Triple-negative breast cancer is the most potent metastatic type of breast cancer that can spread to other body parts. Chemotherapy and surgical intervention are the sole treatments for TNBC, owing to the scarcity of therapeutic targets. Manipulation of the membranes as per the desired targets of exosomes has recently gained much attention as a drug delivery method. Despite their known roles in different diseases, very few studies have focused on signalling that triggers the metastasis of triple-negative breast cancer to other body parts by exosomes. This article highlights the significant roles of exosomes associated with TNBC, the involvement of exosomes in breast cancer diagnosis, progression, and the treatment of triple-negative breast cancer by the exosomes as a drug delivery system. This review paper also illustrates the role of exosomes in initiating EMT in breast cancer, including novel signalling. Breast cancer is the leading cancer in women, accounting for 27% of all cancers in India . TNBC suClinically, treatment for TNBC is very restricted due to the lack of specific targets . The deaRestrictions on anticancer drug potency are due to poor drug solubility, half-life, and a drug-resistant result of inadequate binding of the drugs with the specific ligands of the tumours . To findIntercellular communication in all cells results from the extracellular vesicles produced by almost all cell types. EVs can be divided into three categories, i.e., microvesicles (MVs), exosomes, and apoptotic bodies, depending upon the size and the generation of pathways . Among tExosomes are overexpressed with distinguished membrane proteins called tetraspanins and proteins such as CD9, CD37, CD63, CD81, and CD82. Tumour susceptibility gene (TSG)-101 and Alix of the exosome sorting complex required for transport (ESCRT) are some of the exosome’s biomarkers . The eveThe number of exosomes released by TNBC (triple-negative breast cancer) cells is more incredible than more significant normal healthy cells. The augmentation of TSAP6, a p53-regulated gene product that governs the exosomal secretion signalling transcription by activating p53, which is frequently aberrantly driven in cancer cells, could alter the rise in exosome shedding from cancer cells. The most associated biomarkers with exosomes are tetraspanins , proteins of the endosome system , and heat shock chaperones . These are all the characterizations of all exosomes ,35,36.Exosomes produced by cancer cells have different content than normal cells. The microRNA content in the exosomes extracted from cancer cells has different signature microRNA and acts similar to “fingerprints”, which can identify their origin. Hence, they can also serve as a biomarker. A protein senescence-associated secretory phenotype (SASP) that can enhance tumour growth is one of the constituents of exosomes .Chemotherapeutics induce apoptosis. Some cancer cells experience therapeutic-induced senescence (TIS), which enables them to stay metabolically active but lose their capacity to metastasize. This condition makes them more resistant to chemotherapies. Kavanagh et al. found that exosomes collected from TIS TNBC CAL51 cells had much larger quantities than non-senescent cells. Concerning non-senescent TNBC cells, the researchers found a remarkable increase in the amounts of 142 proteins in exosomes from these TIS cells. Essential proteins, which are all involved in (i) cell proliferation, (ii) ATP depletion, (iii) apoptosis, and (iv) SASP factors, are more prevalent in exosomes from TIS cells, implying that the abolition of these proteins from TIS cells through exosomes permits cancer senescent cells to stay feasible .Exosomes also play an important role as a messenger and can communicate between the recipient cells and donor cells through the microRNA, which they carry, which becomes translated into proteins. Communication can involve immune suppression, microenvironment changes, angiogenesis, immune escape and invasion, and tumour progression and repression.With the advancement of nano-theragnostics, exosome profiling, diagnosis, and potential disease therapy are possible. A ‘liquid biopsy’ enables the study of EVs and exosomes. It also aids in analysing circulating DNA from tumours and cells from tumours. The exosomes carry specific biomarkers that help to diagnose triple-negative breast cancer. The content of the exosomes is different for patients with triple-negative breast cancer . ProteinEMT is a process through which normal cells transfer to mesenchymal cells. Mesenchymal cells have the property of invasion and malignancy, leading to the worst prognosis and TNBC migrating to other organs. The EMT transcription factors involved are β-catenin, TWIST, zinc finger protein, SNAIL2 (zinc finger protein SNAI 1), SLUG (SNAI 2 gene), ZEB1 (zinc finger E-box-binding homeobox 1), and ZEB 2. Studies have proven that altering these transcription factors is not enough to cease the epithelial–mesenchymal transition. The exosomes are involved in intercommunication between the cancer cells. The exosomes have various contents, which can regulate the transcription involved in the epithelial-to-mesenchymal transition . The litSynthesis of exosomes takes place when intercommunication between notch receptors, ligands (JAGs) (jagged protein), and regulators (ADAM 10/17)  occurs. ASPH (aspartate beta-hydroxylase) initiates the Notch cascade to synthesis or release of pro-metastatic exosomes. Normal breast cells have muted ASPH. The activation of ASPH by the NOTCH signalling gives rise to aggressive tumour progression in breast cancer with the help of exosomes . Hippo sThe Wnt/β-catenin signalling pathway is involved in the epithelial–mesenchymal transition. Specifically, a β-catenin molecule is involved in the epithelial–mesenchymal transition ,51. ScieThe lncRNAs MALAT1 (metastasis-associated lung adenocarcinoma transcript 1) and ID4 (inhibitor of DNA-binding 4) cause an upregulation of the circular RNA circ 0076611 in exosomes released by TNBC. This circular RNA circ 0076611, released from the exosomes of TNBC cells, interacts with MYC (proto-oncogenes) and VEGFA  mRNAs, further initiating cell proliferation and migration of TNBC cells . ExosomeThe production extraction of exosomes from the cells includes ultrafiltration, ultracentrifugation and precipitation, and immunoaffinity . All thePreservation of the exosomes often involves storing them at −80 °C or 4 °C. Keeping them at −80 degrees Celsius shows perfect enzymatic activity of beta-glucuronidase . ExosomeLoading the exosomes with the desired compound is important. The exosomes can be used as a delivery agent for molecular drugs such as microRNA and siRNA, which are digested by the nuclease present in the body . Other bIn the exogenous methods, the exosomes are loaded by the interest of protein or nucleic acids with the help of artificial processes such as electroporation, co-incubation, sonication, lipofection, pH gradient, and calcium chloride methods . All loaSurface engineering of exosomes is significant, as exosomes may be helpful in delivering drugs in vitro, but, for the perfect delivery in vivo, the exosomes need to be engineered. Otherwise, it will be limited due to non-specific toxicity and off-target effects . The exoExosomes can be used as delivery agents to influence signalling pathways, silence genes, and regulate the expression of different proteins. Exosomes perform gene transfection because of their intrinsic role in endogenous gene transfer. The endogenous origin makes exosomes less toxic to the cells and has a minimal immune effect . The humHELA-exosomes  are engineered exosomes that exhibit significant anticancer effects in both a mouse model and human breast cancer organoids by increasing the activation of cDC1s in situ and, hence, boosting the tumour-reactive CD8+ T cell responses. Exosomes extracted from mesothelin (MSLN) initiate CAR-T cells, which have abilities similar to T cells, including CARs (cysteinyl-TRNA-synthetase) and CD3 surface expression. The exosomes loaded with CARs are seen to inhibit the growth of both endogenous and exogenous MSLN + TNBC. With the help of proteins such as perforin and granzyme B, exosomes can terminate the tumour. The tumour inhibition rate is high, followed by fewer side effects. Exosomes that have been isolated from CAR-T cells that are