14.22) were a significant predictor of malnutrition risk. Our findings also indicates that a high risk of malnutrition is more common among patients who didn’t feel that the amount of food they eat is enough (57.1 %) compared to those who felt that the amount of food they eat is enough (37.4 %) (p < 0.05).Complications during hemodialysis session including; nausea (27.5 %), headache (39.1 %), and hypotension (40.6 %) were more prevalent in patients with high risk of malnutrition than in those with no risk of malnutrition  of hemodialysis patients at Hebron Governmental Hospital at Hebron city, Palestine had a high risk of malnutrition. The prevalence of malnutrition in our sample was generally lower than that reported in previous studies including; Egypt (67 %) , BaghdadOur research confirmed that the existence of certain diseases, especially osteoporosis has a significant influence on patients’ nutritional status as assessed by renal iNUT score. Malnutrition is an important risk factor for osteoporosis in patients undergoing hemodialysis . SecondaOur study showed malnutrition is not correlated with the level of education among hemodialysis patients. This finding is inconsistent with a former Saudi Arabian study, which is found that un educated patients had a higher risk of malnutrition compared to highly-educated patients . The curIn contrast to a Romanian study , and a fAccording to the current study, functional status is one of the predictors that have significant relationship with high risk of malnutrition. Our analysis indicates that high risk of malnutrition was significantly more predominant among hemodialysis patients who are not able to ambulate (55.1 %) compared to those who are unable to ambulate (42.0 %). This finding was in agreement with a former research which has found that malnourished patients had lower functional status in comparison to their well-nourished counterparts . FurtherFurthermore, our findings reveal that high risk of malnutrition were significantly more prevalent in patients who did not walk compared to their counterparts who usually walk. This finding robustly goes in agreement with the hypothesis that malnutrition and reduced protein metabolism accompanied with increased catabolism sturdily effect the capability of patients on hemodialysis to carry out physical activities .Moreover, it was found that complications during hemodialysis session such as nausea, headache, and hypotension were more prevalent in patients with high risk of malnutrition than in those with no risk of malnutrition. The results also indicate that hemodialysis side effects such as; itching, and access site complications were more common in participants with high risk of malnutrition than in those with no risk of malnutrition. In addition, our findings indicates that a high risk of malnutrition is more common among patients who didn’t feel that the amount of food they eat is enough compared to those who felt that the amount of food they eat is enough. The relationship between these variables and malnutrition has not yet been discovered before.Results of the current research must be considered in the framework of its design limitations. First, the major limitation of the current study resides in its design. Being cross-sectional, it’s impossible to derive a cause-effect relationship. Second, the study was limited to hemodialysis patient at Hebron Governmental Hospital and does not exemplify the overall hemodialysis patients’ category in Palestine. Third, inflammatory markers were also required to be extracted in order to have better overall clinical status of the patients and with a view to ease the interpretation of albumin level differences. Fourth, dietary assessment was not included in the study. Finally, Secondly, we have used self-reporting methods which increases the risk of respondent error. Regardless of these limitations, our research will give indicative marks to health professionals towards the nutritional status among patients on hemodialysis and the necessity for consultation in hemodialysis centers in Palestine.Screening the nutritional status in patients on hemodialysis is a fundamental issue and necessitate to be pursued by a team of health professionals in hemodialysis centers. This research showed an increased prevalence of malnutrition among hemodialysis patients at Hebron Governmental Hospital, therefore the nutritional status of patients on hemodialysis requires more alertness by renal dietitians and constant education, nutrition counseling, and nutrition assessment. Furthermore, findings indicate that osteoporosis, walking, certain hemodialysis side effects , and complications during hemodialysis session  are significantly associated with malnutrition. There is an urgent necessity to expand the research to further Palestinian hospitals. At last, there is a necessity to pursue the research prospectively to uncover a cause-effect relationship.


