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- Benedetti, Franzisca Domeisen, et al.
(författare)
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International palliative care experts' view on phenomena indicating the last hours and days of life
- 2013
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Ingår i: Supportive Care in Cancer. - : Springer Science and Business Media LLC. - 0941-4355 .- 1433-7339. ; 21:6, s. 1509-1517
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Tidskriftsartikel (refereegranskat)abstract
- Providing the highest quality care for dying patients should be a core clinical proficiency and an integral part of comprehensive management, as fundamental as diagnosis and treatment. The aim of this study was to provide expert consensus on phenomena for identification and prediction of the last hours or days of a patient's life. This study is part of the OPCARE9 project, funded by the European Commission's Seventh Framework Programme. The phenomena associated with approaching death were generated using Delphi technique. The Delphi process was set up in three cycles to collate a set of useful and relevant phenomena that identify and predict the last hours and days of life. Each cycle included: (1) development of the questionnaire, (2) distribution of the Delphi questionnaire and (3) review and synthesis of findings. The first Delphi cycle of 252 participants (health care professionals, volunteers, public) generated 194 different phenomena, perceptions and observations. In the second cycle, these phenomena were checked for their specific ability to diagnose the last hours/days of life. Fifty-eight phenomena achieved more than 80 % expert consensus and were grouped into nine categories. In the third cycle, these 58 phenomena were ranked by a group of palliative care experts (78 professionals, including physicians, nurses, psycho-social-spiritual support; response rate 72 %, see Table 1) in terms of clinical relevance to the prediction that a person will die within the next few hours/days. Twenty-one phenomena were determined to have "high relevance" by more than 50 % of the experts. Based on these findings, the changes in the following categories (each consisting of up to three phenomena) were considered highly relevant to clinicians in identifying and predicting a patient's last hours/days of life: "breathing", "general deterioration", "consciousness/cognition", "skin", "intake of fluid, food, others", "emotional state" and "non-observations/expressed opinions/other". Experts from different professional backgrounds identified a set of categories describing a structure within which clinical phenomena can be clinically assessed, in order to more accurately predict whether someone will die within the next days or hours. However, these phenomena need further specification for clinical use.
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| 2. |
- Erzurumluoglu, A. Mesut, et al.
(författare)
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Meta-analysis of up to 622,409 individuals identifies 40 novel smoking behaviour associated genetic loci
- 2020
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Ingår i: Molecular Psychiatry. - : Nature Publishing Group. - 1359-4184 .- 1476-5578. ; 25:10, s. 2392-2409
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Tidskriftsartikel (refereegranskat)abstract
- Smoking is a major heritable and modifiable risk factor for many diseases, including cancer, common respiratory disorders and cardiovascular diseases. Fourteen genetic loci have previously been associated with smoking behaviour-related traits. We tested up to 235,116 single nucleotide variants (SNVs) on the exome-array for association with smoking initiation, cigarettes per day, pack-years, and smoking cessation in a fixed effects meta-analysis of up to 61 studies (up to 346,813 participants). In a subset of 112,811 participants, a further one million SNVs were also genotyped and tested for association with the four smoking behaviour traits. SNV-trait associations with P < 5 × 10-8 in either analysis were taken forward for replication in up to 275,596 independent participants from UK Biobank. Lastly, a meta-analysis of the discovery and replication studies was performed. Sixteen SNVs were associated with at least one of the smoking behaviour traits (P < 5 × 10-8) in the discovery samples. Ten novel SNVs, including rs12616219 near TMEM182, were followed-up and five of them (rs462779 in REV3L, rs12780116 in CNNM2, rs1190736 in GPR101, rs11539157 in PJA1, and rs12616219 near TMEM182) replicated at a Bonferroni significance threshold (P < 4.5 × 10-3) with consistent direction of effect. A further 35 SNVs were associated with smoking behaviour traits in the discovery plus replication meta-analysis (up to 622,409 participants) including a rare SNV, rs150493199, in CCDC141 and two low-frequency SNVs in CEP350 and HDGFRP2. Functional follow-up implied that decreased expression of REV3L may lower the probability of smoking initiation. The novel loci will facilitate understanding the genetic aetiology of smoking behaviour and may lead to the identification of potential drug targets for smoking prevention and/or cessation.
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