mesothelin (MSLN)-targeted significantly hinder the proliferation of MSLN-positive TNBC cells. Exosomes extracted from CAR-T cells exhibit a form of cell-free therapy that is significantly less toxic and more effective at preventing tumour growth than live CAR-T cells .The exosomes derived from the HEK293T (human embryonic kidney cells with adenovirus) cells have LipHA-hEVs  as a ligand loaded with doxorubicin to target those cells expressing CD44, which often escape from the macrophages in MCF7/ADR cell lines or breast cancer . TherefoIt is well known that the recently-created chemotherapeutic medication erastin has the ability to cause ferroptosis in TNBC cells, but its use has been limited by its poor water solubility and side effects. To solve this problem, Yu et al. loaded erastin into exosomes labelled with folate (FA) to target the TNBC cells overexpressing folate receptors on the cell surface, thereby enhancing ferroptosis with the pre-depletion of intracellular glutathione and overgeneration of reactive oxygen species (ROS). This significantly reduced the proliferative and migratory abilities of MDA-MB-231 cells . Anti-ILOne possible option is to increase tissue-specific targeting using tissue-specific antibodies or homing peptides by engineering the innate homing ability of exosomes. A chimeric protein against HER2+ is delivered by engineered exosomes with pLEX-LAMP DARP on their surface ,85. EngiExosomes generated from adipose mesenchymal stem cells infused with miRNA-381-3p reduce the metastasis in TNBC cell lines. Additionally, it was observed that miRNA-381-3p downregulates the expression of Wnt signalling and elements linked to the epithelial–mesenchymal transition in MDA-MB-231 . miR3182Exosomal miR-335-5p suppresses TNBC immune escape mediated by PD-L1 by promoting the ubiquitination of PD-L1 by USP22 via downregulation of USP22 and PD-L2 . miR-134The level of microRNA let-7a expression in TNBC cell lines and patients suffering from triple-negative breast cancer shows low expression. The let-7a conjugated with exosomes, when targeted in the TNBC mice model, reduces tumour growth, migration, and invasion. The let-7a binds to the 3’ UTR of the c-Myc gene and silences the gene expression . miR-770In vivo tumour growth is aided by miR-9. miR-9 is an essential player in the cross-communication between cancer cells and stroma . miR-9 aAs per the current report, exosomes delivering lncRNA DARS-AS1 siRNA in TNBC inhibit mild stress induced by TNBC tumours, which causes metastasis . The mesEndogenous exosomes’ capacity to transmit proteins or miRNA makes them particularly useful for delivering nucleic acid- or peptide-based therapies directly to cancer cells. The siS100A4 is coupled to cationic bovine serum albumin (CBSA). This approach has demonstrated a strong suppressive impact on post-surgery metastasis in TNBC. The expression of the S100A4 protein, which is involved in metastasis, is downregulated by exosome-mediated RNAi, and postoperative metastasis in TNBC is significantly suppressed as a result. These investigations show that an effective therapeutic method for the prevention and treatment of cancer is the administration of siS100A4 through exosome membrane-packed core-shell NPS . Table KLF4 (Kruppel-like factor 4) and HOXD4 (homeobox D4) are the transcription factors that play a major role in metastasis and proliferation. MicroRNA miR-10b (microRNA) can transfer in metastatic BC cells (MDA-MB-231) according to different research by Singh et al., 2014 . The tarGong et al. created metalloproteinase-15-rich nanoscale target-specific exosomes that can carry doxorubicin (DOX) and miR-159 that have been modified with cholesterol to TNBC cells. In both in vitro and in vivo scenarios, it has been demonstrated that exosomes co-loaded with DOX and cholesterol-miR159 efficiently inhibit the growth of MDA-MB-231 TNBC cells by downregulating the expression of TCF-7 (transcription factor 7) . The exoPD-L1 (programmed death ligand 1) is the major biomarker for TNBC . When exExosomes for immunotherapy in TNBC can also be produced by mesenchymal stem cells (MSCs), i.e., MSC-derived exosomes, in addition to lymphocytes. Exosomes made from MSCs play a crucial role in immunomodulation as well, primarily by controlling immune cell activity and modifying the release of inflammatory mediators such as TNF and IL-1 in inflammatory bowel disease . MonocytDerivation of exosomes is also from many other sources such as bacteria and plants. Exosomes from plants and bacteria have shown anti-tumour function. Low toxicity and large production are why exosome usage is attractive in cancer therapy. The tumour microenvironment contains EVs or outer membrane vesicles in the size of 40 nm obtained from gram −ve and +ve bacteria. They can produce anti-tumour cytokines as well as interferon-γ and CXCL10 (C-X-C motif chemokine ligand 10) . Plant-dNo current therapy involves plant-derived exosomes for the treatment of TNBC. As per the studies after the characterization of exosomes extracted from plants, it can be used as a new therapeutic for TNBC by targeting the pathways such as Wnt signalling, NOTCH, HIPPO signalling, and genes involved in epithelial–mesenchymal transition, which can fulfil the present research gap as mentioned in this review paper. More exploration with plant exosomes is needed.Vaccines against cancer help the human body to redirect the immune system to fight against cancer rather than to prevent it. Standard vaccination leads to the prevention of infection towards other viral or bacterial infections. As a result, it appears that developing preventative EV-based cancer vaccines against virally-produced cancers, such as HPV, and therapeutic EV-based cancer vaccines for non-virally induced cancers, such as TNBC, would be more appropriate. EVs can communicate between the cells and, therefore, can be used as a vaccine. Natural killer cells derived from exosomes have more potential cytotoxicity effects against tumour cells. Exosomes have the potential to be a drug delivery agent in vitro as well as in vivo. Clinically, exosomes are tested as a delivery vehicle in other cancers. Exosome delivery could be a hope for TNBC as well ,122. ExoExosomes are small vesicles secreted from dendritic cells consisting of MHC class 1 and class II molecules, along with other CD86 proteins and tetraspanins, stimulate an immune response and undergo phase 2 clinical trials ,124. TheExosomes have many promising characteristics—bio-feasibility, biocompatibility, good loading capacity, and less toxicity, being derivatives from biological sources—making them a very appropriate candidate for drug delivery. Exosomes can easily be manipulated accordingly by making changes to their surface with the addition of proteins or antibodies. Many methods are available for loading and also for extraction. Cost-effective production of exosomes is also needed, as there is a limitation with the available methods for the extensive quantity of extractions from the cells. Much scientific data showed how exosomes could reduce the metastatic rate of TNBC by delivering miRNA, siRNA, LncRNA, and chemotherapeutic drugs in breast cancer cell lines. The extraction of exosomes from plants has not been explored yet in the field of TNBC, but scientific proof is present with brain cancer. Hence, the exosomes from plants can also be used as a delivery agent for TNBC. More research is needed in the field to study the characteristics of TNBC and the constituents of TNBC. Exosomes also play a crucial role in the EMT transition in TNBC. Altering the secretions of exosomes by modulating the content or silencing some genes may be a solution for the metastasis of TNBC, which progresses to other organs. Exosomes can also be biomarkers for TNBC that are responsible for chemoresistance in cancers. Exosomes also target signalling such as Wnt, NOTCH, and PTEN/AKT/PI3K (phosphatase tensin homolog/phosphokinase pathway), etc., in the TNBC, which plays a vital role in the epithelial-to-mesenchymal transition. Exosomes can also target genes, making them a perfect target delivery agent. Therefore, a cure for TNBC with personalized therapy is possible if targeted therapy with the help of exosomes is studied more widely. An in-depth study can also pave the way for exosomes to emerge as a vaccine to treat triple-negative breast cancer.