Propionic acidemia (PROP) is an autosomal recessive inherited deficiency of propionyl‐CoA carboxylase (PCC) which is involved in the catalytic breakdown of the amino acids valine, isoleucine, methionine, and threonine. PROP nutritional management is based on dietary protein restriction and use of special medical formulas which are free of the offending amino acids, but are enriched in leucine. The resulting imbalance among branched‐chain amino acids negatively impacts plasma concentrations of valine and isoleucine, which might impact growth in children with PROP.Our primary objective was to describe dietary protein and calorie intake and their impact on long‐term growth outcomes of four PROP patients. This was accomplished through a longitudinal retrospective chart review following the cohort from birth to 18 years.Z scores, and elevated weight and body mass index (BMI) Z scores. Energy intakes for all subjects were within 80% to 120% of the dietary reference intakes for age. All children had low intakes of intact protein compared with current guidelines and were supplemented with medical formula and single l‐amino acids , which led to the excess consumption of total protein.All children (n = 4) had poor growth outcomes with persistently reduced height‐for‐age Z scores. Restricted intact protein consumption together with the abundant use of medical formula could have affected overall growth. To optimize dietary management in patients with PROP, further research is needed to determine the optimal intake of medical formula relative to intact protein.Despite adequate total protein and energy intakes, all children had persistently low height  BCAAsbranched‐chain amino acidsBMIbody mass indexDRIsdietary reference intakesILEisoleucineLEUleucineMETmethioninePCCpropionyl‐CoA carboxylasePROPpropionic acidemiaRDArecommended dietary allowanceTHRthreonineVALvalineZ scores, elevated weight, and BMI Z scores, likely influenced by reduced intake of intact protein:energy ratio.In a longitudinal retrospective chart review, children with propionic acidemia show inadequate growth indicated by reduced height 1Propionic acidemia (PROP) (OMIM 606054) is an autosomal recessive, inherited metabolic disorder caused by a defect in the mitochondrial enzyme propionyl‐coenzyme A (CoA) carboxylase (PCC) (EC 6.4.1.3). PCC catalyzes the reversible biotin‐dependent conversion of propionyl‐CoA to D‐methylmalonyl‐CoA. Propionyl CoA is an intermediate in the catabolism of isoleucine (ILE) and valine (VAL), two of the three branched‐chain amino acids (BCAAs), as well as threonine (THR), methionine (MET), and odd chain fatty acids.Nutritional management is a mainstay in the treatment of PROP with the goal to reduce the accumulation of toxic metabolites by restricting dietary protein sources of the propiogenic amino acids The primary objective of this study is to describe historical and current dietary therapeutic practices and their impacts on long‐term growth outcomes in four PROP patients through a natural history study. A retrospective chart review was conducted on the four PROP patients between 1999 and 2018. We hypothesize that the patients would have poor growth outcomes associated with high medical formula consumption relative to intact protein consumption.22.1This study was approved by the University of British Columbia Children's and Women's Research Ethics Board H19‐02912). A retrospective chart review was conducted on four pediatric patients with PROP followed in the Biochemical Diseases Clinic at BC Children's Hospital, Vancouver, British Columbia, Canada. Patients were two sibling pairs, with the older siblings  diagnosed after an acute metabolic decompensation in the neonatal period, and the younger siblings diagnosed right after birth as they were screened (Table 9‐02912. 2.2l‐carnitine (100‐300 mg/kg) orally. Metronidazole to halt growth of gut bacteria producing propionic acid were given in alternating intervals 2 weeks on, 2 weeks off, as well as various supplements were also given as needed. Low intact protein convenience foods were at the expense of the families until 2014, and then provided under a public health care grant. Intact protein restriction was based around the age‐related DRI and fine‐tuned according to biomarkers indicating metabolic stability, nutritional and growth‐related needs. The following guidelines were used to inform protein and energy supply: prior to 2004, the Ross recommendations were used as dietary management guidelines,Following diagnostic confirmation of PROP and/or stabilization after the initial metabolic crisis, all patients were started on nutrition therapy with restricted natural protein intake and additional medical formula depleted of the offending amino acids . Medical formula was provided under a public health care grant. Additional nutritional supplements, such as calcium, iron, other trace elements, and vitamins, were added according to needs. In addition to the nutrition therapy, all patients received supplementation of Metabolic stability was determined via regular monitoring of blood gas, anion gap, plasma ammonia levels, and urine ketones. Other biomarkers such as blood lactate levels and urine citrate/methylcitrate ratio were also determined.During sick times, patients received a home sick day formula which supplied 120% of estimated energy requirement (EER) and 50% to 100% of the regularly prescribed intact protein, while the missing amount of intact protein was replaced by respective amounts of medical PROP formula. Zero percent intact protein was prescribed when admitted with acute metabolic decompensation, advancing gradually to 50% intact protein intake, then 100% upon achievement of metabolic stability.2.32.3.12. Anthropometric measurements were expressed as age‐ and gender‐specific Z‐scores, using the WHO Anthro and Anthroplus software for 0 to 5 years of age and 5 to 19 years of age, respectively. For 0 to 5 years, indicators included: weight for age, weight for height, and height for age. For 5 to 19 years, indicators included: weight for age, height