Predicting adverse outcomes in adults with a community-acquired lower respiratory tract infection: a protocol for the development and validation of two prediction models for (i) all-cause hospitalisation and mortality and (ii) cardiovascular outcomes

BACKGROUND: Community-acquired lower respiratory tract infections (LRTI) are common in primary care and patients at particular risk of adverse outcomes, e.g., hospitalisation and mortality, are challenging to identify. LRTIs are also linked to an increased incidence of cardiovascular diseases (CVD) following the initial infection, whereas concurrent CVD might negatively impact overall prognosis in LRTI patients. Accurate risk prediction of adverse outcomes in LRTI patients, while considering the interplay with CVD, can aid general practitioners (GP) in the clinical decision-making process, and may allow for early detection of deterioration. This paper therefore presents the design of the development and external validation of two models for predicting individual risk of all-cause hospitalisation or mortality (model 1) and short-term incidence of CVD (model 2) in adults presenting to primary care with LRTI. METHODS: Both models will be developed using linked routine electronic health records (EHR) data from Dutch primary and secondary care, and the mortality registry. Adults aged ≥ 40 years with a GP-diagnosis of LRTI between 2016 and 2019 are eligible for inclusion. Relevant patient demographics, medical history, medication use, presenting signs and symptoms, and vital and laboratory measurements will be considered as candidate predictors. Outcomes of interest include 30-day all-cause hospitalisation or mortality (model 1) and 90-day CVD (model 2). Multivariable elastic net regression techniques will be used for model development. During the modelling process, the incremental predictive value of CVD for hospitalisation or all-cause mortality (model 1) will also be assessed. The models will be validated through internal-external cross-validation and external validation in an equivalent cohort of primary care LRTI patients. DISCUSSION: Implementation of currently available prediction models for primary care LRTI patients is hampered by limited assessment of model performance. While considering the role of CVD in LRTI prognosis, we aim to develop and externally validate two models that predict clinically relevant outcomes to aid GPs in clinical decision-making. Challenges that we anticipate include the possibility of low event rates and common problems related to the use of EHR data, such as candidate predictor measurement and missingness, how best to retrieve information from free text fields, and potential misclassification of outcome events

Synthesis and Application of New Chiral Amines in Dutch Resolution : Family Behaviour in Nucleation Inhibition.

De (organische) chemie heeft zo zijn eigen vakjargon. Om dit proefschrift enigszins begrijpelijk te maken voor niet-chemici is het nodig om ‘de taal te spreken’. Deze Nederlandse samenvatting begint dan ook met een kleine, maar simpele uitleg over de taal die synthetisch chemici spreken. Na deze snelle taalcursus begint de samenvatting met een inleiding van de begrippen chiraliteit, enantiomeer en hun bioactiviteit. Dit proefschrift gaat voor het grootste gedeelte over het splitsen van enantiomeren via het “Dutch Resolution” protocol. Deze methode, die nog in de kinderschoenen staat van het toepassen en het begrijpen ervan, zal vergeleken worden met de “klassieke resolutie”. ... Zie: Samenvatting

CCDC 154389: Experimental Crystal Structure Determination

Related Article: M.van der Sluis, J.Dalmolen, B.de Lange, B.Kaptein, R.M.Kellogg, Q.B.Broxterman|2001|Org.Lett.|3|3943|doi:10.1021/ol016840f,An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.

Synthesis of Enantiopure 1-Aryl-1-butylamines and 1-Aryl-3-butenylamines by Diastereoselective Addition of Allylzinc Bromide to Imines Derived from (R)-Phenylglycine Amide

The synthesis of enantiopure 1-aryl-1-butylamines via a highly diastereoselective addition of allylzinc bromide to imines derived from (R)-phenylglycine amide is reported. These are synthesised by a three-step procedure, which involves: (a) formation of the chiral imines; (b) asymmetric addition of the allylzinc reagent; (c) removal of the chiral auxiliary by means of a reductive or non-reductive method. The reductive method provides 1-aryl-1-butylamines whereas the non-reductive method preserves the double bond to afford 1-aryl-3-butenylamines.

Predicting adverse outcomes in adults with a community-acquired lower respiratory tract infection: a protocol for the development and validation of two prediction models for (i) all-cause hospitalisation and mortality and (ii) cardiovascular outcomes

Abstract Background Community-acquired lower respiratory tract infections (LRTI) are common in primary care and patients at particular risk of adverse outcomes, e.g., hospitalisation and mortality, are challenging to identify. LRTIs are also linked to an increased incidence of cardiovascular diseases (CVD) following the initial infection, whereas concurrent CVD might negatively impact overall prognosis in LRTI patients. Accurate risk prediction of adverse outcomes in LRTI patients, while considering the interplay with CVD, can aid general practitioners (GP) in the clinical decision-making process, and may allow for early detection of deterioration. This paper therefore presents the design of the development and external validation of two models for predicting individual risk of all-cause hospitalisation or mortality (model 1) and short-term incidence of CVD (model 2) in adults presenting to primary care with LRTI. Methods Both models will be developed using linked routine electronic health records (EHR) data from Dutch primary and secondary care, and the mortality registry. Adults aged ≥ 40 years with a GP-diagnosis of LRTI between 2016 and 2019 are eligible for inclusion. Relevant patient demographics, medical history, medication use, presenting signs and symptoms, and vital and laboratory measurements will be considered as candidate predictors. Outcomes of interest include 30-day all-cause hospitalisation or mortality (model 1) and 90-day CVD (model 2). Multivariable elastic net regression techniques will be used for model development. During the modelling process, the incremental predictive value of CVD for hospitalisation or all-cause mortality (model 1) will also be assessed. The models will be validated through internal-external cross-validation and external validation in an equivalent cohort of primary care LRTI patients. Discussion Implementation of currently available prediction models for primary care LRTI patients is hampered by limited assessment of model performance. While considering the role of CVD in LRTI prognosis, we aim to develop and externally validate two models that predict clinically relevant outcomes to aid GPs in clinical decision-making. Challenges that we anticipate include the possibility of low event rates and common problems related to the use of EHR data, such as candidate predictor measurement and missingness, how best to retrieve information from free text fields, and potential misclassification of outcome events

CCDC 200249: Experimental Crystal Structure Determination

Related Article: J.Dalmolen, M.van der Sluis, J.W.Nieuwenhuijzen, A.Meetsma, B.de Lange, B.Kaptein, R.M.Kellogg, Q.B.Broxterman|2004|Eur.J.Org.Chem.|2004|1544|doi:10.1002/ejoc.20030066