Insufficient sleep is common among teachers and is associated with diverse health risks. This study aimed to predict intention and sleep duration by applying the Theory of Planned Behavior (TPB) and to examine the effectiveness of an implementation-intention intervention to improve sleep duration and quality.M = 36.8 years, SD = 10.4) were assigned to an active control group (CG) or intervention group (IG). At baseline, TPB variables were assessed and participants of the IG received instructions to develop implementation intentions to reach the goal of sleeping 8 h on average. During a 3-week intervention period, all participants wore an activity tracker (Fitbit Charge HR) to measure sleep duration and kept diaries to assess sleep quality, physical activity, and stress. After 1 month, a 1-week follow-up, including a booster for the IG, was conducted.Sixty-nine teachers (Forty-two percent of variance in sleep duration were explained by control variables, past behavior, perceived behavioral control, and intention. Mixed ANOVAS with baseline covariates found a large main effect with longer sleep duration in the IG. A time x group interaction suggested that only the IG slept significantly longer in the follow-up period compared to the intervention period. For sleep quality, a medium-sized main effect for study group was found and a time x group interaction indicated higher sleep quality in the IG for week 3 and the follow-up.The TPB was effective in predicting sleep intention and duration. Implementation intentions accompanied by daily monitoring and a booster appear to be promising for improving sleep.The online version contains supplementary material available at 10.1007/s12529-022-10069-7. Poor sleep has been associated with numerous negative health outcomes and impaired professional and non-professional activities –3. TeachOur study aimed to predict sleep duration among teachers using the Theory of Planned Behavior  and inThe TPB states that the most proximal determinant of an individual’s behavior is behavioral intention, which is understood as a function of three motivational variables: perceived behavioral control (PBC), subjective norm, and attitude towards the behavior. It is further assumed that PBC can determine behavior directly as well. The TPB has been successfully applied to a wide range of health-related behaviors like physical activity, nutrition behaviors, and alcohol consumption –18.However, the application of the TPB in the study of sleep behavior is surprisingly rare and focusses on student samples. A recent review  identifid = .65) on goal attainment  )Regarding our first aim, we hypothesized that the TPB variables attitude, subjective norm, and PBC predict the intention to sleep approximately 8 h per night at baseline (H1) and that intention and PBC predict sleep duration during the intervention period (H2). Regarding our second aim, we hypothesized that during the intervention period and during the follow-up, the intervention group (IG) receiving an implementation-intention intervention would exhibit longer sleep duration (H3) and higher subjective sleep quality (H4) compared to an active control group (CG).Ethical approval was obtained by the ethics commission of the Faculty of Behavioural and Cultural Studies at Heidelberg University (protocol number: AZ Schm 2018/1–1).Teachers were recruited at a private educational institute which runs several general schools and institutes at different locations in two larger German cities. The study was presented during staff meetings and via mailing lists. The following exclusion criteria were specified:  sufferinN = 69 teachers aged 24 to 70 years . Fifty were female, 54 lived in a relationship or marriage, and 14 had at least one child. On average, teachers reported working 41.0 h per week (SD = 8.4).Initially, we included 80 teachers in the study, of whom two persons decided not to further participate because of the privacy policy of Fitbit, Inc. after the first appointment. We lost data of six participants due to technical problems . Due to skin rash, two participants had to stop wearing their device and one participant moved during the survey period and did not participate in the follow-up see Fig. . The finThe study consisted of four appointments with the research manager (L.M.S.) which were held in different facilities of the educational institute  for the purpose of item generation. To elicit behavioral beliefs, the participants were asked to name advantages and disadvantages of sleeping 8 h on average. To elicit normative referents, they were asked to list all individuals or groups agreeing that they should sleep 8 h on average. To elicit relevant influencing variables, they were asked to list all factors enhancing and hampering sleeping 8 h on average. Answers were used for the construction of the final questionnaire, which included both direct and indirect measures using the salient beliefs of the pilot questionnaire. Based on Fishbein and Ajzen’s [Following Fishbein and Ajzen’s  guidelin Ajzen’s  guidelinpast sleep duration was measured using one item, namely “Please estimate how many hours you slept per night on average for the past four weeks.” Attitude was measured using ten 7-point semantic differential scale items with the prompt: “To sleep on average 8 h during the next three weeks would be…” . Subjective norm was assessed with two items . Perceived behavioral control (PBC) was assessed using two items, namely “I am sure that I will be able to sleep 8 h on average for the next three weeks” and “It is up to me if I will be able to sleep 8 h on average for the next three weeks” (Cronbach’s α = .68). Behavioral intention was measured using two items adapted from Sieverding et al. [α = .76). To calculate the mean score with possible scores from 0 to 6, intention items were aggregated using the formula ((item 1 − 1) + (item 2*6/100))/2 following previous studies [For all items, if not specifically mentioned, 7-point Likert scales ranging from 1 (“strongly disagree”) to 7 (“strongly agree) were used. As past behavior has been recognized as an important control variable in the application of TPB models , 35, pasg et al. , namely, studies , 37.Sleep duration, as the primary outcome measure, was assessed via Fitbit and transferred by participants to their daily diary. To capture the secondary outcome subjective sleep quality, two items of the Pittsburgh Sleep Diary  were used in a slightly modified version. Participants were asked to rate how well they slept subjectively and how rested they felt when waking up on a scale ranging from 0 (“very bad”) to 100 . If participants slept less than intended, they were asked to name reasons regardless of the group, while only the intervention group was subsequently asked to form a new or altered implementation intention addressing the respective reason. Further insights regarding categories and frequencies of those reasons recorded in the daily diaries are given in Electronic Supplementary Material (ESM) number of steps, copied to the diary from the Fitbit application, and the self-reported duration of moderate to intense activity (“active minutes”). To achieve a comparable effort, the CG was asked to name reasons, why, if the case, they did not engage in physical activity. Daily stress was assessed using one item from the HEI-STRESS [I-STRESS  ranging α level and power of 80%, the required sample size was 60. With an anticipated 10–15% dropout rate, for example, due to technical problems, the aim was to recruit 70 participants. This sample size would also be enough to detect medium-sized effects in linear multiple regressions . Statistical analyses were performed using SPSS version 24.0. The Kolmogorov–Smirnov tests revealed normal distribution of aggregated sleep parameters , whereas intention and PBC showed violations. Examinations of skewness and kurtosis then revealed low coefficients  that have been reported to produce robust results in Monte Carlo simulations [t-tests to examine group differences in baseline variables in order to control for possible differences in subsequent analyses. Key assumptions regarding regression analyses were met . With respect to the first hypotheses, two regression analyses on behavioral intention (H1) and sleep duration (H2) were conducted followed by relative weight analyses (RWA) which allow partitioning the explained variance among multiple predictors [F-test in ANOVAS [p’s > .05) and homogeneity of error variances and covariances was given, as assessed by Levene’s tests and Box’s tests (p’s > .05). With 69 participants completing the study and 28 nights measured, the missing rate for the total of 1932 nights was only 7% regarding sleep duration and quality. Missing information was not related to sociodemographic data or TPB variables. As missing rates were relatively low, values were not imputed [As three previous studies applying implementation intentions to improve sleep (hygiene) outcomes yielded promising results and about medium effect sizes , 30, 39,ulations . In preledictors , as oppoedictors , as welledictors , we a prn ANOVAS . Spheric imputed .adjR2cum = .51) followed by a relative weight analysis, age , gender , and subjective norm  did not significantly contribute to variance explanation, while attitudes  and PBC  were significant predictors of intention. Hence, Hypothesis 1 could be confirmed, with the TPB variables explaining 51% of variance in behavioral intention, corresponding to a large effect (f2 = 1.04).Table RW) indicating the respective amount of explained variance per predictor. Past behavior (self-reported sleep duration over the last 4 weeks) was entered in the first step. Age, gender, and daily stress were entered in the second, and PBC and intention to sleep 8 h in the third step. The overall model explained 42% of variance in sleep duration (adjR2cum), corresponding to a large effect (f2 = .72). The addition of intention and PBC at step 3 increased the variance accounted for by 8%, confirming H2. According to RWA, intention was 3.9 times (35.8/9.2) more important than PBC as a predictor of sleep duration.Sleep duration during the intervention period was positively associated with PBC, intention, and daily stress, but did not correlate with active minutes and steps per day (see Table f2 = .96). Adding study group in the 4th step significantly increased explained variance in sleep duration over and above past behavior, control variables, and TPB variables . According to RWA, intention and PBC together accounted for 30.2% and study group for 24.8% of explained variance.To address our second aim regarding the effectiveness of the intervention, we first repeated the regression analysis on overall sleep duration with the variable study group in a fourth step. We thereby aimed to explore the share of variance that can be attributed to (baseline) TPB variables vs. the