for age, and BMI for age  (http://www.who.int/growthref/tools/en/).Anthropometric data were collected from medical and dietetic records during clinic visits. Weight and length for children under 2 years of age were obtained by standard techniques using digital baby weighing scales and crown‐heel length on a scale length board, respectively. Weight and height for children older than 2 years of age were measured using digital scale and a stadiometer, respectively. Measurements were performed by the dietitian or clinic nurse. BMI was calculated using the equation kg/m2.3.2l‐isoleucine and l‐valine) supplements. Protein intake was also separately calculated as g/kg/d of intact protein and protein from medical formula. Energy intake was collected as kcal/kg/d. The protein to energy (P:E) ratio was calculated based on amount of total protein in grams per 100 kcal per day. The calculated P:E ratio values were compared with P:E values associated with optimal growth in patients with inborn error of protein metabolism described by Evans et al .Dietary data were collected on the basis of formula recipes delivered via tube feeding and/or food records for the oral intake. Dietary intake was analyzed manually following Ross protocols 1999‐2004) and then using the MetabolicPro software from Genetic Metabolic Dietitian International (GMDI). Dietary data were only collected when patients were metabolically stable. We excluded data during sick days as patients were consuming a special sick day formula, number of sick days for each subject was calculated, and is presented with patient characteristics , for 5 to 10 years , for 10 to 18 years . Growth data were also expressed as median and range (minimum‐maximum).Statistical analysis was performed using GraphPad Prism 8.0 . Descriptive statistics were used to compare actual energy and protein intakes with different recommendation; all data were expressed as median and range (minimum‐maximum). Growth 33.1Three female and one male patient with PROP were followed in the Biochemical Disease Clinic at BC Children's Hospital Table . All pat3.2Z scores, and elevated weight and BMI Z scores  for weight for height, and −0.717 (range: −1.36 to −0.2) for height. From 5 to 10 years, height Z scores declined to a median of −1.03 (range: −1.78 to −0.23) for all patients. However, their median BMI for age Z scores were at 1.35 with a range of 1.01 to 2.03 that translates to a BMI percentile of (>84 and <95) and classifies them as overweight. Between 10 and 18 years, height Z scores, calculated on three patients, decreased even more to a median of −1.4. After 10 years of age, the WHO recommends not using weight for age, as it does not distinguish between height and BMI during pubertal growth spurts. Thus, our data are only presented as BMI for age from 10 to 18 years. PROP‐03, who had received growth hormone replacement therapy at age 9, had an improvement in height and BMI Z scores  vs actual height at age 18 years was 166.5 cm (−1.4 Z score); midparenteral height for PROP‐02 is 161.5 cm (0 Z score) vs actual height at age 18 years was 153 cm (−1.4 Z score). For PROP‐03, midparenteral height is 162 cm (0 Z score) vs actual height age 18 years was 157.8 cm (−1.8 Z score). PROP‐04 passed away at age 10 years, and so we were not able to calculate these values.The reported parental heights for PROP‐01/‐02 father's height is 175 cm and mother's height is 162 cm; for PROP‐03/‐04, father's height is 177 cm and mother's height is 161 cm. Based on midparental height compared to patient's actual heights an estimation as shown earlier by Wright and Cheetham,Z scores showed that they were both ~0 to +1 Z scores  from healthy siblings of PROP‐01/‐02: male 16 years  and male 10 years . We observed that their WHO height‐for‐age s Figure .3.3Protein intakes, including total, intact, and protein from medical formula in g/kg/d, are presented for each patient in Figure Median percentages of intact protein vs protein from medical formula varied for each patient and for each age group Figure .The protein to energy ratio (P:E) in g/100 kcal/d was calculated for both intact vs total protein and presented for all subjects in Table 4Z scores, but at the same time had elevated weight and BMI for age Z scores. Poor growth outcomes with respect to height rather than weight in subjects with PROP have been well described,In this retrospective chart review study, we show growth data in association with nutrition therapy of four patients with PROP followed longitudinally from birth to the 18 years at the same center between 1999 and 2018. We observed that all patients had poor growth outcomes, with persistently reduced height‐for‐age Our center's nutrition therapy protocol was based on restriction of intact protein, supplementation of medical formula , and provision of high calorie intake. During the 19‐year observation period, the targets for total protein intake were informed by three consecutively published international guidelines. These guidelines showed considerable discrepancies within the 0‐12 month) age, with the Ross guidelines recommending 3 to 3.5 g protein/kg/d, and the Sass and GMDI guidelines recommending much lower amounts ranging from 1.5 to 2 g protein/kg/d. month agImbalanced amino acid intake could be another reason for reduced growth. Medical formula used in PROP is not only depleted of VAL and ILE but also at the same time is enriched in leucine (LEU). Enhanced VAL and ILE oxidation in the presence of abundant LEU, known as BCAA antagonism, makes both VAL and ILE less available for anabolism and thus could contribute to growth deficiency despite high total protein intakes.Z scores were lower compared to the expected height Z scores. Despite the reduced height, our patients had elevated weight for height/age Z scores during the first 5 years of life, as well as elevated BMI Z scores after the age of 5 years. All children had energy intakes within recommendations (80%‐120% of EER) at different ages. It is likely that subjects could have been physically inactive due to