Previous meta-analyses have shown that almost all antipsychotics are associated with weight gain. However, mean weight gain is not informative about clinically relevant weight gain or weight loss.To provide further insight into the more severe body weight changes associated with antipsychotic use, we assessed the proportion of patients with clinically relevant weight gain (CRWG) and clinically relevant weight loss (CRWL), defined as ≥7% weight gain and ≥7% weight loss.We searched PubMed, Embase and PsycInfo for randomised controlled trials of antipsychotics that reported CRWG and CRWL in study populations aged 15 years or older. We conducted meta-analyses stratified by antipsychotic and study duration using a random-effects model. We performed meta-regression analyses to assess antipsychotic-naive status and psychiatric diagnosis as modifiers for CRWG. PROSPERO: CRD42020204734.We included 202 articles (201 studies). Almost all included antipsychotics were associated with CRWG. For CRWL, available data were too limited to draw firm conclusions. For some antipsychotics, CRWG was more pronounced in individuals who were antipsychotic-naive than in individuals switching to another antipsychotic. Moreover, a longer duration of antipsychotic use was associated with more CRWG, but not CRWL. For some antipsychotics, CRWG was higher in people diagnosed with schizophrenia, but this was inconsistent.Switching antipsychotic medication is associated with both weight gain and weight loss, but the level of CRWG is higher than CRWL in antipsychotic-switch studies. CRWG was more pronounced in antipsychotic-naive patients, highlighting their vulnerability to weight gain. The impact of diagnosis on CRWG remains inconclusive. A complex interplay of neurotransmitters, hormones, neuropeptides and genetic and epigenetic factors contribute to it.16 However, it remains impossible to predict antipsychotic-induced weight gain in individual patients, as no definite predictive factors are known.16The life expectancy of people with severe mental disorders is 20 years lower and mortality is higher than in the general population, partly explained by the metabolic side-effects of antipsychotic medication.17 Other reviews and meta-analyses showed similar results.25 In general, olanzapine and clozapine are associated with the most severe weight gain.25 Ziprasidone,25 aripiprazole25 and lurasidone25 show a more benign profile. However, looking at weight as a continuous variable has limitations, as some patients gain body weight whereas others lose body weight. The average outcome of weight change depends on a balance between weight gain and weight loss. This average may also be determined by outliers in a small proportion of patients, leading to an overestimation of the mean weight gain or loss. Therefore, assessing the proportion of patients with clinically relevant weight gain (CRWG) and clinically relevant weight loss (CRWL), defined as ≥7% weight gain and ≥7% weight loss,27 may provide further insight into the more severe body weight changes associated with antipsychotic use.A recent meta-analysis showed that antipsychotics were associated with weight gain over time.28 cariprazine,24 lurasidone,29 quetiapine XR28 and ziprasidone.24 In these meta-analyses, olanzapine was associated with the highest risk of CRWG.28 In a recent comparison, in mainly short-term studies, olanzapine was associated with a higher risk of CRWG than risperidone and paliperidone. Risperidone, in turn, had a higher chance of CRWG than paliperidone.30 In a meta-analysis of first-episode psychosis studies, all antipsychotics except ziprasidone showed a significantly higher risk of CRWG compared with placebo.31To date, only a handful of systematic reviews and meta-analyses have addressed CRWG and even fewer CRWL. Over the short term (≤13 weeks), evidence indicated that most antipsychotics were associated with a significantly higher risk of CRWG compared with placebo; the exceptions were aripiprazole,22 For ziprasidone and placebo, the proportion of patients with CRWL was higher than that with CRWG, suggesting that ziprasidone could be weight-neutral.22 In a recent review, most antipsychotics were associated with a significantly higher risk of CRWG compared with placebo; the exception was aripiprazole.32Evidence for the long term is particularly scarce. A comprehensive database analysis showed that in long-term studies (≥52 weeks), a higher proportion of patients taking olanzapine or risperidone had CRWG than patients taking ziprasidone or a placebo.20 It suggests a time effect, with a longer duration of antipsychotic use associated with weight gain.20 Also, a statistically significant proportion experienced CRWL after starting an antipsychotic, but no duration–response pattern was found.20 To date, no other systematic review or meta-analysis has assessed CRWG or CRWL in longer-term (≥13 weeks) studies.A previous meta-analysis, stratified by study duration, showed that the duration of antipsychotic use was associated with a clinically significant proportion of people experiencing CRWG on almost all antipsychotics.29 or did not distinguish between short-term and long-term studies.32 They also included mainly studies involving people with schizophrenia, and they did not clearly distinguish between antipsychotic-naive patients (i.e. those with no history of antipsychotic use) and antipsychotic-switching patients (i.e. those switching from one antipsychotic to another).25 This distinction is relevant because antipsychotic-naive patients are more likely to develop antipsychotic-induced weight gain, and there is no effect of prior antipsychotic use on body weight, as previous reviews and meta-analyses33 have shown.Recent systematic reviews and network meta-analyses either focused mainly on short-term weight gain20 This update was limited to data on CRWG and CRWL, and included the new antipsychotics introduced from 2012 onwards. As in the earlier meta-analysis, we stratified analyses by study duration and distinguished four time frames by dividing the number of days in the study by 7: short-term (<6 weeks), medium short-term , medium-term  and long-term studies (>38 weeks). We distinguished between antipsychotic-naive and antipsychotic-switching patients and reported CRWG and CRWL regardless of diagnosis.With this meta-analysis, we aimed to update the Bak et al meta-analysis from 2014.Second, to enable future prediction, we investigated whether several factors were associated with weight gain. We expected that the duration of antipsychotic use would be associated with a higher proportion of individuals with CRWG and CRWL. Furthermore, we expected CRWG to be more pronounced in the antipsychotic-naive group than in the antipsychotic-switching group. Finally, we investigated whether a diagnosis of schizophrenia was associated with more CRWG compared with other psychiatric diagnoses.34 The review protocol was registered with the International Prospective Register of Systematic Reviews (PROSPERO: CRD42020204734).The meta-analysis was performed and reported according to the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement.We performed a literature search assessing CRWG and CRWL associated with the use of antipsychotic medication in randomised controlled trials (RCTs).https://doi.org/10.1192/bjo.2022.619. We searched databases from their respective inception dates until 21 July 2021 and used no language filters. Hand-searching reference lists of previously published reviews and meta-analyses supplemented the database search.We conducted an electronic database search in PubMed, Embase and PsycInfo for articles that reported CRWG or CRWL in patients taking antipsychotic medication. The search terms used for PubMed were  AND weight)), combined with the various antipsychotic names. The search terms were similar for each database; the complete search strategy is provided in Supplementary Appendix 2, available at We designed the search to identify RCTs with patients receiving an antipsychotic medication in placebo-controlled or active-controlled trial conditions. The outcome measures of interest were CRWG and CRWL, defined as ≥7% weight gain or ≥7% weight loss. We defined the following inclusion and exclusion criteria.study populations aged 15 years or older taking an antipsychotic drug, all diagnoses included, except eating disordersa randomised placebo-controlled or active-controlled designavailable CRWG or CRWL data per antipsychotic (gain and/or loss).The inclusion criteria were:studies that did not apply an intention-to-treat (ITT) analysisa study population with at least 1 participant younger than 15 yearsa study population diagnosed with eating disorders (e.g. anorexia nervosa or bulimia nervosa)a study population receiving adjunctive therapy  that might influence body weight during the studya study population using antipsychotic medication to treat nausea or vomiting in, for example, people with cancer or pregnant womenstudies with a duration of less than 1 week, for example studies assessing brain changes after antipsychotic intake, rapid tranquillisation studies, acute antipsychotic intervention studies and treatment of delirium: these studies are too short in duration and interventions will not substantially affect body weight in a weekpost hoc and subgroup analyses; case reports; expert opinions; narrative reviews; poster presentationssystematic reviews; meta-analyses; pooled data-sets; non-human studiesstudies not published in English: although we are multilingual, we chose English as it is the most accessible to all researchers and the most transparent and traceable.The exclusion criteria were:At least two reviewers  per article independently performed all the steps of the selection process. First, we controlled for double entries and checked titles and abstracts for relevance and eligibility. Then, we screened potentially relevant full-text articles for the inclusion and exclusion criteria. In case of disagreement between the reviewers, they discussed the articles until they reached a consensus.We extracted data only from studies that performed an ITT analysis because drop-out due to weight problems could otherwise bias our results. We excluded extension studies if they had different baseline data than the original study or reported a subgroup analysis. Also, we included only studies that reported weight change per individual antipsychotic or group of antipsychotics (i.e. first- or second-generation antipsychotics).Two reviewers independently extracted the data , with these two reviewers checking each other's data extraction. In case of disagreement, a third reviewer (M.B.) made the final decision.Characteristics of included studies to be extracted were: author, year of publication, study design, number of participants, the proportion of male/female participants, mean age (with standard deviation), diagnosis, antipsychotic treatment, antipsychotic-naive/switching population, study duration (in weeks), baseline body mass index (BMI), the proportion of participants with CRWG and proportion of participants with CRWL. We extracted data for every randomised study group separately (either using antipsychotic or placebo).The primary outcome measures were: the proportion of patients with (a) CRWG or (b) CRWL after starting or switching an antipsychotic.We defined four categories of antipsychotic exposure: short-term (<6 weeks), medium short-term (6–16 weeks), medium-term (16–38 weeks) and long-term (>38 weeks).35 As we had already started piloting our review before a newer version of the tool became available, we decided to continue using the original instrument for our assessment. We assessed the following domains: (a) random sequence generation, (b) allocation concealment, (c) masking (‘blinding’), (d) incomplete outcome data and (e) selective outcome reporting; and classified individual risk of bias items as low, high or unclear risk (Supplementary Appendix 11). Any disagreements among reviewers were resolved by consulting a third reviewer (M.D.). The Grading of Recommendations Assessment, Development and Evaluation (GRADE) system36 was used to evaluate the certainty of evidence as high, moderate, low or very low (Supplementary Appendix 11).Risk of bias assessment for the primary outcomes was done by two independent reviewers  using the Cochrane risk of bias tool.37 We performed meta-analyses using a random-effects model since we assumed included studies to be heterogeneous. Forest plots, including pooled estimates with their corresponding 95% confidence intervals (95% CI), were generated for all antipsychotics stratified by study duration using the metaprop command via the Freeman–Tukey double arcsine transformation method.38 The analyses aim to test whether the proportions of patients with CRWG and CRWL are significantly different from zero. Meta-analyses were performed only if data from at least two studies per antipsychotic could be included. Summary estimates were calculated under the DerSimonian and Laird method. We calculated the 95% CI of the pooled estimates using the Wald method. We further explored heterogeneity using the I-squared test, which estimates the proportion of variability in the summary estimates resulting from heterogeneity among the true effects. I-squared values of more than 50% were considered to indicate heterogeneity.We performed all statistical analyses using Stata for Mac version 13.1.In the subset of antipsychotics with sufficient data, we performed meta-regression analyses to assess whether the duration of antipsychotic use was a modifier for CRWG or CRWL and whether there was a difference in weight gain between antipsychotic-naive and switched patients. To this end, we transformed proportional data using a natural logarithm. Furthermore, we performed meta-regression analyses to investigate whether other psychiatric diagnoses were associated with less CRWG compared with schizophrenia. Schizophrenia was used as the reference category in these analyses for reasons of power, since people with schizophrenia are by far the most included in RCTs assessing antipsychotics and body-weight change. Analyses were stratified by antipsychotic and duration of exposure.Since the inclusion of various study populations could have contributed to heterogeneity, we performed sensitivity analyses by excluding studies in antipsychotic-naive populations and studies in populations aged over 65 years. We also performed a sensitivity analysis by excluding studies with a high risk of bias for at least one domain according to the Cochrane risk of bias tool.Finally, we assessed publication bias stratified by antipsychotic and duration. We obtained funnel plots and performed Egger tests. We performed trim-and-fill analyses to estimate the effects of publication bias.post hoc analyses and conference abstracts yielded another 47 potentially eligible articles.The database search yielded a total of 2363 articles: 918 in PubMed, 1393 in Embase and 52 in PsycInfo. Manual searches of reference lists of previously published reviews and meta-analyses, pooled analyses, post hoc analyses were included only when the original published articles did not report CRWG or CRWL.43 RCTs that allowed co-medication were included only if a stable dose was maintained throughout the study period.After removing duplicates, 1822 articles remained. Checking titles and abstracts led to the exclusion of a further 1265 articles. The PRISMA flow diagram presents an overview of the selection process (Supplementary Appendix 1). Pooled data-sets and 45 We included 202 articles (201 studies) with a total of 80 372 participants in the meta-analysis (Supplementary Appendix 3).Full-text screening resulted in the exclusion of a further 355 articles (Supplementary Appendix 4). Two articles reported data on one study, with one paper reporting data on CRWG and the other on CRWL in the same study population.46 Most studies reported on CRWG in antipsychotic-switch patients (193 studies); only 7 studies investigated CRWG in antipsychotic-naive patients. The proportion of participants showing CRWG was significant for all antipsychotics and for placebo and varied between 1.7% (blonanserin <6 weeks) 1.9% (brexpiprazole 6–16 weeks) and 2.0% (placebo <6 week) on the one hand and 47% (clozapine 16–38 weeks), 49% (first-generation antipsychotics >38 weeks) and 76% (clozapine >38 weeks) on the other. Full details and forest plots can be found in Supplementary Appendix 5. Results for antipsychotics for which data for only one exposure period were available are presented in Supplementary Appendix 7. The I-squared for the included studies ranged from 0.0 to 94.16%, indicating no heterogeneity to strong heterogeneity (Supplementary Appendix 5.1).Of the included studies, 200 studies had usable data on CRWG . Two studies reported on CRWL only.CRWG by treatment duration is shown in Meta-regression analyses showed that the proportions of patients with CRWG were significantly higher for aripiprazole , asenapine (>38 weeks), cariprazine (16–38 weeks), first-generation antipsychotics (>38 weeks), haloperidol , olanzapine , paliperidone  and risperidone (>38 weeks) compared with the exposure period <6 weeks. For brexpiprazole, CRWG was significantly lower in period 2 (6–16 weeks) than in the exposure period <6 weeks. Full results are presented in I-squared for the included studies ranged from 0.0 to 94.86%, indicating little to strong heterogeneity (Supplementary Appendix 6.1).Of the 202 studies included in this meta-analysis, only 45 reported CRWL . Data were available for amisulpride, aripiprazole, asenapine, haloperidol, lurasidone, olanzapine, paliperidone, quetiapine, risperidone, second-generation antipsychotics, ziprasidone and placebo. Overall, the proportion of patients who lost ≥7% of body weight was rather modest. After 38 weeks of treatment, CRWL was apparent for aripiprazole , olanzapine , lurasidone  and ziprasidone . In the placebo condition, 12.0% (95% CI 7.0–1.8) of the patients lost ≥7% of their body weight after 38 weeks of treatment. Further details and forest plots can be found in Supplementary Appendix 6. Results for antipsychotics for which data were available for only one exposure period are presented in Supplementary Appendix 7. The 53 reported on CRWG in antipsychotic-naive patients (19 records) and no studies reported on CRWL in that population. For aripiprazole (6–16 weeks), olanzapine  and quetiapine (6–16 weeks) more antipsychotic-naive patients than switch patients had CRWG (Only seven of the included studieshad CRWG . DiffereAfter excluding studies examining antipsychotic-naive patients (seven studies), results were essentially unchanged and in the same direction. For blonanserin (16–38 weeks), analyses could not be repeated as data were available only for antipsychotic-naive patients. After excluding studies that examined patients older than 65 years (nine studies), the results were also unchanged, except for brexpiprazole (6–16 weeks). Here, data were available only for patients older than 65 years.After the removal of studies with at least one domain with a high risk of bias (five studies), the results for CRWG changed only for ziprasidone . After excluding the high risk of bias studies, for iloperidone available data were not sufficient to perform an analysis (<6 weeks). For CRWL, the results did not change. For quetiapine (>38 weeks) and risperidone (<6 weeks), no more data were available. In the analyses of the other antipsychotics, the exclusion of these studies did not significantly affect our results (Supplementary Appendix 9).P-values were similar to the original analyses (Supplementary Files 10.1 and 10.3). For clozapine, the trim-and-fill analysis was unable to calculate pooled estimates. Funnel plots with low numbers of studies could not be interpreted. For CRWL, trim-and-fill added two studies for aripiprazole (6–16 weeks) and one study for asenapine (<6 weeks), risperidone (6–16 weeks) and placebo (16–38 weeks) with similar pooled estimates and P-values as the original analyses (Supplementary Files 10.2 and 10.4).Funnel plots and Egger tests showed some evidence of publication bias, specifically for antipsychotics with fewer studies (Supplementary Appendix 10). For CRWG, trim-and-fill added only a single study for asenapine (>38 weeks), chlorpromazine (<6 weeks), clozapine , risperidone (>38 weeks), second-generation antipsychotics (>38 weeks) and ziprasidone (16–38 weeks). Pooled estimates and The present results showed that almost all included antipsychotics were associated with CRWG. In addition, among the few antipsychotics with sufficient data, some were associated with CRWL. For some antipsychotics, CRWG was greater in antipsychotic-naive than in antipsychotic-switch patients. Moreover, a longer duration of antipsychotic use was associated with more CRWG, but not with CRWL. For some antipsychotics, CRWG was greater for patients diagnosed with schizophrenia, but this was not consistent.Because we analysed a wide variety of antipsychotics, CRWG per antipsychotic can be assessed. However, with the current methods it was impossible to compare CRWG between different antipsychotics. The reader should keep this in mind when reading different percentages for antipsychotics below. Network meta-analysis using the present data-set can enable those conclusions in the future . CRWG was severe for clozapine and olanzapine, with respectively 76.3% and 36.9% of patients gaining ≥7% body weight after 38 weeks of use. After prolonged use, paliperidone (18.3%), quetiapine (17.0%) and risperidone (23.0%) showed moderate percentages of CRWG, as did so-called metabolically friendly antipsychotics: aripiprazole (17.2%), amisulpride (20.6%), asenapine (16.4%) and haloperidol (22.8%). This may seem counterintuitive, as most studies and systematic reviews report the average weight gain in a study population, which may obscure the true magnitude of antipsychotic-induced weight gain. An average weight change is calculated by averaging weight gain in some patients and weight loss in others and may also be distorted by outliers in a small proportion of patients. When focusing on average weight change, some antipsychotics may appear metabolically friendly, i.e. on average do not lead to much weight gain, but this average does not apply to all patients. Thus, even if an antipsychotic is generally considered metabolically friendly and associated with limited weight gain or even CRWL in some patients, other patients using it may still experience