effect of our intervention that should be illustrated in the assignment to our two study groups. This second model explained 49% of variance in sleep duration  =  −2.12, p < .05) and intention (t(67) =  −3.53, p < .01) among teachers assigned to the IG, our further analyses took into account those baseline differences by controlling for those variables. Study groups did not differ in any other baseline variables, i.e., .In further preliminary analyses before testing the effects of our intervention in mixed ANOVAS, we compared baseline scores of IG and CG. As M = 7.64 h, SD = .55; CG: M = 7.08 h, SD = .48; t(67) =  −4.57, p < .001; Cohen’s d = 1.10) and the follow-up  =  −5.82, p < .001; d = 1.41). In order to investigate how the intervention affected sleep duration during the course of the study, a mixed ANOVA with fitbit-measured sleep duration as dependent variable  was conducted. Baseline scores in PBC and intention were entered as covariates. Results revealed a large main effect for study group with longer sleep duration in the IG  = 14.03, p < .001, partial η2 = .18) and a medium-sized main effect for baseline scores in intention  = 6.95, p < .05, partial η2 = .10). Moreover, there was a significant interaction between time and group  = 4.41, p < .01, partial η2 = .06)  = 4.89, p < .01, partial η2 = 014), but not in the CG  = 0.51, p > .05, partial η2 = .02). Participants in the IG slept significantly longer than their counterparts in the CG during the three intervention weeks and during the follow-up , with longer sleep duration at follow-up.As assumed in Hypothesis 3, large effect sizes in fitbit-measured sleep duration were observed favoring the IG in the intervention period  = 3.02, p < .05, partial η2 = .04) with Welch tests demonstrating higher sleep quality in the IG for week 3 and the follow-up  = 4.27, p < .01, partial η2 = .06). There was a small- to medium-sized main effect for study group with higher sleep quality in IG  = 4.2, p < .05, partial η2 = .06) confirming Hypothesis 4.For sleep quality, a second mixed ANOVA with analogously aggregated scores for each intervention week and follow-up week and PBC as covariate was conducted. Results indicated a significant interaction between time and group  and fitbit-measured sleep duration (H2) among teachers. The intention to sleep 8 h on average could be explained by attitudes and PBC, whereas subjective norm did not contribute to variance explanation. With respect to fitbit-measured sleep duration, the TPB variables intention and PBC together accounted for 45% of explained variance (RW) over and above the effect of self-reported past sleep duration.η2 = .18); participants of the intervention group slept significantly longer than participants of the active control group; large effect sizes were found for the three intervention weeks and the follow-up. Moreover, we found a main effect of baseline intention scores, and an interaction of time and group indicated longer sleep duration at follow-up, which included the booster, compared to intervention week 1 only in the intervention group. For self-reported sleep quality, an interaction of time and group indicated significantly higher sleep quality in the intervention group for week 3 and the follow-up; the main effect for group was small to medium (η2 = .06).The second aim was to explore the effectiveness of an intervention based on implementation intentions on sleep duration (H3) and quality (H4). For fitbit-measured sleep duration, mixed ANOVAS revealed a main intervention effect . As several studies show that the effects of sleep duration and sleep quality on health outcomes are not simply additive, it is crucial to consider both facets where possible [Effect sizes were larger for sleep duration, which was the main target of our intervention. Participants were instructed to formulate implementation intentions that were useful to increase this main outcome parameter and did so , but some of their implementation intentions also addressed sleep hygiene behaviors that might also help to increase sleep quality . As indicated in Table possible .Our findings are partly in line with those of Mairs and Mullan  who repoR2 = .51) was comparable or even higher than in studies on student samples  [r = .44), which is in line with findings of Kor and Mullan [r = .39) and sleep quality (r = .29). Compared to other occupations, teachers report a higher effort-reward imbalance and overcommitment [Variance explanation by TPB variables regarding the intention to sleep 8 h on average 2 = .50) , 21, 25,d Mullan  that sup0 , 21, 2mmitment , which rTo the best of our knowledge, the present study is the first in the context of sleep behavior that combines the established framework of the TPB and an implementation-intention intervention with ambulatory assessment in a longitudinal design. Additional strengths include a non-student sample, a strong control condition, the inclusion of a booster for the intervention group during follow-up, a standardized intervention protocol allowing replication of the results, and the combined collection of self-report and fitbit-based data over a longer time period. According to the recommendation of the German Association for sleep research and sleep medicine , sleep dHowever, several limitations should be considered. First, randomization of single persons was not possible as participants were allocated into the two study groups in teams by workplaces. Thereby, we avoided communication between participants of the CG and the IG, but effects of external characteristics may still remain, although baseline differences were taken into account and groups did not differ in terms of past sleep behavior, demographics, stress level, and physical activity. However, threats to internal validity due to the quasi-experimental design cannot be ruled out . Second,In sum, our results support our proposition that the Theory of Planned Behavior is a useful framework for the prediction of sleep intention and duration in a non-student sample. Hence, TPB constructs should be included in further research and can be promoted through a training intervention, e.g., via self-efficacy with effects on PBC and intention. Further, our findings complement research on sleep interventions by providing ambulatory-assessed data, with an implementation-intention intervention yielding promising effects. The inclusion of a booster follow-up starting 7 weeks after the first appointment had beneficial effects within the intervention group, as the effects on both sleep duration and quality intensified during this period. However, larger sample sizes are needed to additionally test the effectiveness of a booster vs. no booster condition in a between-subjects design with respective subgroups in the intervention group. Longer follow-up periods are needed to determine whether such effects can be maintained over time, and if additional support  is needSupplementary file1 (DOCX 30 KB)Supplementary file2 (DOCX 31 KB)Below is the link to the electronic supplementary material.


Diffusion‐weighted imaging lesions (DWILs) are associated with unfavorable outcome in intracerebral hemorrhage (ICH). We proposed a novel predictive nomogram incorporating DWILs.A total of 738 patients with primary ICH in a tertiary hospital were prospectively enrolled as a training cohort. DWILs were defined as remote focal hyperintensities on DWI corresponding to low intensities on apparent diffusion coefficient images and remote from the focal hematoma. The outcome of interest was modified Rankin Scale scores of 4–6 at 90 days after onset. Multivariate logistic regression was used to construct a nomogram. Model performance was tested in the training cohort and externally validated with respect to discrimination, calibration, and clinical usefulness in another institute. Additionally, the nomogram was compared with the ICH score in terms of predictive ability.Overall, 153 (20.73%) and 23 (15.54%) patients developed an unfavorable outcome in the training and validation cohorts, respectively. The multivariate analysis revealed that age, National Institutes of Health Stroke Scale (NIHSS) score, anemia, infratentorial location, presence of DWILs, and prior ICH were associated with unfavorable outcome. Our ANAID‐ICH nomogram was constructed according to the aforementioned variables; the area under the receiver operating characteristic curve was 0.842 and 0.831 in the training and validation sets, respectively. With regard to the 90‐day outcome, the nomogram showed a significantly higher predictive value than the ICH score in both cohorts.The ANAID‐ICH nomogram comprising age, NIHSS score, anemia, infratentorial location, presence of DWILs, and prior ICH may facilitate the identification of patients at higher risk for an unfavorable outcome. Using data from 738 patients with primary ICH, this study identifies 6 predictors for 90‐day unfavorable outcomes, including age, NIHSS score, anemia, infratentorial location, presence of DWILs, and prior ICH. A novel nomogram based on these factors shows remarkable discrimination and calibration in the training and validation sets, respectively. It may be helpful for clinical decision‐making and patients surveillance.  Eligible patients who met the following criteria were recruited as the training cohort: (i) age ≥18 years; (ii) diagnosis of ICH within 7 days from symptom onset or the last time seen well; and (iii) magnetic resonance imaging (MRI) performed within 28 days after ICH. Patients with any of the following characteristics were excluded: (i) hemorrhage due to trauma, intracranial vascular malformation, neoplasm, or any other presumed cause of secondary ICH; (ii) isolated IVH; (iii) any type of surgical hematoma evacuation; (iv) poor‐quality imaging data; (v) baseline modified Rankin Scale (mRS) score of >3; or (vi) loss to follow‐up  score, and GCS score at initial presentation. Using a standard assessment form, trained physicians obtained data on the functional status prior to onset, comorbidities , and pre‐hospital use of antiplatelet and anticoagulation agents. The following laboratory parameters were retrieved from medical records: hemoglobin, platelet count, international normalized ratio (INR), fasting blood glucose, low‐density lipoprotein (LDL), and creatine. Hemoglobin, platelet count, and INR were measured on admission, whereas fasting blood glucose, LDL, and creatine were measured on the next morning after admission. Anemia was defined as hemoglobin concentration <120 g/L in women and <130 g/L in men.2.3www.itksnap.org). IVH was defined as an intraventricular hyperdense image that was not attributable to the choroid plexus or calcification and was not included in the hematoma volume.Non‐contrast computed tomography (NCCT) was performed using multidetector row scanners with 4.8‐ or 5‐mm slice thickness axial reconstruction, tube potential