concomitant manifestations such as muscular hypotonia, neurological deficits, cardiomyopathy, and general discomfort during physical activity and accordingly would have needed less energy, as shown earlier for MMA by Hauser et al.Children heights are also attributable to parenteral height, and our patients' height data might have been influenced by this.Z score.Dietary protein and energy are interdependent, and an adequate energy intake ensures efficient protein utilization.Limitations associated with this study first and foremost include the small sample size. Although all four patients were treated at the same clinic, there was high variability in their dietary treatments. Therefore, we could not perform any statistical correlations between diet and growth outcomes. While the dietary data represented actual intakes rather than prescribed, it did not describe intakes of the BCAAs. Although the longitudinal growth data over 18 years enabled us to confidently document growth patterns, there was no information on body composition (lean body mass vs fat mass). One of the patients (PROP‐03) was found to be growth hormone deficient, which prompted the clinic to treat with growth hormones. This could have been the reason for the skewed height data in this patient's chart.5Z scores. Although total protein intakes were above recommendations, intact protein intakes were low, with abundant use of medical formula and single amino acid supplements. Optimizing dietary management in PROP, balancing medical formula relative to intact protein, and an assessment of intact P:E ratio is recommended.Despite adequate amount of total protein and energy intake meeting respective recommendations, growth outcomes in all PROP patients were below standards, especially for height The authors declare no potential conflict of interest.Haneen Saleemani collected data and participated in study design, data analysis, and data interpretations. Haneen Saleemani drafted the manuscript. Csilla Egri and Gabriella Horvath contributed to data collection and patient care and together with Sylvia Stockler‐Ipsiroglu and Rajavel Elango cosupervised study design, data analysis and interpretation, and edited the manuscript. All authors have read and approved the final version of this paper.The study was approved by the University of British Columbia and BC Children's Hospital Research Ethics Board (H19‐02912).SUPPLEMENTARY FIGURE 1 Comparison with Different Guidelines for Total Protein Intakes in patients with propionic Acidemia (PROP)Click here for additional data file.SUPPLEMENTARY FIGURE 2 PROP‐01, 02 healthy siblings: Scores as per 2021 @ age of 16y, and 10yClick here for additional data file.SUPPLEMENTARY FIGURE 3 Energy Intakes in children with Propionic Acidemia (PROP) compared to 80‐120% Recommended Dietary allowance (RDA) for energyClick here for additional data file.


For lumbar spine MRI (LMRI), an ideal performance metric should account for the global severity of lumbar degenerative disease (LSDD) which may influence reporting time (RT), thereby affecting clinical productivity. This study aims to derive a global LSDD metric and estimate its effect on RT. A 10-year archive of LMRI reports comprising 13,388 exams was reviewed. Objective reporting timestamps were used to calculate RT. A natural language processing (NLP) tool was used to extract radiologist-assigned stenosis severity using a 6-point scale  at each lumbar level. The composite severity score (CSS) was calculated as the sum of each of 18 stenosis grades. The predictive values of CSS, sex, age, radiologist identity, and referring service on RT were examined with multiple regression models. The NLP tool accurately classified LSDD in 94.8% of cases in a validation set. The CSS increased with patient age and differed between men and women. In a univariable model, CSS was a significant predictor of mean RT  and independent predictor of mean RT (p < 0.001) controlling for patient sex, patient age, service location, and interpreting radiologist. The predictive strength of CSS was stronger for the low CSS range  compared to higher CSS values . Individual radiologist study volume was negatively correlated with mean RT . The composite severity score predicts radiologist reporting efficiency in LMRI, providing a quantitative measure of case complexity which may be useful for workflow planning and performance evaluation.Conventional measures of radiologist efficiency, such as the The online version contains supplementary material available at 10.1007/s10278-021-00462-1. Efficient interpretation and reporting are central principles of delivering value in diagnostic radiology . Common To address this question, we studied degenerative disease of the lumbar spine (LSDD) on MRI (LMRI). Compared with other imaging reports, LMRI reporting is typically structured by anatomic level and each level is assigned a grade of relative severity of stenosis . Prior wWe hypothesized that a measurable relationship exists between disease severity (complexity) and RT owing to increased cognitive energy required to render a diagnosis and assign a severity value , 16. Then = 23,607) or reported on the weekend (n = 3202) were excluded because the workflow for these studies was presumably different. We also excluded studies in which the calculated RT was over 60 min (n = 3058) because this was felt to be beyond the conventional RT for an attending radiologist without significant interruption , representing 29 different MR scanners. Studies which were co-interpreted with trainee assistance  and manually reviewed the radiology reporting text to assess for discrepancy or error. For each case, the reporting text was manually reviewed and assigned a 0–5 value by a radiologist. Scores were considered concordant if NLP and manual review matched exactly and any degree of discordance was considered unsuccessful.We applied a customized NLP algorithm to raw radiology report text. Using a rule-based approach employing a dictionary of customized regular expressions (RegEx), the algorithm was designed to extract