CRWG. Proportions showing CRWG were relatively low for lurasidone (7.4%) and ziprasidone (7.6%) after 38 weeks of use. Proportions showing CRWG were similar to placebo (6.7%), but available data for lurasidone and ziprasidone were limited. Of the newer antipsychotics, brexpiprazole and cariprazine were promising for the short term, but little or no data were available for the long term .Visual inspection of 32 In contrast, our findings suggest that time is a relevant factor in antipsychotic-induced weight gain. Unfortunately, a majority of studies included in this meta-analysis focused on the short term , and only about 15% of the studies focused on the long-term. The availability of long-term data is limited and suggests that the true impact of long-term antipsychotic use on weight gain remains hidden. Conducting long-term studies or longitudinal studies in this patient population presents methodological and other challenges but is essential to obtain a complete picture of antipsychotic-induced weight gain. We believe that non-randomised studies will better answer this question. We plan to conduct a systematic review and meta-analysis on such studies in the near future. However, non-randomised studies also have methodological shortcomings.Previous systematic reviews and meta-analyses focused mainly on short-term weight gain or did not distinguish between short-term and long-term studies.32Our findings suggest that the duration of antipsychotic use is an important determinant of weight gain, highlighting the importance of monitoring weight gain and weight management strategies from the start and throughout antipsychotic treatment.CRWL was noticeable for aripiprazole (9.1%), lurasidone (12.6%) and ziprasidone (28.6%), but also olanzapine (10.8%) after 38 weeks of treatment. Except for ziprasidone, CRWL for these antipsychotic was similar to CRWL for placebo (12.0%). Data, however, are too limited to draw firm conclusions. For lurasidone and ziprasidone, long-term results were based on a single study . The eff56 A switch to so-called metabolically friendly antipsychotics  might also result in weight loss.25 In a recent meta-analysis of antipsychotic-switch studies, switching to aripiprazole and ziprasidone was associated with weight loss, but a switch to amisulpride, paliperidone, risperidone, quetiapine or lurasidone was not.57 However, switching to a metabolically friendly antipsychotic should be done with caution and always involves uncertainties, for instance about the effect on symptoms and side-effects. In addition, there is evidence that weight gain and other metabolic side-effects appear to be associated with more clinical improvement.58 Our meta-analysis shows modest CRWL for aripiprazole, lurasidone and olanzapine, comparable with placebo. Surprisingly, although olanzapine is known to be associated with weight gain, and the present data showed that the proportion of patients showing CRWG increased, in other patients CRWL increased over time. However, this result was strongly influenced by one long-term study with a 2-year follow-up, comparing long-acting injection and oral administration, but with no significant differences between both groups.59 Ziprasidone appears promising for long-term CRWL, but these results were based on one study46 and need replication in larger patient populations to draw firm conclusions. Based on these results, there is insufficient evidence to recommend switching antipsychotic as a weight management strategy.Recently, much research has been done on strategies to prevent or counteract antipsychotic-induced weight gain. Behavioural therapies, exercise programmes, dietary counselling and pharmacological interventions such as metformin and topiramate may have a moderate effect on reducing weight gain.56In contrast to current guidelines that recommend switching to a metabolically friendly antipsychotic as the first step in controlling antipsychotic-induced weight gain, we strongly recommend considering alternative strategies for losing body weight. Focus first on individual lifestyle counselling and exercise interventions, possibly supplemented with metformin, topiramate or bupropion, before switching to another antipsychotic.25 However, this distinction is relevant because antipsychotic-naive patients are more likely to develop antipsychotic-induced weight gain, as previous meta-analyses31 have shown. Moreover, weight gain in antipsychotic-naive patients is much more indicative of the effect of a specific antipsychotic on weight, whereas in switch studies the effect of a previous antipsychotic can certainly not be excluded for the short term and therefore leads to bias.60 We could include only a few studies that assessed CRWG and no studies that assessed CRWL in an antipsychotic-naive population. We excluded multiple studies that had data on 7% weight change but were either not randomised, used a per-protocol analysis or included patients younger than 15 years. Antipsychotic-naive patients taking aripiprazole, olanzapine and quetiapine showed significantly more CRWG than switch patients 24 or analysed data at the individual patient level.30Second, this meta-analysis was intended to give an overview of individual antipsychotic drugs, not to compare them. With the analysis method we used, comparisons between interventions (including placebo) were not possible. Direct comparison of weight change between antipsychotics would require novel analysis methods such as a network meta-analysis. Given the stratified structure of the data, i.e. studies were included with multiple time points and multiple antipsychotics, and the heterogeneity of the included studies and patient groups, this was not possible within the scope of the current meta-analysis.28 A systematic review or meta-analysis based on cohort studies could therefore provide a better understanding of the long-term effects of antipsychotics on weight gain in large study populations. Meta-analyses such as the present one including only RCTs and meta-analyses including observational studies both have their own merits. Since both types of meta-analysis are needed to obtain a complete overview, a meta-analysis including observational studies is needed in the near future.Third, we included only randomised studies in this meta-analysis because this design has the lowest risk of bias and the highest methodological quality at the expense of ecological validity. We excluded multiple cohort studies, which often follow large study populations for a prolonged period. The majority of studies included in this meta-analysis were of relatively short duration, whereas our results clearly showed that weight change continues even after 38 weeks of treatment. Cohort studies also include patients with comorbidities and co-medication often excluded in RCTs.62Fourth, in this meta-analysis we could include only a handful of studies that examined clinically relevant weight change in antipsychotic-naive patients. Studies in antipsychotic-naive patients were more often excluded. Therefore, results should be interpreted with caution. It is clear that more research is needed in the antipsychotic-naive population. Research on antipsychotic-induced weight gain in this population is highly relevant because, as first-time users, previous use of an antipsychotic does not affect weight gain. Moreover, they are more vulnerable to excessive weight gain because of their lower age and lower BMI.72 New original research could shed more light on this. We could not correct for antipsychotic drug use before participation in the trials. As most studies included here are antipsychotic-switch studies, a recording of the pre-switch antipsychotic would have been important. However, these pre-switch antipsychotics were not given or were listed as a percentage of which antipsychotic was previously prescribed. Except for a recent meta-analysis on antipsychotic switching,57 pre-switch medication is usually not included in meta-analyses. Interpreting the data is arduous32 and underscores the relevance of investigating antipsychotic-induced weight gain in an antipsychotic-naive population.Furthermore, a meta-analysis using data reported in original papers can assess only what the studies analysed. Results controlled for confounders (such as age and baseline BMI) and results stratified by a modifier (such as antipsychotic dose) can be extracted only when the original studies reported them. For example, antipsychotic dose differed within one arm of RCTs and therefore antipsychotic dose could not be analysed as a modifier. Only when the RCTs included study arms with different dosages or when the dosage differed between studies could this be analysed in our meta-analysis. For some antipsychotics , preliminary evidence indicates a dose–response relationship between serum concentrations and metabolic outcomes; for other antipsychotics, the evidence for such a dose–response relationship is limited.The present meta-analysis aimed to be comprehensive and thus to include all RCTs. The RCTs were not restricted to a specific diagnosis, age group or study duration. Despite the fact that all antipsychotics were analysed separately, the data showed a high degree of heterogeneity and results should therefore be interpreted with caution. However, because of the heterogeneity, the data were analysed using the random-effects option.31 Another meta-analysis excluded Chinese studies because of a higher risk of methodological shortcomings.24 We did include studies from China written in English. We did evaluate those articles as methodologically sound and they had all been published in peer-reviewed journals.We included only articles written in English because it would be difficult to judge the methodological and outcome properties otherwise. A few studies from China or other Asian countries were therefore excluded. A meta-analysis found Asian patients to have a lower BMI and lower antipsychotic-induced weight gain than Western patients.post hoc trim-and-fill analyses were performed to control for this, but the pooled estimates and P-values were similar to the original analyses.Finally, we did not include unpublished work in our meta-analysis, and studies with null findings tend to remain unpublished. We checked poster presentations and conference abstracts to partly overcome this. We found some evidence of publication bias, particularly for antipsychotics with few available studies. Where possible, 57 Clinically relevant weight gain is more pronounced in antipsychotic-naive patients, which emphasises the importance of immediate follow-up. Prevention of antipsychotic-induced weight gain and subsequent complications ultimately appears to be the best strategy and should be the focus of treatment. Careful consideration of possible side-effects of antipsychotics and individual risk factors for increased vulnerability to weight gain is crucial before initiating antipsychotic treatment.Switching antipsychotics can lead to both clinically relevant weight gain and weight loss, although weight gain is more prominent. Switching to a metabolically friendly antipsychotic may help patients to lose some weight, but it does not appear to be very effective in substantially reducing body weight and carries the risk of worsening symptoms.