of 120–130 kVp, and tube current of 100–300 mA in accordance with the local NCCT protocol at each site. The ICH location was assessed on baseline NCCT scans and classified into lobar , deep , and infratentorial (involvement of the brain stem and cerebellum). Volumetric assessment was completed using a semiautomated planimetric method with the ITK‐SNAP software  = 230 mm] or a 3.0‐Tesla scanner . In the validation cohort, patients underwent MRI scans on a 1.5‐Tesla scanner , and DWI sequences were performed with the following parameters: TR = 4000 ms, TE = 102 ms, b = 0/1000 s/mm2, 5.0‐mm slice thickness, 1.5‐mm gap, and FOV = 230 mm.MRI was performed on a 1.5‐Tesla  or 3.0‐Tesla scanner  with a standardized protocol consisting of axial T1‐weighted, T2‐weighted, T2‐FLAIR, DWI, and apparent diffusion coefficient (ADC) sequences at our site. Axial DWI sequences were acquired on a 1.5‐Tesla scanner ) based on their distribution, as evaluated using the Shapiro–Wilk test. Student's t‐test or Mann–Whitney U test was used to compare the continuous variables. The inter‐rater agreement for DWILs was calculated for a sample of 30 patients using κ coefficient.Categorical variables are summarized as p < 0.1 in the univariate regression analysis and those identified in previously published articles were included in the multivariate logistic regression models to identify the independent risk factors for the 90‐day outcome. We used backward elimination procedures to arrive at a minimal model including only variables at p < 0.05. Potential multicollinearity was evaluated using variance inflation factor (VIF), and an arithmetic square root of VIF ≤2 was considered as non‐collinearity. A nomogram was formulated based on all independent predictors that predict poor outcome in the final model.To develop a nomogram with good calibration and discrimination, a model was constructed in our training set and was subsequently validated in another dataset. Variables with The discriminative ability of the nomogram was summarized by calculating the area under the receiver operating characteristic curve (AUC‐ROC). Calibration was measured using the Hosmer–Lemeshow test and a calibration plot with a bootstrap of 1000 resamples. To assess the performance of the nomogram in the external validation cohort, the established nomogram was then applied to predict the probability of an unfavorable outcome, and the AUC‐ROC, calibration curve, and Hosmer–Lemeshow test were applied. Pairwise comparison of the AUC‐ROC was conducted to compare the nomogram with the ICH score in each cohort. Categorical net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were used to determine the extent to which the predictive power of the nomogram improved than the ICH score system. Decision curve analysis (DCA) was carried out to assess the clinical usefulness of our nomogram and ICH score by quantifying the net benefit across different threshold probabilities in the training and validation cohorts.http://www.Rproject.org). The nomogram and calibration plot were constructed using the “rms” package; the NRI and IDI were calculated using the “PredictABEL” package; and the DCA was established using the “rmda” package. A two‐tailed value of p < 0.05 was considered to be indicative of statistical significance.All statistical analyses were performed using R software version 4.0.1 (3n = 132 [78.6%]) especially the frontal lobes (n = 66 [39.4%]). Of the remainder, 28 (16.7%) were deep hemispheric and 8 (4.8%) infratentorial. Most DWILs (n = 97 [57.7%]) were ipsilateral to the ICH. A total of 153 (20.73%) patients developed an unfavorable outcome at 90 days after ICH. Table Figure p < 0.1). We included these factors and infratentorial location in binary logistic regression models. Based on backward stepwise elimination, age , NIHSS score , prior ICH , anemia , infratentorial location , and presence of DWILs  were identified as independent predictors of 90‐day unfavorable outcome in adjusted model. No significant collinearity was observed for any variables included in the model.The results of the univariate and multivariate logistic analyses are presented in Table p = 0.13), indicating acceptable goodness‐of‐fit. In order to further test the developed nomogram, 149 additional patients from another institution were enrolled to serve as the validation cohort. The baseline characteristics of the training and validation cohorts are summarized in Table p = 0.15). The ANAID‐ICH nomogram showed an AUC‐ROC of 0.831  for the estimation of unfavorable outcome in the validation cohort , anemia, infratentorial location, presence of DWILs, and prior ICH Figure . Furthert Figure . Additiot Figure .p < 0.001; Figure p = 0.03; Figure p < 0.001; IDI: 0.297 [0.257–0.337], p < 0.001) and validation cohort  consisting of age, NIHSS score, anemia, infratentorial location, presence of DWILs, and prior ICH to predict the 90‐day unfavorable outcome. Our nomogram performed well in both the training and validation cohorts in terms of discrimination and calibration. Furthermore, it was more effective in predicting worse outcome than the ICH score.A growing body of evidence has shown the unfavorable effects of DWILs on morbidity and mortality after ICH. A pooled analysis of 1752 patients across four trials reported that DWILs occurred in 31.3% of patients with ICH and were independently associated with worse outcome at 90 days.p = 0.001).In our study, several established risk factors, such as age, higher baseline NIHSS score, infratentorial location, and prior ICH, remained relevant to the unfavorable outcome.By applying the aforementioned factors, we constructed a novel nomogram model to predict the post‐ICH outcome status. This model showed great predictive value, with AUC‐ROC of 0.842 and 0.831 in the training and validation sets, respectively. Moreover, we recognized the superior prognostic utility of this nomogram, as compared to that of the ICH score. The undesirable discrimination of the ICH score might be attributed to two reasons: first, the patients in our study were less severe than those enrolled to develop the ICH score,As a multifactor graphical predictive tool, the advantage of a nomogram is that it can provide an individualized estimation for the prediction of the event of interest, which is entirely based on each included factor, without averaging or combining within a category.Our study has some limitations. First, this analysis was based on tertiary institutions, and those who underwent hematoma evacuation or were unstable to tolerate MRI were not included, which might have introduced the possibility of selection bias and limited the generalizability of our observations. Second, MRI scans were not collected serially in our cohorts, which makes it possible that DWILs had reversed or would present later by the time of undergoing MRI imaging. In fact, Menon et al.In conclusion, the present study with a relatively large sample size provided further evidence that DWILs were associated with the 90‐day outcome in ICH. Based on this finding, we developed and externally validated the ANAID‐ICH nomogram to predict the probability of a 90‐day unfavorable outcome in patients with ICH. Our nomogram, which showed remarkable discrimination and calibration, would be helpful for making therapeutic decisions and conducting patient surveillance.FG and LT have full access to all data in the study and take responsibility for the integrity of the data and the accuracy of data analysis. JL and DL equally contributed to the study and were involved in data analysis and manuscript writing. JL, LT, and FG designed the study. LT and FG reviewed, edited, and approved the final version of the manuscript. FP, QK, HL, and MC collected the data.This study was funded by the National Natural Science Foundation of China (NSFC) , the Science and Technology Action Plan for Major Diseases Prevention and Control in China (2017ZX‐01S‐006S3), and the Science and Technology Department of Zhejiang Province (2022KY174).None.Table S1Click here for additional data file.


Dear Editor,Between 12 and 18% of patients with Crohn's disease (CD) have hidradenitis suppurativa (HS) and clinical and radiological differentiation between both entities when affecting the perianal area can be extremely challenging. The presence of HS is associated with an earlier onset, more aggressive CD, treatment failure, more surgical requirements and lack of response to anti TNFalfa antibodies.A 38‐year‐old man was diagnosed in 2006 of extensive ileocolonic and perianal CD and in 2008 of HS Hurley stage III affecting inguinoscrotal Figure , left, pFrom 2019 to date the patient is under treatment with apremilast 30 mg bid and dapsone with improvement of facial lesions but without significant perianal or inguinal response. In 2020, subcutaneous guselkumab (100 mg monthly) was added and after 3 months, IHS4, DLQI, and visual analogue scale (VAS) for pain have changed, respectively, from 13, 10, and 7 before treatment, to 1, 0, and 0. After 2 years of treatment no inflammatory lesions have reappeared  has been recently evaluated in a randomized trial against placebo and achieved better clinical and endoscopic outcomes at week 12.To date, only three other case reports have been published in which guselkumab is used for coexisting CD and HS, all with positive outcomes.n = 20), patients treated with apremilast achieved HS clinical response with statistical significance and fewer abscesses, nodules, itch or pain during follow‐up.Apremilast has also been used recently for treatment‐refractory HS. In a clinical trial against placebo (Our patient did not respond with apremilast and dapsone in the perianal area and groin and it was after guselkumab was added that an excellent overall control of both HS and CD could be achieved. No other reports have been published of combined treatment with guselkumab and apremilast in this setting. Although more robust evidence is lacking and preliminary trials of guselkumab in hidradenitis show only moderate benefit, our case had excellent response with this combination and could be of substantial usefulness in patients with concomitant HS and CD.Full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis: Manuel Agud de Dios. Study concept and design: Manuel Agud de Dios. Acquisition, analysis, and interpretation of data: Manuel Agud de Dios, Jorge Arroyo‐Andrés, Carmen Rubio‐Muñiz, Concepción Postigo‐Lorente. Drafting of the manuscript: Manuel Agud de Dios, Concepción Postigo‐Lorente. Critical revision of the manuscript for important intellectual content: Concepción Postigo‐Lorente. Statistical analysis: None. Obtained funding: None. Administrative, technical, or material support: Jorge Arroyo‐Andrés, Carmen Rubio‐Muñiz. Study supervision: Concepción Postigo‐Lorente.None, by any of the authors.Written consent for publication by the patient was obtained.


