Illumina and nanopore sequencing technologies are powerful tools that can be used to determine the bacterial composition of complex microbial communities. In this study, we compared nasal microbiota results at genus level using both Illumina and nanopore 16S rRNA gene sequencing. We also monitored the progression of nanopore sequencing in the accurate identification of species, using pure, single species cultures, and evaluated the performance of the nanopore EPI2ME 16S data analysis pipeline. Fifty-nine nasal swabs were sequenced using Illumina MiSeq and Oxford Nanopore 16S rRNA gene sequencing technologies. In addition, five pure cultures of relevant bacterial species were sequenced with the nanopore sequencing technology. The Illumina MiSeq sequence data were processed using bioinformatics modules present in the Mothur software package. Albacore and Guppy base calling, a workflow in nanopore EPI2ME (Oxford Nanopore Technologies—ONT, Oxford, UK) and an in-house developed bioinformatics script were used to analyze the nanopore data. At genus level, similar bacterial diversity profiles were found, and five main and established genera were identified by both platforms. However, probably due to mismatching of the nanopore sequence primers, the nanopore sequencing platform identified Corynebacterium in much lower abundance compared to Illumina sequencing. Further, when using default settings in the EPI2ME workflow, almost all sequence reads that seem to belong to the bacterial genus Dolosigranulum and a considerable part to the genus Haemophilus were only identified at family level. Nanopore sequencing of single species cultures demonstrated at least 88% accurate identification of the species at genus and species level for 4/5 strains tested, including improvements in accurate sequence read identification when the basecaller Guppy and Albacore, and when flowcell versions R9.4 (Oxford Nanopore Technologies—ONT, Oxford, UK) and R9.2 (Oxford Nanopore Technologies—ONT, Oxford, UK) were compared. In conclusion, the current study shows that the nanopore sequencing platform is comparable with the Illumina platform in detection bacterial genera of the nasal microbiota, but the nanopore platform does have problems in detecting bacteria within the genus Corynebacterium. Although advances are being made, thorough validation of the nanopore platform is still recommendable.

Depth of invasion (DOI) is the most important predictor for lymph node metastasis (LNM) in early stage (T1-T2) oral cancer. The aim of this study is to validate the cut-off value of 4 mm on which the decision to perform an Elective Neck Dissection (END) is made. We performed a retrospective study in patients with pathologically proven early stage oral cavity squamous cell carcinoma (OCSCC) without clinical or radiological signs of LNM, who were treated between 2013 and 2018. An END was performed when DOI was ≥ 4 mm and a watchful waiting protocol was applied in patients with DOI < 4 mm. Three hundred patients were included. END was performed in 77% of patients with DOI ≥ 4 mm, of which 36% had occult LNM (pN+). Patients in the watchful waiting group (48%) developed a regional recurrence in 5.2% for DOI < 4 mm and 24.1% for DOI ≥ 4 mm. For DOI ≥ 4 mm, regional recurrence free survival was higher for patients who were treated with END compared to watchful waiting (p = 0.002). A Receiver-Operator-Curve -analysis showed that a DOI cut-off value of 4.0 mm was the optimal threshold for the prediction of occult LNM (95.1% sensitivity, 52.9% specificity). A DOI of ≥ 4 mm is an accurate cut-off value for performing an END in early stage OCSCC. END results in higher survival rates and lower regional recurrence rates in patients with DOI ≥ 4 mm.

Background: Increasing evidence suggests that sarcopenia and a higher systemic immune-inflammation index (SII) are linked with morbidity in patients with COPD. However, whether these two conditions contribute to all-cause mortality in middle-aged and older patients with COPD or asthma is unclear. Therefore, we investigated the association between sarcopenia, SII, COPD or asthma and all-cause mortality in a large-scale population-based setting.Methods: Between 2009 and 2014, 4482 participants (aged >55 years; 57.3% female) from the population-based Rotterdam Study were included. COPD and asthma patients were diagnosed clinically and based on spirometry. Six study groups were defined according to the presence or absence of COPD or asthma and sarcopenia. Cox regression models were used to assess all-cause mortality in the study groups, adjusted for sex, age, body mass index, SII, smoking, oral corticosteroid use and comorbidities. In addition, all participants were categorised into sex-specific quartiles of SII, and mortality in these groups was compared.Results: Over a median follow-up of 6.1 years (interquartile range 5.0–7.2 years), 466 (10.4%) persons died. Independent of the presence of sarcopenia, participants with COPD had a higher risk of all-cause mortality (hazard ratio (HR) 2.13, 95% CI 1.46–3.12 and HR 1.70, 95% CI 1.32–2.18 for those with and without sarcopenia, respectively). Compared to lower SII levels, higher SII levels increased mortality risk even in people without sarcopenia, COPD or asthma.Conclusion: Middle-aged and older people with COPD, higher SII levels or sarcopenia had an independently increased mortality risk. Our study suggests prognostic usefulness of routinely evaluating sarcopenia and SII in older people with COPD or asthma.

Objectives: This systematic review aims to provide further insights into the conduct and reporting of clinical prediction model development and validation over time. We focus on assessing the reporting of information necessary to enable external validation by other investigators. Materials and Methods: We searched Embase, Medline, Web-of-Science, Cochrane Library, and Google Scholar to identify studies that developed 1 or more multivariable prognostic prediction models using electronic health record (EHR) data published in the period 2009–2019. Results: We identified 422 studies that developed a total of 579 clinical prediction models using EHR data. We observed a steep increase over the years in the number of developed models. The percentage of models externally validated in the same paper remained at around 10%. Throughout 2009–2019, for both the target population and the outcome definitions, code lists were provided for less than 20% of the models. For about half of the models that were developed using regression analysis, the final model was not completely presented. Discussion: Overall, we observed limited improvement over time in the conduct and reporting of clinical prediction model development and validation. In particular, the prediction problem definition was often not clearly reported, and the final model was often not completely presented. Conclusion: Improvement in the reporting of information necessary to enable external validation by other investigators is still urgently needed to increase clinical adoption of developed models.

Context: Several studies have searched for factors that significantly influence adult height (AH) of children with GH deficiency (GHD) who have been treated with biosynthetic GH, but a prediction model for AH has not yet been presented. Objective: Our objective was to develop models for prediction of AH, using information available at the start of GH treatment or after 1 yr of treatment. Design and Setting: For this retrospective study, data were collected from the National Registry of Growth Hormone Treatment in Children, which contained data of Dutch children treated with GH. Patients/Intervention: Patients included males born before 1985 and females born before 1987 with either diagnosis of GHD (syndromes, tumors, and other diseases were excluded) or a maximal GH response during provocation tests of less than 11 ng/ml, treated with biosynthetic GH for at least 1 yr. To be able to use the complete group of 342 children for the development of the models, multiple imputation was used for missing values. Main Outcome Measure: We assessed AH sd scores (SDS). Results: Each prediction model contained both target height SDS and current height SDS. The change in height SDS during the first year proved an important predictor for AH. In all models, addition of GH dose was not significant. The percent explained variance, after correction for overfitting, ranged from 37% (prepubertal children, prediction at start) to 60% (pubertal children, prediction after 1 yr). Conclusion: The presented prediction models give accurate predictions of AH for children with GHD at start and after 1 yr of GH treatment. They are useful tools in the treatment of these children.

Key FeaturesThe Integrated Primary Care Information Project (IPCI) database contains longitudinal data of patients of a group of general practitioners (GPs) in