Data from 110 patients were available. In all, 48 patients were included pre-COVID-19 and 62 during the COVID-19 pandemic. The authors found no significant difference between pre-COVID-19 prevalence of distress (p = 0.112), anxiety (p = 0.385) or depression (p = 0.084). Regression analyses additionally did not show any significant influence of COVID-19 on the above analysed parameter. Analyses of our cohort’s data could not underline the negative impact of COVID-19 restrictions, shortcuts of professional and remodelled caregiver support on psycho-oncological outcomes.Up to 40% of neuro-oncological patients already deal with high levels of distress under conventional circumstances. Due to COVID-19, pandemic hospital visitor rules have been restricted and patients did not receive the same level of supporting caregiver network as before COVID. The aim of the present study was to analyse the impact of the COVID pandemic on the prevalence of distress, anxiety and depression in neuro-oncological patients. Patients admitted for brain tumour surgery were screened regarding distress, anxiety and depression. Furthermore, aspects of patients’ quality of life and clinical data were covered. Retrospectively available data of patients treated pre-pandemic (group A) and throughout the COVID-19 pandemic (group B) were statistically analysed using Chi-square tests and independent-sample  Stress, also known as psychological distress, results in symptoms such as anxiety and depression and reveals itself when people experience extraordinary emotional pressure. There are external and internal stressors triggering psychological stress, e.g., serious health issues such as cancer diagnosis can also act as a stressor. Levels of distress, anxiety and depression are known to be increased in cancer patients. Up to 35% of cancer patients suffer from distress ; additioIn the subgroup of neuro-oncological patients, prevalence of distress is even higher with ranges from 38 to 52% . PrevaleBy releasing stress hormones and glucocorticoids, the body tries to cope with the situation but simultaneously worsens the situation when there is a cancer disease. Studies revealed that in the body’s fight-or-flight reaction with the release of norepinephrine, a stimulation of metastasis  can be fTherefore, psycho-oncological screening for diagnosis of increased distress, anxiety or depression is of high relevance, as well as a support system to facilitate a psycho-oncological support.In the last years, huge effort has been made to install screening instruments for everyday use and cancer centres all over the world have developed professional support systems for their patients.During the Corona pandemic, treatment capacities were reduced in all areas of medical treatment in order to create treatment capacities for COVID patients and a decrease in the risk of infection. That has led to devastating, effects especially on cancer patients. A systematic review published in 2021 identified 38 categories of delays or interruption of treatments for cancer patients, e.g., chemotherapy, surgery and radiotherapy, and underlined that a delay in cancer treatment without specifying any category was observed in 5–52.6% . In GermIn addition to the elimination of professional supportive systems, contact restrictions that were introduced to the hospitals at the beginning of COVID-19 pandemic led to a reduction in family support as this was only available at restricted visiting hours or, throughout some periods of the pandemic, reduced to digital communication devices.The COVID-19 pandemic has had an extensive effect on worldwide medical care. With the present study, we aimed to answer the question if COVID-19 had an objectifiable negative impact on psycho-oncological screening parameters, focused on distress, anxiety and depression scores, in neuro-oncological patients. Furthermore, we analyzed the impact if tumour entity or special patients’ situations such as recurrent diagnosis had any influence on the psycho-oncological screening parameters under the light of the COVID-19 pandemic.In this retrospective single-center analysis , we investigated the impact of the COVID-19 pandemic on the psycho-oncological distress of brain tumour patients. The study was approved by the local ethical committee (Study Number 4087). Reporting of this study aimed to strengthen the reporting of observational studies in the epidemiology (STROBE) guidelines for observational studies .Inclusion criteria for analysis were as follows: (1) patients age > 18 years with the diagnosis of a brain tumour of any malignancy (2) who were electively admitted for tumour surgery at our neuro-oncological department at University hospital Duesseldorf between (3) January 2019–September 2020.For further analysis, patients were divided into two subgroups. Subgroup A included patients who were screened before the onset of the first wave of the COVID-19 pandemic between March 2019–September 2019. Subgroup B were patients who underwent screening during the COVID-19 pandemic and federal lockdown measures in Germany between March 2020 and September 2020. Epidemiological information on patients of both groups is summarized in All patients were screened during hospitalisation with the further described tablet-based questionnaire instruments. In case of positive screening results by means of increased scores concerning the used screening items, or in case patients did objectively not have any increased needs but asked for professional support, we introduced the psycho-oncological team to the patients. Patients were instructed in the use of tablets and accompanied during completion by our neuro-oncological team. The following screening questionnaires were used. All of them have been widely discussed in the literature and proved feasibility for screening of psycho-oncological distress ,19,20,21The DT is part of the NCCN Guidelines as an easy to administer self-reporting tool with a rating scale ranging from 0 (no distress) to 10 (extreme distress). According to the NCCN guidelines, a DT score of 5 or above is an indicator for distress. Additionally, the DT also contains a list of 40 symptoms representing practical, family, emotional, spiritual-religious and physical concerns. The items in the list provide information about reasons for stress and technical responsibility. There are no systematic data for re-test reliability available as the DT is a visual analogue scale that mostly was used in validation studies with cross-sectional study design.The HADS has been established as an effective screening tool for the assessment of anxiety and depression. It is a 14-item self-report questionnaire comprising 7 items used to identify anxiety (HADS-A) and 7 items to identify depression (HADS-D). Each item has a 4-point (0–3) Likert-type scale, and the maximum score on each subscale is 21 points. A cut-off score of >8 is assumed to be optimal concerning sensitivity and specificity in defining anxiety disorders in patients ,23. OverThe EORTC QLQ-C30-BN20 is a disease-specific questionnaire developed by the European Organization for Research and Treatment of Cancer (EORTC) to assess the quality of life of cancer patients comprising the QLQ-BN20, which consists of 20 questions specifically assessing brain tumor-related symptoms. For our analyses, we took a deeper look into the following items: social-functioning, cognitive-functioning, emotional functioning, role functioning, physical functioning and global health status. We chose to include this aspect because previous studies showed that distress and health-related quality of life strongly correlate .t-tests . Furthermore, multiple linear regression analysis was performed to explore the impact of additional possible factors  on dependent variables. For correlation of diagnosis and recurrent disease status, the Pearson correlation coefficient was calculated. Statistical significance was set at p < 0.05 and all tests were performed two-sided. Computerized analyses were conducted using IBM SPSS Statistics Version 26 .Descriptive analyses were performed to investigate patients’ sociodemographic and clinical characteristics. Chi-square tests were performed to assess the impact of COVID on nominal variables . Metric variables were analysed by independent-sample If data were missing e.g., as described further on in the results The present retrospective study only reports data that were collected at our local neurosurgical ward. Data that are directly accessible for research associates are allowed to be used in scientific context without previous informed consent according to local legislation. Due to anonymization, discerning the identity of individual persons is impossible. Data collection and analyzation included in this study were approved by the local ethical committee, Heinrich-Heine University, Faculty of Medicine, Düsseldorf, Germany (Study number: 4087).Overall, data from 110 patients could be included in the present analyses. In all, 49 patients were male (45%), 61 female (55%). Mean age of the cohort was 59.63 [±14.23 SD]. Overall, 50 patients were diagnosed with high-grade glioma (46%), 9 with low-grade glioma (8%) and 22 patients suffered from cerebral metastases (20%). Moreover, 29 patients were hospitalised due to cerebral lesions of another entity (26%). Mean time from primary diagnosis to data acquisition (screening) was 1.86 years [±3.35 SD].n = 16, 33%), 6 patients from low-grade glioma (13%) and 10 patients were treated due to cerebral metastases (21%). Overall, 33% were hospitalised due to other cerebral entities such as meningioma and pituitary adenoma (n = 16).Patients of group A were screened pre-first wave of the COVID-19 pandemic in Germany between 19 March and 19 September . In this group, about one third suffered from high-grade glioma ; furthermore, we included 3 patients with low-grade glioma (5%). In all, 12 patients had been diagnosed with cerebral metastases (19%) and 21% were treated because of other entities such as in group A . Primary and recurrent diagnoses were reported for all 31 patients (50% and 50%).Patients in group B were screened during the first wave of COVID-19 between 20 March–20 September; here, we included 62 patients  with a slightly higher mean age of 61.76 [±13.68 SD]. More than half of the patients were treated due to high-grade glioma  = −0,40, p = 0.688, and physical functioning as indicated by the KPS score χ2(2) = 3.76, p = 0.152.All patients were either directly treated post-operatively at the neurosurgical normal ward or stayed at the recovery ward. None of the patients were treated at our intensive care unit. Group comparison using independent-sample p = 0.112) did not significantly differ between both groups.In group A, pre-COVID-19 pandemic, 29 patients indicated increased distress (60%). In comparison, 28 patients in group B (during the COVID-19 pandemic) complained about distress (45%). The prevalence of the distress (p = 0.385) and depression (p = 0.084) did not differ significantly between patients screened pre- or during the COVID-19 pandemic.The same results could be obtained for prevalence of anxiety and depression. Overall, 10 patients from group A reported increased anxiety (21%) and 6 depression (13%), whereas 9 patients of subgroup B (15 %) reported anxiety and 16 depression (26 %). Therefore, prevalence of anxiety . The same