a severity rating using a 6 point scale . A severity score was extracted for spinal canal stenosis (SCS) and left and right neural foraminal stenosis  for each of six spinal levels: T12-L1 through L5-S1. The NLP was designed using empirically, iteratively developed syntactic and semantic rules including common radiology terminology and phraseology to elicit a severity score (0–5) for the 18 locations , resulting in a 6 × 3 matrix for each study  resulting in scale ranging from 0  to a theoretical maximum of 90 .The composite severity score (CSS) was calculated as the sum of the constituent severity scores  and subdivided by age and sex. Age groups were defined as < 40, 40–49, 50–59, 60–69, 70–79, and ≥ 80. The sex-based distributions and age group distributions were compared to exponential theoretical distributions using quantile–quantile plots. The Kolmogorov–Smirnov (K-S) test was used to test difference in distribution between subgroups.The distribution of CSS was assessed for the study population , interquartile range (box margins).CSS and RT were grouped by sex and age and inter-group differences were calculated using ANOVA and pairwise p value < 0.05 was considered significant. A curvilinear regression was then fitted to model predictive value of CSS on mean RT. This was repeated for subgroups of low-CSS (0–25) and high-CSS (> 25) groups. Unless otherwise stated, values are reported with ± standard error of the mean (SEM). All statistical analysis and data visualizations were performed using R statistical computing software .Simple, univariable linear least squares regression was used to assess the predictive relationship of CSS, age, sex, service location, and interpreting radiologist on RT. The distribution of residuals indicated heteroscedasticity . These differences are summarized in Table From the initial dataset of 43,255 studies, we included 13,388 after applying exclusion criteria . The NLP was 100% accurate in 5/18 level instances (27.8%) and was least accurate at right L5-S1, correctly classifying the severity in 86% of cases.R = 0.44 (p < 0.001) and a weak positive correlation between age and RT . The distribution of CSS was exponential and differed by different age group with increasing flattening of the distribution at higher age groups, indicating a shift toward higher CSS  and the relative difference in proportion by sex is shown for each CSS value in Fig. Mean CSS for the full study population was 6.04 ± 0.07. The relationship of CSS and RT to patient age is shown in Fig. CSS Fig. B. The di p < 0.00 and a weR = 0.23, p < 0.001). In a log–log linear regression model, CSS was a significant predictor of RT (adjusted R2 = 0.063) according to the function ln(RT) = 2.14 + 0.17(ln(CSS))  = 905.6, p < 0.001) meaning that for every 1% increase in CSS, we predict a 0.17% increase in RT. The predictive relationship of mean RT to CSS was better modeled using a 3rd order polynomial regression which shows an adjusted R2 = 0.38 (p < 0.001)  compared to high CSS range (> 25)   = 73.97, p < 0.001). Simple regression models also identified smaller, significant predictive value of age  = 528.4, p < 0.001), sex  = 4.47, p = 0.03), and requesting service  = 118.7, p < 0.001). In the multiple regression model incorporating all 5 predictors , each variable was a significant and independent predictor of RT (p < 0.001 for each). In this 5-variable model, the adjusted R2 was 0.52  = 67.56, p < 0.001) and the interpreting radiologist was the most important predictor (partitioned R2 = 0.43) followed by CSS (partitioned R2 = 0.043), age , and sex (partitioned R2 = 0.00024).The interpreting radiologist identity was also a statistically significant predictor for RT, accounting for a larger portion of variance than CSS (adjusted p < 0.001) and pairwise analysis showed a significant, stepwise for each sequential group except the transition from CSS 21–30 to CSS 31–40 (p = 0.74) and CSS 31–40 to CSS 41–50 (p = 0.13) . There was also a significant negative correlation between the radiologist’s study volume (number of studies interpreted by each radiologist over the 10-year period) and mean RT . Pairwise t tests between radiologist volume quintiles showed significant decrease in mean RT between the 3rd and 4th quintiles  and the 4th and 5th quintiles . There were no significant pairwise differences in the 1st–3rd quintiles (p > 0.05). The correlation between individual radiologist volume and CSS was nonsignificant (p = 0.75).Because the effect size of individual radiologist identity was large, the mean RT as a function of mean CSS was plotted for individual radiologists, excluding the 5% upper and lower tail outliers Fig. . There wles Fig. B . The relationship of CSS and RT supports the anecdotal observation that severe LSDD is more challenging to interpret than normal anatomy, measured in terms of time spent generating a final radiology report. The CSS may be useful as a ‘correction factor’, allowing studies to be weighted based on overall complexity. The CSS could then be applied to quality improvement by defining efficiency benchmarks, ensuring equitable distribution of work, and improving existing measures of radiologist productivity .R2 = 38%). Notably, the CSS performed better at low-range CSS than high-range CSS (R2 = 83% vs. 15%), suggesting a de-coupling of this relationship for LMRI studies with large burden of LSDD. In multiple regression, CSS was relatively less important than the identity of the interpreting radiologist. Taken together, these findings indicate that LSDD severity is only one of many factors which influence real-time radiologist efficiency. Nonetheless, we can extract meaningful benchmarks from this large dataset which estimates an increase in median RT of 50% for LMRI studies with a CSS of ~ 12 relative to a normal study (CSS = 0). The Current Procedural Terminology (CPT) Code for non-contrast LRMI  in the Washington, DC area, is assigned a global RVU of 6.32, of which 1.48 RVU is allocated for physician work (estimated payment ranging from $220.87 to $273.47) [The