Viral respiratory infections are one of the main causes of hospitalization in children. Even if mortality rate is low, 2% to 3% of the hospitalized children need mechanical ventilation. Risk factors for admission to the pediatric intensive care unit (PICU) are well known, while few studies have described risk factors for invasive ventilator support and prolonged hospitalization.A retrospective study including all patients aged between 2 and 18 months with a confirmed viral respiratory infection, requiring admission to PICU from September to March between 2015 and 2019, was conducted at Bambino Gesù Children's Hospital in Rome, Italy.p = 0.02); moreover, comorbidities were present in 43.8% of intubated patients and in 24.8% of patients treated with non‐invasive ventilation (p = 0.006). Viral coinfection did not result to be a risk factor for mechanical support, while virus–bacteria coinfection was significantly associated with mechanical ventilation (p < 0.001). Similar risk factors were identified for prolonged hospitalization.One hundred ninety patients were enrolled, with a median age of 2.7 months; 32.1% had at least one comorbidity, mainly prematurity. The most frequent isolated viruses were RSV‐B, rhinovirus, and RSV‐A; 38.4% needed mechanical ventilation. This subgroup of patients had lower median birth weight compared with patients not requiring mechanical ventilation (2800 g vs. 3180 g, Early identification of patients who could have a sudden respiratory deterioration and need of mechanical ventilation is crucial to reduce complications due to orotracheal intubation and prolonged hospitalization in PICU. Further studies are needed to define high‐risk group of patients and to design targeted interventions. The secondary aim focused on the identification of predictors of invasive ventilation support, prolonged ICU stay, and prolonged overall hospitalization in order to improve knowledge and the management skills of this subset of patients.22.1This study was conducted at Bambino Gesù Children's Hospital , a 607‐bed tertiary care academic hospital in the Lazio Region, Italy. OPBG had one NICU and four PICUs; data were collected from two of the four PICUs with about 612 annual admissions on average over the study period; the average length of ICUs hospitalization was equal to 7.4 days.2.2We conducted a retrospective descriptive study including all patients aged between 2 and 18 months with a confirmed viral infection, identified on a respiratory sample , requiring admission to PICUs from September to March between 2015 and 2019.Patients requiring respiratory support for underlying diseases not related to the viral infection or those receiving antiviral therapy and/or intravenous immunoglobulin therapy (IVIG) within 48 h before symptoms onset were excluded.2.3Data were collected by physicians from patients' medical charts. Information collected for each patient included age at admission, gender, gestational age, birth weight, current weight, flu vaccination, previous VRIs, comorbidities, onset of respiratory symptoms, length of hospitalization, length of ICU hospitalization, administration of low flow oxygen or high‐flow oxygen through nasal cannula (HFNC) before ICU admission, type of respiratory support received in ICU, and antiviral and antibiotic therapies. Microbiological results were reviewed for all the study population.We defined patients experienced a “clinical cure” if both of the following criteria were met: (1) no signs or significantly reduced signs of respiratory distress and (2) no need of ventilator support or significantly reduced need of ventilation support after ICU discharge.Death, ICU readmission within 48 h after discharge, or need for tracheostomy were considered as “clinical failure.”All the collected data were uploaded on Research Electronic Data Capture (REDCap) database which is a secure web application for building and managing online surveys and databases, available at no charge to not‐for‐profit institutions.2.4Patients were described according to demographic and clinical factors. Collected data were presented as count and proportions  or median and interquartile range . Categorical data were compared using Chi‐squared test or the Fisher's exact test, as appropriate. Continuous data were compared through the Wilcoxon rank‐sum test.A univariate analysis was conducted to identify risk factors related to need of invasive mechanical support, the overall duration of hospitalization, and the length of ICU hospitalization. Duration of mechanical ventilation, length of ICU admission, and length of the overall hospitalization were categorized on the median values. Sex, age, weight at birth, presence of at least one comorbidity, type of comorbidity virus coinfection, and bacterial isolation were tested as risk factors for duration of mechanical ventilation, length of PICU admission, and overall length of hospitalization.All statistical analyses were conducted using STATA 13 .3During the study period, 190 patients required PICUs admission for VRIs. Patients were more commonly female , with a median age of 2.7 months (IQR: 1.5–7.9) and a median weight of 5000 g (IQR: 4040–7000) at the time of hospital admission (Table Sixty‐one patients (32.1%) had at least one comorbidity, and 34 of them (55.7%) had more than one comorbidity. The most frequently comorbidities were perinatal diseases , cardiovascular diseases , and respiratory diseases .Thirty‐one (16.3%) had already experienced at least another previous episode of respiratory infection. None patient had received influenza vaccination. The median duration of symptoms before hospital admission was 3 days (IQR: 2–5 days).Most patients were admitted to the PICU directly from the Emergency Department , whereas 42.6% of cases n = 81) were transferred to the PICU from a pediatric ward and 12.6% (n = 24) were referred from another hospital. Median length of hospitalization in PICU was 8 days (IQR: 5–15), whereas the overall median length of hospitalization was 14 days (IQR: 10–26) . Overall, the most frequent isolated virus was RSV‐B , followed by Rhinovirus , RSV‐A , bocavirus , adenovirus , coronavirus , metapneumovirus , influenza A , parainfluenza , influenza B , and enterovirus . Figure Of the 190 patients, 34.7% of patients had a respiratory coinfection with two or more viruses; in particular, two viruses were identified in 58 patients (30.5%), three viruses in six patients (3.2%), and four viruses in two patients (1%). The most common viral coinfections were RSV‐A or B plus rhinovirus  and RSV‐A plus coronavirus .Haemophilus influenzae was the main coinfecting pathogen , followed by Klebsiella pneumoniae  and Staphylococcus aureus .A bacterial coinfection was identified in the respiratory tract of 15.3% of patients (n = 29); 3.2Thirty‐two patients (16.8%) received oxygen therapy in the pediatric ward before they were transferred to PICU; the median duration of oxygen therapy was equal to 12 h (IQR 6–24).In PICU, Helmet CPAP was used in 82.6% (n = 157) of patients with a mean duration of 72 h (IQR: 36–120); among them, 54 patients (34.4%) needed an upgrade to mechanical ventilation due to worsening of the respiratory distress and ventilator parameters. Conversely, 10% (n = 19) of patients were directly intubated at the time of admission in PICU. Median duration of the mechanical ventilation in the cohort of patients that were intubated was 216 h (IQR range: 144–372). The remaining 14 patients received oxygen therapy through high‐flow nasal cannula (HFNC).3.3p = 0.6). However, intubated patients received more frequently a combined antibiotic treatment (p < 0.001).Overall, 264 antibiotic prescriptions were issued to 172 patients (90.5%) with a mean of 1.5 antibiotic molecules per patient; 75 patients  received more than one antibiotic molecules. Clarithromycin was the most prescribed antibiotic , followed by ceftriaxone  and amoxicillin/clavulanic acid . Antibiotics were equally prescribed in the group of patients that needed mechanical ventilation and in the group of patients treated with non‐invasive ventilation ; seven (20.6%) patients received oseltamivir for influenza infection, and 27 (79.4%) patients received ribavirin for RSV infection with a median length of therapy of 8 days (IQR: 4–15). Out of these 27 patients, 19 (70.4%) received ribavirin in association with IVIG .3.4p = 0.004).The majority of our study population  had a complete clinical cure. Five patients (2.6%) experienced “clinical failure”: two patients (1.0%) died, one was affected by CHD, while the other had no known comorbidities. Moreover, two patients (1.0%) needed a tracheostomy for prolonged mechanical ventilation and 1 (0.5%) needed a readmission to ICU. All the patients with a “clinical failure” had received mechanical ventilation (3.5p = 0.02). Comorbidities were present in 43.8% (n = 32) of intubated patients and in 24.8% (n = 29) of patients treated with non‐invasive ventilation (p = 0.006). No differences in the type of comorbidity were found between the two groups, as well as in the viral distribution. Viral coinfection did not result to be a risk factor for mechanical support. Conversely, virus–bacteria coinfection was significantly associated with mechanical ventilation (p < 0.001).As shown in Table p < 0.001).Ventilated patients had a longer length of both overall hospitalization  and PICU admission  compared with patients receiving non‐invasive ventilation  with respect to those with shorter need of mechanical ventilation .3.6p = 0.007). Forty patients (40.0%) experiencing longer hospitalization had at least one comorbidity with respect to those with shorter hospitalization , with a higher frequency of patients with congenital disorders . Need for mechanical ventilation, virus coinfection, and bacterial isolation emerged to be risk factors for longer length of hospitalization (p ≤ 0.03).Patients with longer hospitalization had a lower median weight compared with those with shorter length of hospitalization .p = 0.02) between patients who required mechanical ventilation and patients who did not. In line with our findings, Papoff et al in 2011 found that infants with severe bronchiolitis had a median birth weight of 2.8 ± 0.4 kg, significantly lower than infants with mild‐to‐moderate forms not requiring ventilator support.et al, the adverse environment in utero, which affects the weight gain of the fetus, plays a role also in the reduced growth of the airways, predisposing to bronchiolar obstruction during viral infections.In our cohort, the median birth weight was 3080 g (range 2600–3470 g), with a significant difference . Even if patients with at least a comorbidity had a significantly higher need of mechanical respiratory support, neither prematurity nor CHD were statistically associated with need for mechanical ventilation, probably due to the small size of the sample. These findings confirms data from previous studies; Mecklin Overall, RSV was the most frequently detected pathogen, identified in the respiratory tract of 48% of our patients. According to the literature, most children have been infected with RSV at least once by 2 years of age and present a self‐limited course of disease; however, approximately 2% to 3% of infants younger than 12 months are hospitalized with RSV infection each year in the United States.Interestingly, the peak period for PICU admission due to VRI occurred between December and January in each season; this reflects the frequency of RSV, which generally peaks in