results were obtained when correlating the status of recurrent disease with the DT .Diagnosis regardless of tumour entity had no significant influence on the distress thermometer in both groups . In comparison, the mean score in group B was 5.89 [±4.01 SD] for anxiety and 7.04 [±5.01 SD] for depression. Neither of both results reached significance  . In comparison to group A, data of 59 patients in group B were available of whom 20 patients required professional support (32%). The difference in demand between both groups was not significant ( = 0.78) .We aimed to analyse differences between both subgroups concerning social-functioning, cognitive-functioning, emotional functioning, role functioning, physical functioning and global health status. Neither of the analysed items reached significance when the pre-COVID data and the data obtained under the COVID-19 lockdown were compared. Due to the lack of data in group B, analyses of group B data only comprised 37 patients. The results are summarized in For clinical evaluation, we used the Karnowski Performance Index (KPS). A KPS of 100–90 was seen in 87 patients pre-op and 85 patients post-op, an index of 80–70 in 21 subjects pre- and post-operatively and a KPS of <70 % was seen in 2 patients pre-op and 4 patients post-op. The KPS did not differ significantly between both timepoints (pre and post-op). The correlation of the KPS and the scoring in the DT and HADS did not show significant correlation between clinical status and one of the analysed scoring parameters neither pre- nor post-op.The COVID-19 pandemic had and still has enormous influence on patient care in hospital and outpatient department settings. In our study, we aimed to analyze if there had been a significant impact on the psycho-oncological screening outcome in neuro-oncological patients during the first wave of the COVID-19 pandemic due to restrictions in professional patient care and family supportive systems.In our cohort, prevalence of distress and anxiety did not significantly differ in neuro-oncological patients who were screened pre-COVID and post-COVID first wave. We could observe, even though results were not statistically significant, that mean scores for both screening parameters were higher in the pre-COVID screening group. On the contrary, depression prevalence and mean scores in the depression screening were higher during the COVID-19 screening than before the start of the pandemic in 2020, and the results only narrowly failed to reach significance.In line with our results, an online cross-sectional study, not focusing on patients but standard population with 15.000 participants in Germany published in 2020, showed that prevalence of depression increased under COVID-19 from 5.6 % to 14.3 % . In anotIn our cohort age, recurrent diagnosis and entity of diagnosis, either malignant or benign, had no influence on the psycho-oncological outcome of the patients independently of screening timepoint. However, we actually had expected some higher distress levels in patients with recurrent diagnosis as there are specific timepoints reported in the literature in neuro-oncological patients where an increased distress was observed, especially during hospitalisation as well as at the time point of tumour recurrence , which mIn our additional analysis of specific items of the quality-of-life questionnaire, we found that there was no statistical difference in social-functioning, cognitive-functioning, emotional functioning, role functioning, physical functioning and global health status in both of our subgroups; and interestingly, under COVID-19, patients did not ask for professional support more often under COVID-19.Our data show that circumstances that came along with the first wave of COVID-19 did not have significantly negative effects on the psycho-oncological outcome of our neuro-oncological patients.In consideration of what we have learned from recent studies, these results were somehow surprising to the authors. We know that neuro-oncological patients tend to have higher distress levels when they are hospitalized or in recurrent situations. We hypothesized that under COVID-19 these effects would have been even more increased. On the contrary, our cohort did not show significant differences in both groups and recurrent diagnoses did also not correlate with higher scoring results.There are some points that might have contributed to that fact the authors would like to point out.Firstly, patients who are diagnosed with brain tumors are burdened due to the diagnose to such an extent and focused so much on that mostly felt life-threatening incident that further external influences might not play such a great role to them. Taking that into consideration, maybe the patients’ relatives were the ones who suffered from COVID-19 restrictions even more due to strict visiting hours or even visiting interdictions and the subjective feeling to be even more helpless due to these special circumstances.Another aspect of the patient relative relationship is that we have learned from different studies that used Fear of Progression Questionnaires (FoP-Q) that both parts tend to the mutual burden each other. One can hypothesize that the more distant relationship under the COVID-19 pandemic might have a positive influence on some patients’ disease and therapy perception.On the contrary, the restrictions of the first wave of the COVID-19 pandemic unarguably have led to enormous changes for hospitalized patients, particularly when it comes to availability of family support systems, especially for those patients who strongly depend on family care structures. In our department, we aimed to connect closely with patients’ families via daily phone calls and encouraged patients and families to make use of multi-media devices to keep up their daily contact.The use of these tools to overcome the visiting restrictions as well as the engagement of additional urgent professional support whenever special needs were diagnosed might have contributed to the reported results, which we evaluate as positive findings under the given circumstances.Regarding sensitivity and specificity of the used measures for the diagnosis of certain psychiatric diseases such as anxiety and depression, there are different cut-off values published and used in the literature. That might lead to different numbers of diagnoses in patient cohorts when different cut-offs are used. Nevertheless, in the present study, the questionnaires were only used as screening instruments not as a diagnostic tool, and there is a wide range of published data agreeing about feasibility as screening instruments.At last, concerning the requirement of psycho-oncological support and EORTC-C30-BN20 datasets, we can only report analysis of limited datasets as there were only 27 instead of 48 datasets in group A and only 59 instead of 62 patients’ datasets available for group B  and only 37 instead of 62 datasets in the EORTC-Bn20 screening for group B. A further limitation is the arguably very specific and small subgroup of neuro-oncological patients who also split into further subgroups of benign and malign diagnoses. Nevertheless, as we could show that the entity of diagnosis had no significant influence, our results can be conveyed to the subgroup of neuro-oncological patients. However, the small number of patients could also have contributed to our findings reported above.Here, we present data of a very special group of cancer patients. In the present cohort of neuro-oncological patients, the impact of COVID-19 restrictions and shortcuts of professional and remodelled caregiver support did not have a significant influence on psycho-oncological outcome in terms of distress, anxiety or depression in the cohort. Our data show that a dedicated communication and a very close patient and patients’ family guidance might help to overcome gaps in professional psycho-oncological support even in very special situations such as we have experienced in the past two years.


The pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) led to the coronavirus disease 2019 (COVID-19) pandemic that drastically impacted the United States. The evidence was not clear on how SARS-CoV-2 infection impacted children, given the high prevalence of SAR-CoV-2 infection. Febrile infants less than 60 days old are an ongoing challenge to risk-stratify for serious bacterial infection (SBI), including urinary tract infection (UTI), bacteremia, and meningitis. We hypothesized there would be a lower rate of SBI in SARS-CoV-2 positive febrile infants compared to those SARS-CoV-2 negative.This was a retrospective chart review with a nested, age-matched, case-control study performed from March 2020–June 2021. Infants less than 60 days old presenting with fever were assigned groups based on SARS-CoV-2 infection. Blood, urine, and cerebrospinal fluid cultures were used as the gold standard to diagnose SBI. We compared overall rate of SBI as well as individual rates of SBI between each group. We performed a subgroup analysis evaluating the age group 29–60 days old.A total of 164 subjects met criteria for analysis: 30 COVID-19 positive and 134 COVID-19 negative subjects. Rate of SBI was 17.9%  in the COVID-19 negative group compared to 0% (95% CI: 0.0%-11.1%) in the COVID-19 group, which demonstrated statistical significance (p = 0.008). In the age-matched data, we found statistical significance for any SBI (p = <0.001). For individual rates of SBI, we found statistical significance for UTI (p = <0.001) and bacteremia (p = <0.001). The 29–60 days-old subgroup analysis did not achieve statistical significance (p = 0.11).This study demonstrated the utility of including SARS-CoV-2 infection as part of the risk stratification of febrile infants less than 60 days old. While overall there is a low incidence of bacteremia and meningitis in this age group, these results can contribute to existing literature and potentially help decrease invasive testing and exposure to broad-spectrum antibiotics. In early March 2020, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a novel single-stranded RNA virus, led to a pandemic of coronavirus disease 2019 (COVID-19).4Infants less than 60 days old presenting to the PED with fever are some of the most vulnerable patients due to their risk for serious bacterial infections (SBI), which include urinary tract infections (UTI), bacteremia, and bacterial meningitis. The reported rates of SBI for infants less than 60 days old ranges from 7.0–12.5% for low-risk infants to as high as 20% for high-risk infants.7Recently, the American Academy of Pediatrics (AAP) updated its guidelines for the evaluation and management of febrile infants 8–60 days old to further clarify risk stratification, creating new age-group definitions.12The emergence of a new infectious disease presents challenges in the risk assessment of any patient, but particularly in this age group. Our goal in this paper was to identify the rate of SBI in febrile infants less than 60 days old presenting to the ED who were COVID-19 positive and then compare it to the rate of SBI in febrile infants less than 60 days old who were COVID-19 negative. We hypothesized that infection with SARS-CoV-2 in febrile infants ≤60 days-old would be associated with a lower risk of SBI than febrile infants ≤60 days old without SARS-CoV-2 infection.This was a retrospective