principle finding of this study is that the CSS is a reasonable predictor of mean RT  . LMRI ca$273.47) . Moreovep < 0.001), and the distribution of CSS also differed by sex. However, despite these anatomic differences, there was no sex difference in RT. It is unclear if this asymmetry is due more to sex-specific differences in natural history and disease prevalence, other unaccounted sex differences in anatomy, or potentially due to implicit bias. While the CSS is not sufficient to directly inform surgical decision-making, the utility of CSS in predicting surgical candidacy as well as its correlation with functional status could be evaluated in future work.Differences observed in CSS by sex and age group are largely consistent with prior cohort studies of LSDD in cohort populations, supporting the feasibility of the model –25. The R = 0.26, p = 0.002). There was a highly significant negative relationship between cumulative study volume and RT indicating that radiologists who read more LMRI over the 10-year period tended to report studies more efficiently. This effect was driven largely by the top quintile of radiologists, who read a disproportionately high number of studies. This may reflect experience; radiologists with higher cumulative volume have likely been in practice longer and may need less time to interpret LMRI; alternatively, radiologists who interpret studies quickly (regardless of experience) are likely to have higher volume overall. The impact of CSS on individual reader practice could be studied in future, prospective work.Lastly, while CSS was an important predictor of RT, the effect size attributable to different radiologists was tenfold higher in the multiple regression model (43% vs. 4.3%). In a population of 152 radiologists, significant variation in training, experience, and clinical role could all contribute to this effect. Still, the relationship of CSS and RT showed a significant correlation across radiologists despite substantial differences in individual practice patterns . It is plausible that the exclusion criteria for this study may introduce additional unmeasured bias in NLP accuracy. Further, we opted to use reporting text rather than the underlying radiographic images that were not referenced as a standard. Instead, our CSS presumes high fidelity translation of disease severity in the report and does not account for inter-reader variability. Nonetheless, the simplicity of the CSS provides an intuitive understanding of the interpretation process. Lastly, the generalizability of our results outside of this single academic institution is uncertain. Significant variations in reporting style, case mix, and patient demographics all likely contribute to variance in RT in other organizations and may not translate to private practice workflows. Extrinsic factors such as time of day, day of week, and more granular clinical factors such as chief complaint are all likely important predictors of RT not included in this model. Future work should strive to integrate intrinsic  and extrinsic  factors to predict RT toward a more comprehensive and equitable model of radiologist efficiency.The principal limitations of this study lie in the assumptions used to build our measurement variable (CSS) and outcome variable (RT). Our CSS model does not account for NLP model error, which, although uncommon, could bias the CSS from ground truth report text. Moreover, the NLP tool was validated on the complete dataset rather than the study population Supplementary file2 (TIFF 1339 KB)Supplementary file3 (TIFF 1339 KB)Supplementary file4 (TIFF 1339 KB)Supplementary file5 (DOCX 76 KB)Below is the link to the electronic supplementary material.


Tocilizumab was studied to reduce cytokine syndrome in patients with severe COVID‐19 pneumonia in solid organ transplant (SOT) recipients with conflicting results. We aim to study the early use of tocilizumab in SOT with COVID‐19 pneumonia on low flow oxygen.This is a retrospective cohort study that was conducted in two transplant centers in Saudi Arabia among 46 SOT with COVID‐19 comparing 21 patients who received tocilizumab to 25 patients who received standard of care. Their clinical characteristics and outcomes were described.p = .003), had higher ferritin  and C‐reactive protein (CRP) . More patients in the tocilizumab group required high flow oxygen  compared to patients on standard of care. There were no differences in mortality or mechanical ventilation requirement. Hospital stay was significantly shorter in the tocilizumab group than the standard of care group .Compared to patients who received standard of care, patients in the tocilizumab group were older  recipients with conflicting results. We aim to study the early use of tocilizumab in SOT with COVID‐19 pneumonia on low flow oxygen. ALTalanine aminotransferaseARDSacute respiratory distress syndromeASTaspartate aminotransferaseATGanti‐thymocyte globulinCOVID‐19corona virus disease 2019CRPC‐reactive proteinCRScytokine release syndromeCXRchest x‐rayESRerythrocyte sedimentation rateGGTgamma glutamyltrsansferaseHFNChigh flow nasal cannulaICUintensive care unitIL‐6interleukin 6IVIGintravenous immune globulinLDHlactate dehydrogenaseLFTliver function testPCRpolymerase chain reactionSARS‐CoV‐2sever acute respiratory syndrome coronavirus 2SDstandard deviationSOTsolid organ transplantWBCwhite blood cell1The current acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) caused the COVID‐19 pandemic.d‐dimer, and C‐reactive protein (CRP).Previous studies showed that COVID‐19 disease is associated with an increase in plasma concentrations of cytokines and inflammatory markers including elevation of ferritin, lactate dehydrogenase (LDH), A systemic review reported overall mortality of 18.6% in solid organ transplant patients infected with COVID‐19.The largest cohort of COVID 19 transplant patients who received IL‐6 blockers included 80 cases in Spain. The mortality rate was 32%. Patients who died continued to have an elevation of CRP despite receiving tocilizumab. The level of IL‐6 was not