Europe in the early winter season.et al observed a similar duration of respiratory support and PICU hospitalization in patients infected with a single virus compared with patients infected with more than one virus.Detection of viral coinfections occurred in 34.7% of cases in our cohort; the identification of multiple viruses in the respiratory tract did not result to be a risk factor for orotracheal intubation but seemed to be related to a longer hospitalization. Rates of viral coinfection reported in other studies range from 6% to more than 30%, but the correlation with greater disease severity and longer length of ICU staying is unclear to date. Coleman p < 0.001). This prevalence was lower than previously shown in other studies that reported bacterial coinfections in 26% to 45% of children with bronchiolitis admitted to PICU.H. influenzae was the most frequent isolated bacterium.In our study, bacterial coinfections were identified in 15.3% of children from cultural exams of the tracheal aspirates (TA) and broncho‐alveolar lavages (BAL); we demonstrated that viral–bacterial coinfection is a risk factor for mechanical ventilation (Despite the low rate of bacterial coinfection, antibiotics were prescribed in 90.5% of our patients, with 43.6% of patients receiving more than one antibiotic. Clarithromycin was the most common used antibiotic, followed by ceftriaxone and amoxicillin/clavulanate. The overprescription of antibiotics is probably due to the severity of the patients and to the difficulty to distinguish between a viral or bacterial respiratory infection before microbiological results are available. Bacterial pneumonia is usually diagnosed by a combination of clinical signs and symptoms, laboratory markers, and chest radiography; 53.7% of patients in our cohort had elevated values of inflammatory markers at admission, and in 59.7% of cases, chest X‐ray showed parenchymal opacities, thus making it difficult to exclude a bacterial etiology. However, the misuse and abuse of antibiotics is a challenging problem in Italy and all over the world thus antibiotic stewardship programs should aim to identify feasible targets to monitor and modify the prescription patterns in these settings.2/FiO2 ratio already after the first hour of treatment.Antivirals were prescribed in the 17.8% of patients. In the last few years, updated guidelines on the management of bronchiolitis have been published, but none focused on critically ill infants. The American Academy of Pediatrics guidelines recommend only supportive therapy, such as oxygen therapy for hypoxemia, respiratory support, and maintenance of hydration.et al, but the author concluded that ribavirin did not play any role in the pathogenesis of the myopathy in that patient.More recently, the administration of heated and humidified oxygen with HFNC has been shown to play an important role in reducing respiratory work, improving gas exchanges and avoiding endotracheal intubation.et al reported that mortality rates have fallen in the last three decades from 21.5 to 1.8 per 100.000 children below 12 months, reflecting improvements in pediatric intensive care.Severe bronchiolitis requiring admission to PICU is frequently associated with morbidity; however, the mortality rate is low. In 2005, Panickar In conclusion, demographic and clinical variables can help to identify children affected by VRI at risk for worse outcome. Further studies are needed to design targeted interventions on high‐risk patient groups to reduce the risk of complications related to orotracheal intubation and prolonged hospitalization in intensive care settings.The authors declare that they have no competing interests.Maia De Luca: Conceptualization; data curation; methodology; project administration. Carmen D'Amore: Conceptualization; formal analysis; methodology. Lorenza Romani: Conceptualization; investigation. Costanza Tripiciano: Data curation. Vitangelo Clemente: Data curation. Stefania Mercadante: Data curation. Daniela Perrotta: Conceptualization; investigation; supervision. Joseph Nunziata: Conceptualization; investigation; supervision. Corrado Cecchetti: Conceptualization; investigation; supervision. Emanuele Rossetti: Conceptualization; investigation; supervision. Roberto Bianchi: Conceptualization; investigation; supervision. Carlo Federico Perno: Conceptualization; data curation; supervision. Paola Bernaschi: Conceptualization; data curation; supervision. Cristina Russo: Conceptualization; data curation; supervision. Massimiliano Raponi: Conceptualization; supervision. Laura Lancella: Conceptualization; supervision. Marta Luisa Ciofi degli Atti: Conceptualization; supervision.


Mycoplasma pneumoniae (MRMP) has become prevalent in children. This study investigated the clinical and laboratory variables of MRMP and macrolide‐sensitive M. pneumoniae (MSMP) and identified factors associated with prolonged hospital admission in children.Macrolide‐resistant M. pneumoniae polymerase chain reaction assay.A prospective multicenter study was conducted in 1063 children <18 years old in July 2018–June 2020. The 454 had a positive n = 285) as MRMP pneumonia requiring admission and MSMP* (n = 72) as MSMP pneumonia requiring admission. Patients with MRMP pneumonia were older, more likely to have segmental/lobar pneumonia, and had more febrile days than those with MSMP pneumonia. C‐reactive protein (CRP), lactate dehydrogenase (LDH), and percentage neutrophils were more strongly associated with MRMP* than MSMP* groups. Percentage neutrophils, CRP, and alanine aminotransferase significantly changed between admission and follow‐up measurements in patients with MRMP* (P < 0.05). The duration of admission positively correlated with the number of febrile days after initiation of antibiotic medication and laboratory variables  (P < 0.05). Random forest analysis indicated that the number of febrile days after initiation of antibiotic medication, AST, and percentage neutrophils at admission was over five.Most subjects had MRMP (78.4%), and all mutated strains had the A2063G transition. We defined MRMP*  has become prevalent in children.Macrolide‐resistant M. pneumoniae pneumonia with a higher number of febrile days after initiation of antibiotic medication, AST, and percentage neutrophils at admission were more likely to have prolonged admission duration.Children with  ALTalanine aminotransferaseASTaspartate aminotransferaseCAPcommunity‐acquired pneumoniaCRPC‐reactive proteinLDHlactate dehydrogenaseM. pneumoniaeMycoplasma pneumoniaeMRMPM. pneumoniaemacrolide‐resistant MRMP*M. pneumoniae requiring admissionmacrolide‐resistant MSMPM. pneumoniaemacrolide‐susceptible MSMP*M. pneumoniae requiring admissionmacrolide‐susceptible ORodds ratioPCRpolymerase chain reactionWBCwhite blood cell1Mycoplasma pneumoniae (M. pneumoniae) is a significant cause of community‐acquired pneumonia (CAP) in children and adults.M. pneumoniae infection is usually benign, has mild symptoms, and often causes mild to moderate pneumonia. However, it can be associated with more severe and life‐threatening diseases and a wide array of extrapulmonary manifestations, which occur in approximately 20%–25% of infected children.M. pneumoniae pneumonia reported in 2010–2013, an increasing number of patients with M. pneumoniae infection were admitted to the intensive care unit.M. pneumoniae‐related pneumonia occur annually, resulting in approximately 100 000 hospitalizations in the United States.M. pneumoniae (MRMP) pneumonia has recently become prevalent in these areas, China, Japan, and South Korea.M. pneumoniae (MSMP) pneumonia.Macrolides are used as the first‐line antibiotics in children, but macrolide‐resistant M. pneumoniae and other respiratory pathogens are common, but it is not well‐known whether coinfection is related to the severity of illness.However, there has been controversy regarding differences in clinical characteristics, including hospital admission and laboratory markers, between MRMP and MSMP pneumonia. One studyM. pneumoniae pneumonia. In addition, previous studies of pediatric populations were limited by being performed at a single center, being based in one region, or the incomplete or lack of analysis of prolonged length of hospitalization. Thus, the present study aimed to evaluate children's clinical and laboratory variables with MRMP or MSMP pneumonia and identify factors associated with prolonged hospital stays from 31 centers across six provinces. Also, we investigated the importance of these different factors and laboratory variables, including respiratory virus coinfection, in children with M. pneumoniae pneumonia.The complicated relationship between hospital admission and these different factors and their relative importance remains unclear in children with 22.1A prospective, multicenter study was conducted on children younger than 18 between July 2018 and June 2020. A cooperative hospital monitoring network was established across 31 secondary and tertiary hospitals in Korea. Hospital specialists diagnosed 1063 children with CAP during the study period. The diagnosis of pneumonia was based on both physical examination and radiologic assessments performed in each hospital by respiratory and allergy specialists.M. pneumonia results from a polymerase chain reaction (PCR) assay. The children with a history of antibiotic use within 5 days were excluded from the present study. In the present study, we defined MRMP as a positive PCR result with mutations at residues 2063 and 2064 (n = 356) and MSMP as a positive PCR result with no mutations at residues 2063 and 2064 (n = 98)  as MRMP infection requiring admission and MSMP* (n = 72) as MSMP infection requiring admission . Also, we evaluated the laboratory variables of children, such as the total white blood cell (WBC) count, percentage of neutrophils, C‐reactive protein (CRP), and virus co‐infection. All blood samples were obtained at the time of admission.2.3M. pneumoniae DNA were stored at −70°C before testing. According to the manufacturer's instructions, M. pneumoniae was detected using PCR within 48 h and confirmed using the AllplexTMPneumoBacter Assay . Nucleic acid was extracted from 1 ml of the sample and purified. The cyclic temperature settings were 95°C for 2 min, 60°C for 1 min, and 72°C for 2 min and amplified for 35 cycles at 72°C for 7 min.Samples were obtained from the sputum, bronchoalveolar lavage, nasopharyngeal aspiration, or nasopharyngeal swabs within 24 h after enrollment of hospital admission or outpatient clinic visit. All specimens containing Samples that were positive following PCR amplification were screened for point mutations in domain V of 23S rRNA associated with macrolide resistance. The primers targeted residues 1949–1968 (forward) and 2148–2167 (reverse) in domain V of 23S rRNA (Myco23S‐V‐F AGT CGG GTA AAT TCC GTC CC and Myco23S‐V‐R CGC ATC AAC AAG TCC TAG CG). The PCR products were analyzed using the BigDye® Terminator v3.1 Cycle Sequencing Kit and an ABI 3500 XL series .2.4For multiplex RT‐PCR, viral genomic RNA and DNA were extracted from a total volume of 1 μl of the sample using the guanidinium thiocyanate extraction method. The lysis buffer included 500 molecules of the cloned amplified product used as an internal control in each reaction tube and excluded false‐negative results. Three independent multiplex reverse transcription nested RT‐PCR assays able to detect 1–10 copies of viral genomes were performed. A nested RT‐PCR was performed using a specific primer for PIV , ADV, HEV, and