cohort study of febrile infants ages 60 days old or less who presented to the PED. Fever was considered any rectal temperature of 38°C (100.4°F) or greater in the prior 24 hours either at home or during the PED visit. The study period encompassed March 2020–June 2021. We identified patients by querying the electronic health record (EHR) for all infants ≤60 days old who presented to the PED during the defined study period. We reviewed each variable and how to extract the information from the subject’s chart. Patients were excluded if they were afebrile at home and during the PED visit, had received antibiotics within 48 hours of arrival to the PED, had a recent diagnosis of SBI, gestation age less than 35 weeks, or comorbidities such as congenital heart disease, chronic lung disease, or ventriculoperitoneal shunts, which place those patients at higher risk of complicated illness.What do we already know about this issue?The impact of COVID-19 on children has been less severe compared to adults, but the impact on high-risk, febrile neonates has not been clearly documented.What was the research question?What impact did concurrent COVID-19 infection on febrile neonates less than 60 days old have on serious bacterial infections?What was the major finding of the study?Rate of SBI for concurrent COVID-19 infection was 0% (95% CI 0–11%), statistically significantly lower (p = 0.008) than 18% (12–26%) found in COVID-19 negative patients.How does this improve population health?Febrile neonates are at high risk for serious bacterial infection. COVID 19 positive neonates may have a lower incidence of SBI, and allow for improved risk stratification and management.The PECARN and AAP guidelines vary in their recommendations on the evaluation and management of these infants, including laboratory studies, treatment, and disposition from the PED.International Classification of Diseases, 10th Revision, code diagnoses, length of stay if admitted to the hospital, and antibiotic therapy if treated with at least one dose. Laboratory data collected included white blood cell count, absolute neutrophil count, procalcitonin, urinalysis, urine culture, blood culture, CSF culture, respiratory viral panel (RVP), SARS-CoV-2 result, and chest radiograph result. The primary outcome was the presence of SBI, specifically urinary tract infection (UTI), bacteremia, or bacterial meningitis. A UTI was defined as a urine culture with at least 50,000 colony-forming units per milliliter of a single pathogen obtained via sterile catheterization. Bacteremia and bacterial meningitis were defined as growth of a single pathogen on either blood or CSF culture. We did not consider enteritis as part of SBI due to the low incidence at our institution and the lack of routine stool cultures.Data was collected directly from the EHR and stored in Microsoft Excel . Data collected included demographics, chief complaint, preceding signs and symptoms, maximum temperature, ED disposition, Subjects were assigned to their respective group based on SARS-CoV-2 result. We analyzed the rate of overall SBI between COVID-19 positive and COVID-19 negative patients. A subgroup analysis was also performed examining the rate of each SBI  and the rate of invasive bacterial infections (IBI), which is defined as bacteremia and bacterial meningitis. We performed a nested, age-matched (+/− 2 days) case-control analysis on the data. An age-specific group of 29–60 days old analysis was completed comparing the rate of SBI between COVID-positive and negative groups. Finally, we completed an analysis in which subjects were assigned to viral infection positive or viral infection negative groups based on RVP results. The rate of any SBI, each individual SBI , and IBI were compared between the two groups.We summarized all continuous variables with medians and interquartile ranges, and categorical variables were summarized with frequency and count. We used the Mann-Whitney U test to compare continuous variables. Chi-square or Fisher’s exact test was used when comparing categorical variables. For the matched data analysis, we performed McNemar’s test. All statistical analysis was performed using SPSS statistical software  Alpha was set at 0.05. This study was approved under expedited review by the institutional review board at MMC.We pulled 591 charts from the EHR that met age criteria. A total of 174 subjects met fever criteria for data collection. Of those, 10 subjects were removed because of absence of a SARS-CoV-2 result. No subject was excluded due to having at least one bacterial culture. All infants had urine and blood collected, while 49 (29.9%) of the patients did not have CSF collected. A complete RVP was not always readily available, and 23 patients (14.0%) did not receive a complete RVP. A total of 164 patients were included in the statistical analysis: 30 COVID-19 positive subjects and 134 COVID-19 negative subjects see . SubjectP > 0.05) for any SBI, each of the individual SBI, and IBI. The results are presented in The rate of SBI in the COVID-19 negative group was 17.9%  compared to 0% (95% CI: 0.0%-11.1%) in the COVID-19 positive group. Of the SBI in the COVID-19 negative group, 21 subjects had UTI, five had bacteremia, and two had meningitis. For the IBI, three cases of bacteremia were associated with a concurrent UTI, and one case of meningitis was associated with bacteremia and UTI. There was a statistically significant lower rate for any SBI, and specifically for UTI, in the COVID-19 positive group compared to the COVID-19 negative group. When evaluating those with IBI, we found no statistical significance between the COVID-19 positive and COVID-19 negative groups. The results are presented in P-value was less than 0.05 between all groups except for meningitis as there were no cases in either group (see For the age-matched analysis, a total of 30 COVID-19 negative patients were matched to the 30 COVID-19 positive patients. There were zero SBI found in the COVID-19 positive group and a total of six SBI in the COVID-19 negative group. The number of individual SBI was six UTI, one bacteremia, and zero meningitis. Comparing these two groups, the roup see .The last analysis compared SBI rates between patients with any viral infection on RVP vs those with a negative RVP. A total of 21 SBI were found in those with a negative RVP and three SBI in those with concurrent viral infection. When comparing the positive RVP group to the RVP negative, there was a statistically significant lower rate of SBI in the positive RVP group. When looking at the specific SBI, there remained only a statistically significant lower rate of UTI in the positive RVP group compared to the negative RVP group. Of note, there were also seven cases of viral meningitis, which were not included in or calculated as part of the patients with meningitis as this investigation was looking exclusively at IBI. All the patients who tested positive for viral meningitis had a negative bacterial culture and were COVID-19 negative.The COVID-19 pandemic challenged us with a new disease as well as how to risk-stratify a COVID-19 positive, febrile infant less than 60 days old. Infants less than 60 days old are a vulnerable population for SBI and often need invasive evaluation and treatments. There has been progress in risk-stratifying these infants to avoid more invasive and unnecessary testing. A concurrent viral infection such as RSV has been shown to place febrile infants at a lower risk for SBI.Our results are promising and in line with other published studies that have attempted to address this question. There were zero SBI in any of the COVID-19 positive subjects, and the rate of SBI in the COVID-19 negative subjects is consistent with the rate of SBI in the United States. Moreover, we were able to show a statistically significant difference in any SBI infection between the COVID-19 negative group and COVID-19 positive group. This was in large measure driven by the rate of UTI. The incidence of bacteremia and meningitis has fallen significantly in the US. While the low rate of IBI in the population makes it difficult to obtain findings of statistical significance, we were able to complete a matched analysis that demonstrated a statistically significant lower rate for any SBI and a statistically significant lower rate of UTI and bacteremia in the COVID-19 positive group.Escherichia coli, Enterococcus faecalis, and Klebsiella pneumoniae, while in bacteremia and meningitis the organisms were Escherichia coli and Group B Streptococcus. These findings are parallel to the current published data.The overall rate of SBI we found in this investigation is similar to the findings in previously published data.Infants 29–60 days old are of importance as risk stratification based on preliminary laboratory data and urinalysis determines whether more invasive testing with a lumbar puncture is indicated.The AAP’s recently updated guidelines enhance our ability to risk stratify these infants. These guidelines created additional age-group classifications and include specific laboratory studies for inflammatory markers to consider, such as absolute neutrophil count, procalcitonin, and C-reactive protein.There are several limitations that exist with this study. Retrospective studies are not as strong as prospective studies. However, in this global pandemic with the development of a new illness, it is important to share what is known. It was performed at a single study site, which has inherent limitations including smaller sample size, and which led in turn to an extension of our initial time frame to include more patients. Moreover, our sample size was further reduced due to lower pediatric ED volumes since the beginning of the pandemic. The small sample size limited our ability to describe statistical significance when it comes to bacteremia or bacterial meningitis. Nonetheless, we argue that lack of any bacteremia or bacterial meningitis in the COVID-19 positive cohort is clinically significant.Lastly, our institution does not routinely screen for invasive intestinal illness given the low prevalence in our community, which may have resulted in missed SBI. Our study highlights the need for additional work to further evaluate and better risk stratify these vulnerable patients, particularly in the context of concurrent viral illnesses. Furthermore, the understanding of the effect of SARS-CoV-2 continues to evolve. The impact that it has had on the pediatric patient continues to increase as new information comes to light.This investigation contributes to our understanding of the impact of concurrent viral infections on the rate of serious bacterial infection in febrile infants. As advancements in medicine and research evolve, we will improve upon risk-stratification tools for this vulnerable population. Despite the low incidence of SBI in this population, the mortality and morbidity remain significant. While limitations exist within our results and sample size, we believe our study provides a framework to continue the discussion and drive future research on this topic. Several challenges remain in the creation of clear guidelines to risk stratify febrile infants less than 60 days old. We hope that each investigation will contribute to an ever-growing body of knowledge that will establish clear, evidence-based practice for these clinically challenging patients.