useful to assess response to tocilizumab, and it did not correlate with mortality.1.1SARS‐Cov‐2 infection, defined as presenting with a fever or any respiratory symptoms, including dry cough, and especially in those with a history of travel or exposure to infected people within two weeks before the onset of illness since January 2020. Case definitions of confirmed human infection with SARS‐Cov‐2 were in accordance with the interim guidance from the WHO.In the cases group of the current study, we included all patients with SARS‐CoV‐2 who received tocilizumab during the study period in addition to the standard of care. The control group of the current study included patients who were admitted to the hospital at the same study period but never received tocilizumab and were treated with the standard treatment only, as they were not eligible to receive tocilizumab.1.2Variables included patients' demographics, co‐morbid conditions, signs and symptoms of SARS‐CoV‐2 illness, chest radiological findings, laboratory abnormalities, microbiological testing, use of mechanical ventilation, ventilator modes and settings, interventions used to treat refractory hypoxemia, vasoactive support, medications offered to the patient and treatment outcomes  on hospital admission, during patient's ICU stay, and at hospital discharge. Information sources were medical files, electronic health information records, and laboratories reports of COVID‐19 patients.22.1This is a retrospective cohort study conducted in two transplant centers in Saudi Arabia. The study population was adults (≥18 years) COVID‐19 solid organ transplant recipients who were admitted to King Faisal Specialist Hospital and Research Center, Jeddah  and Riyadh . COVID‐19 SOT recipients who received tocilizumab were compared to COVID‐19 hospitalized SOT patients who only received standard care. Patients were followed during hospitalization until death or discharge. Diagnosis of COVID‐19 was confirmed with a positive SARS‐CoV‐2 nasopharyngeal PCR. Both treatment groups who received tocilizumab and standard of care were diagnosed and managed at the same period time between May and September 2020.We utilized the Research Electronic Data Capture (REDCap), a web‐based software tool that allows researchers to create secure online forms for data capture, management, and analysis; developed by Vanderbilt University .2.2According to the hospital guidelines that both institutions share, all COVID‐19 patients received standard of care treatment at hospital admission. Standard of care treatment in the current study included the use of both off‐label hydroxychloroquine and azathioprine or favipiravir and convalescent plasma. Later on, dexamethasone was added as part of standard care management. It also included oxygen supply to target oxygen saturation reaching at least 90%. patients received antibiotics at the discretion of the treating physician when suspecting a bacterial respiratory super‐infection. The majority of patients had their antimetabolite drugs held during admission.2 in the absence of systemic bacterial or fungal infection and increasing inflammatory parameters defined by any of the following: Ferritin > 500 μg/L or doubling within 24 h, CRP > 70 mg/L. The average dose of tocilizumab given was 4–8 mg/kg (recommend dose was 400 mg once) to be repeated after 12–24 h if the response to the first dose was inadequate (maximum of two doses with a single maximum dose of 800 mg). The decision of when to give tocilizumab was left to the discretion of the treating infectious diseases physician based on the above criteria.Patients were eligible to receive tocilizumab if they had abnormal chest imaging consistent with COVID‐19 pneumonia, rapidly worsening respiratory symptoms/signs, or any requirement of supplemental O2.3d‐Dimer, LDH, and ferritin and admission floor (isolation unit vs. ICU).Baseline clinical characteristics on admission were compared between the two groups, including information related to their transplant, type of immunosuppression, history of any recently treated rejections in the year before COVID‐19 diagnosis, symptoms on presentation, oxygen requirements, chest X‐ray findings, and pertinent laboratory markers including complete blood count, renal profile, hepatic profile, cardiac profile and inflammatory markers including erythrocyte sedimentation rate (ESR), CRP, hospital course was compared between the two groups, including the requirement for high flow oxygen, mechanical ventilation, inotropic support, renal replacement therapy, and the development of secondary infections, modification of immunosuppressive therapy, use of COVID 19 targeted therapy, and death.Inflammatory parameters  and oxygen requirements were evaluated on Days 0 and 3 post‐tocilizumab infusion.2.4χ2 or Fisher's exact test, as appropriate, were used to examine differences in categorical variables, while student t test or Mann–Whitney test, as appropriate, were used to examine differences in continuous variables. Wilcoxon signed ranks test was used to compare changes in laboratory markers and oxygen requirements between days 0 and 3 after receiving tocilizumab. All p values were two‐tailed. p < .05 was considered significant. SPSS software  was used for all statistical analyses.All categorical variables were presented as frequencies and percentages, while continuous variables were presented as means and standard deviations (SDs). Demographics and clinical characteristics on admission and disease course and outcomes were compared between the patients who received and did not receive tocilizumab. 