HMPV; another nested RT‐PCR was prepared with specific primers for HRV , CoV  and BoV  and a third nested RT‐PCR was performed using specific primers for RSV  and IFV  using the AllplexTM Respiratory Panel .2.5M. pneumoniae pneumonia in previous studies We analyzed specific items that had significant associations with 2.6The study protocol was approved by the Institutional Review Board and Ethics Committee of Chungnam National University Hospital (IRB No 2019‐07‐037) and all participating medical centers. Written informed consent was obtained from the parents or guardians of all participants following a detailed explanation of the study.2.7Statistical analysis was performed using IBM SPSS Statistics  and R version 2.8.134 . The experimental results are presented as the mean ± standard deviation, whereas categorical data are reported as numbers (percentages). Inter‐group comparisons were performed using the Mann–Whitney test or Kruskal–Wallis test for continuous variables and the chi‐squared test or Fisher's exact test for categorical variables.P‐value to adjust for multiplicity. Statistical significance was defined as a P‐value below 0.05.Random forest analyses were performed using R, and a variable importance ranking of each random forest was created. The mean decreases accuracy (MDA) and Gini were calculated to model accuracy by permuting each feature's value and measuring each variable's importance in estimating the target variable. To ease interpretation of the graphical displays, a suitable ordering of the components was identified using seriation.33.1M. pneumoniae PCR results is summarized in Table n = 454 [100%], male/female = 221/233, mean age = 6.89 ± 3.77 years) were divided into two groups: MRMP  and MSSP . All isolates in the MRMP group (n = 356) had A2063G point mutations.The clinical course of the 454 patients with positive P < 0.05). The proportion of patients who had segmental or lobar pneumonia or received tetracycline or quinolone treatment was higher in the MRMP group (P < 0.05)  Table .3.2M. pneumoniae PCR results upon hospital admission are summarized in Tables n = 285) and MSMP* (n = 72) groups   Table .P < 0.05) (Tables P < 0.05). However, there was no difference in hospital admission days between the two groups . However, there was no difference in those treated with a macrolide alone, tetracycline, or a quinolone . The relative proportion of neutrophils was higher in the MRMP* pneumonia group (P < 0.001). The relative proportion of respiratory virus coinfection and adenovirus infection was higher in the MSMP* pneumonia group (P < 0.05). However, the WBC count at admission did not differ between the two groups (Table The median CRP and lactate dehydrogenase (LDH) levels in 357 children with n = 145; MSMP*, n = 27) were followed‐up on the day of hospitalization with ≥48 h of fever. On initial and follow‐up tests, laboratory variables, including percentage neutrophils, CRP, LDH, and alanine aminotransferase (ALT), were significantly changed in the MRMP* group (P < 0.05). However, no significant differences were observed between admission and before discharge in any of the laboratory variables in MSMP*, except WBC count (P < 0.05) . However, the length of hospitalization was negatively associated with macrolide treatment alone (P < 0.05) but not with tetracycline or quinolone treatment (P > 0.05).Our analysis indicated that a more prolonged length of hospitalization was associated with multiple factors Figure . In part3.5M. pneumoniae  and radiological characteristics upon admission between the two groups of children with MRMP and MSMP pneumonia have clinical and radiological differences, such as fever duration and lobar pneumonia incidence,M. pneumoniae in children.Recent studies have attempted to identify laboratory variables for predicting MRMP. According to previous studies,M. pneumoniae and other respiratory pathogens is common.Previous studies have demonstrated that MRMP pneumonia may have a prolonged length of hospitalization relative to MSMP pneumoniaeM. pneumoniae pnemonia. To the best of our knowledge, this study is the first study of children infected with M. pneumoniae to rank the relative importance of different factors on a prolonged length of hospitalization.One interesting result from this study is that a higher number of febrile days after initiation of antibiotic medication, AST level, and percentage neutrophils were the most critical factors contributing to a prolonged length of hospitalization, of which a number of febrile days after initiation of antibiotic medication was the most potent factor. Furthermore, a macrolide treatment alone, previous history of admission for respiratory disease, and MRMP were influential factors causing the longer length of hospitalization in children with M. pneumoniae.This study has a few limitations. First, the investigation period was from July 2018 to June 2020; the number of samples decreased dramatically from February 2020, the beginning of the COVID19 epidemic. Second, there were some regional differences in the number of participants among the six provinces within Korea had. Nevertheless, the strengths of the present study are that we analyzed data from 454 children following a medical review, including radiological findings and blood sampling data, from within Korea over 24 months. Unlike previous studies on this topic, we were able to rank the relative importance of different factors on a prolonged length of hospitalization in children with M. pneumoniae pneumonia with a higher number of febrile days after initiation of antibiotic medication, AST levels, and percentage neutrophils were more likely to have prolonged length of hospitalization. Certain factors such as macrolide treatment alone and the MRMP group also affected the probability of extended length of hospitalization. Strategies to predict the length of hospitalization in children with M. pneumoniae pneumonia should focus on the factors identified here.Our study indicated that children with We declare that there are no real or perceived conflicts of interest to declare related with this submission and that we have no links with industry.This study was approved by the Institutional Review Board and Ethics Committee of Chungnam National University Hospital (IRB No 2019‐07‐037). The parents of all study participants gave written informed consent before study enrollment. All methods were performed in accordance with the relevant guidelines and regulations.MS was involved with data curation. MS, EHJ, and JYS were involved with writing the original draft. And writing—review and editing. All authors contributed equally to this manuscript and were responsible for data analysis and interpretation. All authors drafted the manuscript, provided critical revisions, and approved the final version of the manuscript for submission.


In addition, the rate of complications such as acute kidney injury and need for supplemental oxygen was significantly higher in the TIT group compared to the TDT group. In the multivariable logistic regression analysis, age and history of heart or kidney disease were all found to be independent risk factors for increased in‐hospital, all‐cause mortality, whereas the presence of thalassaemia (either TDT or TIT) was found to be independently associated with reduced all‐cause mortality. The presence of thalassaemia in COVID‐19 patients was independently associated with lower in‐hospital, all‐cause mortality and few in‐hospital complications in our study. The pathophysiology of this is unclear and needs to be studied in vitro and in animal models.Although numerous patient‐specific co‐factors have been shown to be associated with worse outcomes in COVID‐19, the prognostic value of thalassaemic syndromes in COVID‐19 patients remains poorly understood. We studied the outcomes of 137 COVID‐19 patients with a history of transfusion‐dependent thalassaemia (TDT) and transfusion independent thalassaemia (TIT) extracted from a large international cohort and compared them with the outcomes from a matched cohort of COVID‐19 patients with no history of thalassaemia. The mean age of thalassaemia patients included in our study was 41 ± 16 years . Almost 81% of these patients suffered from TDT requiring blood transfusions on a regular basis. 38.7% of patients were blood group O. Cardiac iron overload was documented in 6.8% of study patients, whereas liver iron overload was documented in 35% of study patients. 40% of thalassaemia patients had a history of splenectomy. 27.7% of study patients required hospitalization due to COVID‐19 infection. Amongst the hospitalized patients, one patient died (0.7%) and one patient required intubation. Continuous positive airway pressure (CPAP) was required in almost 5% of study patients. After adjustment for age‐, sex‐ and other known risk factors , the rate of in‐hospital complications  and all‐cause mortality was significantly lower in the thalassaemia group compared to the matched cohort with no history of thalassaemia. Amongst thalassaemia patients in general, the TIT group exhibited a higher rate of hospitalization compared to the TDT group ( This may be due to splenectomy, adrenal insufficiency, stem cell transplantation and/or stress erythropoiesis.In this present study, we described the clinical characteristics and outcomes of a cohort of COVID‐19 patients suffering from concomitant thalassaemia using data from a large, international observational study and compared them to a matched cohort with no history of thalassaemia.22.1www. HopeProjectMD.com, http://www.site‐italia.org/2020/covid‐19.php). We analysed all 137 thalassaemia patients identified from the beginning of March to the 30th of November 2020. No patients were excluded. The study was approved by the Ethics Committees in all of the centres involved .Data from the HOPE‐COVID‐19  and from the observational study from the ‘Società Italiana per le Talassemie ed Emoglobinopatie’ (SITE), EMO AER COVID‐19  were extracted and designed as a retrospective registry without any financial support. An online database was built and completed by each participating centre , as well as the type of respiratory support needed , were also evaluated in the outcome analysis.2.4Epidemiological, clinical and outcome data were collected from the electronic medical records. The confidentiality of patient data was protected by storing them in an anonymous manner on a locked, password‐protected computer and/or website. Blood test results, biochemical tests and radiology images were also extracted from the electronic medical records. Comorbidities such as dyslipidemia, diabetes mellitus, obesity, current smoking, kidney disease, lung disease, heart disease, splenectomy and stem cell transplantation were recorded at the time of admission. Data regarding disease management and treatment were also extracted and included the use of different drugs and/or respiratory support. We first identified all COVID‐19 patients with a history of thalassaemia. Then, we compared this cohort with a COVID‐19 cohort matched according to age, gender, diabetes, kidney disease, cardiac disease and pulmonary disease with no history of thalassaemia. For the Cox regression analysis, the entire COVID‐19 cohort consisting of 6109 COVID‐19 patients recruited from the participating centres was included.2.5t test and the Mann‐Whitney U test, respectively. Categorical variables were compared with a Chi‐squared test or Fisher's exact test. A p value less than 0.05 was considered as statistically significant. We performed propensity score matching  was performed in order to adjust for the different clinical profiles observed and obtain balanced pairs. The following variables were included age, gender, diabetes, heart disease and pulmonary disease. Statistical analysis was performed using SPSS statistics v25.0 . Cox logistic regression analysis was performed in order to identify possible independent risk factors. Factors with a p‐value <0.05 were included in a multivariable logistic regression analysis.Continuous variables with a normal distribution are shown as mean ± standard deviation, continuous variables with a non‐normal distribution are shown as a median (interquartile range) and categorical variable are shown as a frequency (%). The Kolmogorov‐Smirnov test was used to assess normal distribution. Continuous variables with normal and non‐normal distributions were compared using a Student's 33.1p < 0.001) and hospitalized more often (p = 0.001). Symptoms such as dyspnoea, fatigue, fever and cough were significantly more prevalent in the TIT group compared to TDT group. In‐hospital complications were similar between groups except with regard to the need for supplemental oxygen and acute kidney injury, both of which were more common in the TIT group.The mean age of thalassaemia patients was 41 ± 16 years . Almost 81% of these patients were found to require blood transfusions on a regular basis. More than one third of patients (53 patients/38.7%) had the blood group O. Cardiac iron overload was documented in only 6.8% of patients, whereas liver iron overload was documented in 35% of patients. 10.2% of patients had a history of diabetes mellitus, 8.1% obesity, 8.8% pulmonary disease, 4.1% pulmonary hypertension and 19.7% cardiac disease. With regard to thalassaemia treatments, splenectomy was documented in one 40% of patients, hydroxyurea therapy in 5.9% and chelation therapy in 117 patients. The baseline characteristics of the study patients are listed in Table 3.227.7% of the thalassaemia patients were hospitalized due to COVID‐19 and 18% of patients required admission to an intensive care unit (ICU). The main presenting symptoms were as follows: dyspnoea, rhinorrhoea, fatigue, anosmia or dysgeusia, pain, headache, sore throat and/or diarrhoea. Fever was documented in almost 51% of patients. 65% of patients exhibited changes in the thoracic CT imaging. 8,1% of patients received hydroxychloroquine, 6,7% lopinavir or ritonavir and 10% glucocorticoids. Antibiotics were prescribed to 17.6% of patients. Blood transfusions were required in 5.5% of cases. Non‐invasive respiratory support with continuous positive airway pressure (CPAP) therapy was required in 5.2% of cases and invasive mechanical ventilation in 0.7%. In general, supplemental oxygen support was required in 10% of patients. Overall, only one study patient (0.7%) died. Complications, treatment regimens and outcomes are listed in Table 3.3p < 0.001. In general, COVID‐19 symptoms including dyspnoea, fatigue, sore throat, fever, cough and diarrhoea were significantly less prevalent in patients with a history of thalassaemia compared to those with no history of thalassaemia. Complications including anaemia at admission (4.6% versus 23.6%), acute kidney injury (3.1% vs. 18.1%), need for supplemental oxygen support (9.4% vs. 74.8%), CPAP therapy (5.5% vs. 22.8%) and death (0% vs. 25%) were significantly less prevalent in thalassaemia patients compared to the patients with no history of thalassaemia. Consequently, the use of various drug treatments such as hydroxychloroquine, hydroxychloroquine plus lopinavir or ritonavir, hydroxychloroquine plus tocilizumab plus lopinavir/ritonavir, glucocorticoid therapy and antibiotics were used at significantly lower rates in thalassaemia patients.In the propensity score matching, 127 COVID‐19 patients with concomitant thalassaemia were matched to 127 patients suffering from COVID‐19 with no history of thalassaemia. Patients with no history of thalassaemia exhibited significantly higher rates of hospitalization than those with a history of thalassaemia as seen in Table 3.4p < 0.01), heart disease  and kidney disease  were revealed as independent predictors of increased in‐hospital, all‐cause mortality, whereas a history of thalassaemia  was revealed as an independent predictor of reduced all‐cause mortality compared to patients with no history of thalassaemia.We matched the thalassaemia patients with COVID‐19 patients in absence of thalassaemia to assess for predictors of in‐hospital, all‐cause mortality. In the univariate analysis, male gender, age, thalassaemia, pulmonary disease, cardiac disease, kidney disease and diabetes were all found to predict all‐cause mortality. Using a multivariable logistic regression analysis Table , age  Thalassaemia was not associated with an increased risk of mortality or need of respiratory support; (2) COVID‐19 infection in the thalassaemia cohort was generally mild and not associated with an increased risk of in‐hospital complications; (3) Thalassaemia is an independent predictor of reduced in‐hospital, all‐cause mortality compared to patients with no history of thalassaemia.To date, various co‐factors have been identified as being associated with increased mortality in COVID‐19 patients, amongst these cancer and hemoglobinopathies.We compared data from thalassaemia patients with COVID‐19 to a matched cohort of COVID‐19 patients with no history of thalassaemia and, seemingly paradoxically, found a significantly lower in‐hospital, all‐cause mortality rate in the thalassaemia cohort and a lower rate of in‐hospital complications. Although the rate of liver disease was higher in thalassaemia patients compared to the age‐, gender‐ and cardiovascular disease‐matched group of COVID‐19 patients, the mortality rate was much lower. Of note, recently published data described a worse prognosis in patients with liver disease, and reported a mortality rate of approximately 15%.These data appear to confirm a lower risk of mortality in thalassaemia patients. In our present study cohort, 99% of patients recovered from COVID‐19.Whereas the need for intubation was documented in only one patient, non‐invasive respiratory support was required in seven patients in our study. Compared to the matched cohort, however, these rates were still significantly lower than in the non‐thalassaemia group. The mild courses of COVID‐19 in the thalassaemia cohort may be related to different factors. Recently published data showed that ORF8 and surface glycoproteins of COVID‐19 could form a complex together with porphyrin. In addition, orflab, ORF10 and ORF3a proteins could attack the porphyrin, which is formed in the mitochondria on the 1‐beta chain of haemoglobin.The majority of patients in our cohort came from European countries and as a result, ethnic differences regarding their outcomes cannot be excluded. In the present study, it seems that transfusion independent thalassaemia patients are suffering more significantly from acute kidney injury and they need more oxygen support. Although invasive and non‐invasive respiratory support and mortality are numerically higher, not a significant difference was documented.A recent observational study regarding the relevance of patients’ blood groups and the risk of COVID‐19 infection reported that patients with blood group A are at a higher risk to develop COVID‐19 and patients with blood group O are at a lower risk for developing COVID‐19.Differences with regard to the treatments used were also observed between groups. Drugs and drug combinations such as hydroxychloroquine, hydroxychloroquine + lopinavir + ritonavir or hydroxychloroquine + tocilizumab + lopinavir + ritonavir were more likely to have been used in the COVID‐19 with no history of thalassaemia, which might simply be reflecting the milder courses of COVID‐19 infection observed in thalassaemia patients.Compared to the matched cohort, COVID‐19 symptoms were significantly lower amongst thalassaemia patients.In the Iranian thalassaemia registry, the prevalence of COVID‐19 was 0.14% in the thalassaemia patients. On this point, we cannot provide any data regarding the prevalence of COVID‐19 in thalassaemia patients since we included all patients with COVID‐19 and did not analyse the number of thalassaemia patients in each and every centre.5Analysis of the data of this large international cohort revealed a lower all‐cause mortality rate in thalassaemia patients compared to a matched cohort with no history of thalassaemia. In‐hospital complications relating to COVID‐19 infection were also significantly lower in thalassaemia patients, although the hospitalization rate was 27.7%, which was significantly lower than in the matched cohort.5.1Data for this study were collected from several centres and bias regarding the heterogeneous diagnostic and treatment strategies between them cannot be excluded. In addition, there are differences in the capacity and other medical resources available in these centres. Although the data summarized in this study came from an international registry, the majority of patients included were from European countries  and outcomes may differ due to ethnic factors and standard of care in other parts of the world.The authors declare no conflicts of interest.Ibrahim El‐Battrawy: Conceptualization ; Data curation ; Formal analysis ; Supervision ; Validation ; Visualization . Filomena Longo: Data curation ; Formal analysis . Iván J. Núñez‐Gil: Conceptualization ; Data curation ; Formal analysis ; Supervision . Mohammad Abumayyaleh: Data curation ; Formal analysis . Barbara Gianesin: Data curation ; Formal analysis . Vicente Estrada: Data curation . Alvaro aparisi: Data curation . Ramón Arroyo‐Espliguero: Data curation . Manuela Balocco: Data curation ; Formal analysis . Susanna Barella: Data curation . Andrea Beccaria: Data curation . Federico Bonetti: Conceptualization ; Data curation . Maddalena Casale: Data curation . Elisa De Michele: Investigation . AnnaRita Denotti: Data curation ; Software . Carmelo Fidone: Data curation . Monica Fortini: Formal analysis ; Investigation . Maria Rita Gamberini: Data curation . Giovanna Graziadei: Formal analysis . Roberto Lisi: Data curation . Antonella Massa: Data curation . Alessia Marcon: Data curation . Bryan Rubinski: Investigation ; Writing‐review & editing . Maurizio Miano: Formal analysis ; Methodology . Irene Motta: Software ; Supervision . Valeria Pinto: Validation ; Visualization . Alberto Piperno: Supervision ; Visualization . Raffella Mariani: Methodology ; Visualization . Maria Caterina Putti: Visualization . Alessandra Quota: Software ; Supervision . Michela Ribersani: Data curation . Marco Marziali: Data curation . Domenico Roberti: Formal analysis . Rosamaria Rosso: Software . Immacolata Tartaglione: Data curation . Angelantonio Vitucci: Data curation . Vincenzo Voi: Data curation . Marco Zecca: Data curation ; Formal analysis . Rodolfo Romero: Data curation ; Formal analysis . Charbel Marouneld: Methodology . Inmaculada Fernández‐Rozas: Data curation . Carolina Espejo: Investigation . Wulandewi Marhaeni: Investigation . Marcos Garcia Aguado: Data curation . Maria Domenica Cappelini: Software . Silverio Perrotta: Data curation . Lucia De Franceschi: Data curation . Antonio Piga: Formal analysis . Gian Luca Forni: Supervision . Ibrahim Akin: Validation ; Visualization ; Writing‐original draft ; Writing‐review & editing .Table S1Click here for additional data file.