3A total of 46 patients were included in the study; 21 patients in the tocilizumab group and 25 patients in the control group. As shown in Table d‐dimer (mg/L)  count ×109/L), 0.92 ± 0.62 absolute lymphocyte count (×109/L), 205.0 ± 69.3 platelets count (×109/L), 247.0 ± 97.8 LDH (U/L), 63.0 ± 73.0 CRP (mg/L), 627.9 ± 739.3 ferritin (μg/L), 1.07 ± 1.15 09/L, 0.9More than half of the patients had lung infiltrates on chest X‐rays. Table p ≥ .05). In the tocilizumab group, the first dose of tocilizumab was administered on average after 6.5 ± 4.4 days from diagnosis with COVID‐19. Compared to the standard of care group, tocilizumab group had older patients , more likely to receive transplant from a deceased donor , required more ICU/intermediate care admission , had higher levels of LDH (p = .019), CRP (p = .012), and ferritin (p = .025).Treatment groups who received tocilizumab and standard of care were diagnosed and managed at the same period time between May and September 2020. Data analysis showed no statistical differences between the two groups when compared over time (p = .009), and azithromycin  as COVID‐19 targeted therapy. Compared with the standard of care group, the tocilizumab group was more likely to require HFNC  and ICU stay  and to have shorter hospitalization . There were no differences between groups regarding hospital death or requirements of mechanical ventilation, renal replacement therapy, and inotropes/vasopressors use.As shown in Table Table 4Our retrospective study was conducted on patients who were not ventilated and received low flow oxygen aiming to evaluate the early use of tocilizumab in solid organ transplant patients with COVID‐19 pneumonia. The results of our study showed that there was no difference in mortality between patients who received tocilizumab versus those in the standard of care group. Patients in the tocilizumab arm had a shorter hospital stay and required more assisted ventilation than patients in the standard of care group who were not treated with tocilizumab. Besides, patients who received tocilizumab were older, had more severe disease, higher inflammatory markers, and were expected to have worse clinical outcomes than those who received standard of care. In a previous retrospective matched control study, there was no difference in mortality between solid organ transplant patients who received tocilizumab versus standard of care. Most patients in that study required high flow oxygen and were ventilated.The mortality rate in our study was comparable to another study from Saudi Arabia. In our study, 46 solid organ transplants were evaluated, 45% received tocilizumab, 19% required ventilation, and the overall mortality was 8%. In the other study, 67 solid organ transplants were evaluated, 23% received tocilizumab, 14% required ventilation, and the overall mortality rate was 4%.In our study, there was no difference in infectious complications between patients who received tocilizumab versus standard of care, and most infections were bacterial pneumonia. Patients in the current study were followed until discharge or death while hospitalized. The results showed only one episode of candidemia that was documented in the standard of care group. Similar results were described in the retrospective matched control study.d‐Dimer remained elevated. Various inflammatory parameters were studied in ICU and non‐ICU solid organ transplant recipients. Although patients in the ICU had higher CRP levels, ferritin, and d‐Dimer than non‐ICU patients, CRP was an important inflammatory parameter to monitor disease progression, unlike ferritin and d‐Dimer, which remained elevated.In our study, at Day 3 post‐tocilizumab infusion, there was a significant reduction in CRP level, but the level of ferritin and The findings of the current study should be interpreted taking into account its limitations. Our study was limited by the small number of patients, nonstandardized antiviral protocol, and retrospective nature of the study. First, retrospective observational studies cannot draw cause/effect inferences taking into full consideration its known and unknown confounders. The tocilizumab in the current study was used in patients with severe COVID‐19 infection and had evidence of a higher level of inflammation compared to the standard treatment group. Therefore, the findings should be considered preliminary taking into account the study limitations and, therefore, tocilizumab efficacy should be validated in adequately powered clinical trials with a randomized allocation of treatment. A recent randomized trial showed increased mortality related to tocilizumab when used for ventilated patients.5In conclusion, the current study findings present some assessment of tocilizumab efficacy in the management of severe COVID‐19 infection which requires mechanical ventilation support and stay in the ICU. The tocilizumab group was a sicker group of patients, and yet mortality was not increased in this group, and hospital stay was shorter. Furthermore, the overall mortality reported by these two centers was far lower than in most other case series or registries of SOT/COVID, so it is even possible that this use of tocilizumab could have contributed to these favorable outcomes. While this cannot approach the level of evidence of a randomized trial, it does suggest that there could be some benefit of tocilizumab, especially in the group of patients who are rapidly progressing and are in the early upswing of the inflammatory phase, but before full‐blown respiratory failure has occurred. As COVID‐19 infection is spreading dramatically all over the globe leading to increased inflammatory markers, irreversible lung damage, demand for ventilatory support and mechanical ventilation, and dramatic radiological manifestations, tocilizumab can be considered a therapy if other treatments are not available or fail to manage the severely ill patients with COVID‐19.The authors declare that there are no conflict of interests.Research design: Amani H. Yamani, Basem M. Alraddadi. Performance of research and data acquisition: Amani H. Yamani, Afnan A. Amer, Fatimah S. Mehdawi, Mohammed A. AL‐Hamzi, Meshari S. Aldajani. Data analysis and interoperation: Amani H. Yamani, Basem M. Alraddadi, Aiman M. Ramadan, Abeer N. Al Shukairi. Writing of paper: Amani H. Yamani, Majda S. Alattas, Abeer N. AlShukairi, Aiman M. Ramadan, Abbas Al Mutair. Critical review of paper: Amani H. Yamani Basem M. Alraddadi, Reem S. Almaghrabi, Ghassan Y. Wali, Abeer N. Al Shukairi, MD, Abbas Al Mutair.