Even if electromyography (EMG) is routinely used to confirm the diagnosis of amyotrophic lateral sclerosis (ALS), few studies have analysed the correlation between electrophysiological parameters and clinical characteristics of ALS. We assessed if the quantification of active denervation (AD) and chronic denervation (CD) provides clinicians with information about phenotype, disease progression and survival in ALS patients.We studied a cohort of 689 ALS patients recording the following parameters: age and site of onset, survival, MRC scale for muscle strength evaluation, burden of upper and lower motor signs as measured with specific scales , ALSFRS-R, progression rate (ΔFS), MITOS and King’s Staging systems (KSS). We performed EMG on 11 muscles, and calculated semiquantitative AD and CD scores for each limb, as well as for the bulbar and spinal regions.p = 4.4 × 10–37 and p = 2.8 × 10–45) and a negative correlation with MRC (respectively p = 4.5 × 10–35 and p = 3.0 × 10–35). Furthermore, patients with higher spinal AD and CD scores had significantly lower ALSFRS-R scores, and higher KSS and MITOS stages. Conversely, only AD was associated to higher ΔFS (p = 1.0 × 10–6) and shorter survival (p = 1.1 × 10–5).We found a positive correlation between AD and CD scores with LMNS (respectively Our results confirmed that EMG examination represents not only a diagnostic instrument, but also a prognostic tool. In this context, AD seems to be a reliable predictor of disease’s progression and survival while CD better describes functional disability.The online version contains supplementary material available at 10.1007/s00415-022-11404-4. Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disorder of adult life of undetermined aetiology that primarily affects the motor neurons of the cerebral cortex, brainstem, and spinal cord. The El Escorial criteria for the diagnosis of ALS were initially published in 1994 and required the presence of lower motor neuron (LMN) and upper motor neuron (UMN) signs in different body regions  recognizIn this context, electromyography (EMG) represents the most important diagnostic test in ALS and, according to the Awaji criteria , neurophThe aim of our work is to assess if the quantification of active denervation (AD) and chronic denervation (CD) provides clinicians with information about clinical features, disease progression and survival in ALS patients. In this context, EMG could represent not only a diagnostic test but also a tool to better define clinical phenotype and prognosis of motor neuron disease.A cohort of patients diagnosed with motor neuron disease  according to El Escorial revised criteria was recruited at IRCCS Istituto Auxologico Italiano between 2008 and 2021. The following clinical information were collected: age at onset; survival ; ALSFRS-R score at evaluation; progression rate (ΔFS) calculated with the formula [(48 − ALSFRS−R score)/disease duration expressed in months]; clinical stages according to the King’s and Milano-Torino (MITOS) staging systems. With regard to motor assessment, weakness of spinal muscles was studied using the MRC scale for three muscle groups for each limb  for a total score of 0–60. The burden of LMN involvement was assessed using LMNS, scoring weakness and wasting in each limb on a scale from 0 to 3 .EMG was performed on all patients using a standard protocol . ExaminaThe following body regions and muscles were evaluated during score calculation: bulbar region , right arm , left arm , right lower limb  and left lower limb . Each muscle was investigated in three different sites and every site was explored using 3 or 4 needle insertions. For each of the five body regions a score ranging from 0 to 3 was assigned according to the amount of positive sharp waves and/or fibrillation potentials for active denervation and characteristics of Motor Unit Action Potentials (MUAPs) for chronic denervation . AD and CD scores for spinal muscles were then obtained by summing the partial scores for each limb.p < 0.05 was considered significant, and all tests were two-sided.Statistical analysis was carried out with IBM Statistical Package for Social Science (SPSS) version 26. Descriptive statistics are reported as numbers and percentages for categorical variables or mean and standard deviation for continuous variables. Survival analysis was performed dividing patients in two groups according to the median values of spinal active and chronic denervation and building Kaplan–Meier curves. To assess the effect of denervation on survival, Cox proportional hazard regression was used. The log-rank test was used to compare survival across groups. EMG parameters were compared with continuous variables using Pearson correlation. A https://doi.org/10.5281/zenodo.6818074) and will be disclosed upon reasonable request. This study was approved by the Ethics Committee of IRCCS Istituto Auxologico Italiano (DAMARE 2021_05_18) and conducted according to the principles expressed in the Declaration of Helsinki.Informed consent for using anonymized data for research purposes was obtained from all patients or their authorized legal representatives. Anonymized data are archived on Zenodo , flail arm , flail leg , UMN-predominant (UMNp) ALS , PLS , respiratory ALS , bulbar ALS  and PMA . ALSFRS-R and ΔFS score were available for 481 patients and the mean scores were 38.2 (± 6.9) and 0.86 (± 0.92) respectively. MITOS and KSS were available respectively for 390 and 684 patients. MRC was calculated for 574 ALS individuals with an average score of 50.0 (± 9.3) in our cohort. LMNS and PUMNS were available for 684 and 685 patients with a mean score of 4.8 (± 3.1) and 9.8 (± 7.3) respectively  of whom were females and 438 (63.6%) males. The mean age of disease onset was 60.4 years (± 12.2), while average survival was 37.6 months (± 36.0). Site of onset (known in 686/689 cases) was bulbar in 155 (22.6%) and spinal in 531 (77.4%) patients. The median time to visit was 21.6 months after disease onset (range 1.5–273.7). With regard to the motor phenotype, we could divide the cohort into the following phenotypes: classic ALS  and CD (R2 = 0.236 p = 3.0 × 10–35) scores  and CD (R2 = 0.254 p = 2.8 × 10–45) scores  and CD  scores.To define if electromyographic parameters represent good markers of muscle weakness, we first compared spinal AD and CD scores with MRC and observed negative correlations with both AD , and CD scores in both the spinal  and bulbar  segments   and CD (p = 1.9 × 10–7) scores ; stage 2 vs 0 ]. We found a similar association with regard to KSS stages for spinal AD (p = 0.025) and CD (p = 0.001) scores .These results were also supported by the findings regarding MITOS and KSS staging systems. Indeed, we observed an association between more severe MITOS stages with higher spinal AD  , p = 7.3 × 10–9], while no effect was observed for spinal CD . Similarly, bulbar AD scores correlated with shorter survival , while bulbar CD did not . Lastly, no association was identified for AD and CD scores with age at onset.Notably, after subdividing our cohort in two groups, according to the median value of spinal AD 4.0), we found that patients with less severe signs of active denervation had significantly longer survival  in predicting survival if compared to the presence of previously remodelled motor units (CD). Indeed, notably, CD well correlates with ALS staging system scores and functional disability but is not associated with survival. Similarly, other previous studies analysed the role of EMG as a prognostic instrument.A recent study of 92 patients calculated denervation values, considering for the score only sites that showed the contemporary presence of active and chronic denervation signs, for bulbar, cervical and lumbosacral regions. In this report higher general denervation scores  and higher bulbar denervation scores were significantly associated with a reduced survival . In anotOur study has some limitations. Firstly, our cohort includes a relatively small number of patients at more advanced disease stages. Additionally, ours is a cross-sectional study and therefore longitudinal data on independent cohorts will be needed to assess evolution of EMG parameters over the course of the disease. The relatively low number of muscles studied does not allow to correlate specific EMG regional patterns with ALS phenotype and prognosis. We also did not routinely perform EMG of thoraco-abdominal muscles, and thus we cannot draw any association between neurophysiological parameters and respiratory function in our patients. Finally, the calculation of AD and CD scores was performed by a single neurophysiologist with experience in the field of motor neuron disease. As such, although the evaluator was blinded to clinical phenotype of ALS patients, it is not possible to determine the inter-rater reliability of AD and CD scores. Conversely, the EMG protocol adopted in our study is reproducible, being routinely used in a clinical setting at our ALS Clinic and generally well-tolerated by patients.Our results confirm that EMG examination represents not only an essential diagnostic instrument, but also an important tool to better characterise phenotype, functional disability, disease progression and prognosis in ALS. Furthermore, we investigated the different role of AD, which in our work seems to be a reliable predictor of disease progression and survival, and CD, which conversely seems to better describe functional disability. Ours is the first study that describes all these correlations in a large cohort of patients, although longitudinal data on independent ALS populations will be needed to confirm these results.Supplementary file1 (DOCX 13 kb)Below is the link to the electronic supplementary material.