Too Much Medicine in older people? Deprescribing through Shared Decision Making

Too much medicine is an increasingly recognised problem,1 2 and one manifestation is inappropriate polypharmacy in older people. Polypharmacy is usually defined as taking more than five regular prescribed medicines.3 It can be appropriate (when potential benefits outweigh potential harms)4 but increases the risk of older people experiencing adverse drug reactions, impaired physical and cognitive function, and hospital admission.5 6 7 There is limited evidence to inform polypharmacy in older people, especially those with multimorbidity, cognitive impairment, or frailty.8 Systematic reviews of medication withdrawal trials (deprescribing) show that reducing specific classes of medicines may decrease adverse events and improve quality of life.9 10 11 Two recent reviews of the literature on deprescribing stressed the importance of patient involvement and shared decision making.12 13 Patients and clinicians typically overestimate the benefits of treatments and underestimate their harms.14 When they engage in shared decision making they become better informed about potential outcomes and as a result patients tend to choose more conservative options (eg, fewer medicines), facilitating deprescribing.15 However, shared decision making in this context is not easy, and there is little guidance on how to do it.16 We draw together evidence from the psychology, communication, and decision making literature (see appendix on thebmj.com). For each step of the shared decision making process we describe the unique tasks required for deprescribing decisions; identify challenges for older adults, their companions, and clinicians (figure); give practical advice on how challenges may be overcome; highlight where more work is needed; and identify priorities for future research (table). Key messages Deprescribing is a process of planned and supervised tapering or ceasing of inappropriate medicines Shared decision making should be an integral part of the deprescribing process Many factors affect this process, including trust in clinicians’ advice, contradictory patient attitudes about medication, cognitive biases that lead to a preference for the status quo and positive information, and information processing difficulties There is uncertainty about the effect of risk communication and preference elicitation tools in older people Older people’s preferences for discussing life expectancy and quality of life vary widely, but even those who wish to delegate their decisions still appreciate discussion of optionsJJ is supported by a National Health and Medical Research Council (NHMRC) early career fellowship (1037028) and KM is supported by an NHMRC career development fellowship (1029241

Class A Orphans in GtoPdb v.2023.1

Table 1 lists a number of putative GPCRs identified by NC-IUPHAR [161], for which preliminary evidence for an endogenous ligand has been published, or for which there exists a potential link to a disease, or disorder. These GPCRs have recently been reviewed in detail [121]. The GPCRs in Table 1 are all Class A, rhodopsin-like GPCRs. Class A orphan GPCRs not listed in Table 1 are putative GPCRs with as-yet unidentified endogenous ligands.Table 1: Class A orphan GPCRs with putative endogenous ligands GPR3GPR4GPR6GPR12GPR15GPR17GPR20 GPR22GPR26GPR31GPR34GPR35GPR37GPR39 GPR50GPR63GPR65GPR68GPR75GPR84GPR87 GPR88GPR132GPR149GPR161GPR183LGR4LGR5 LGR6MAS1MRGPRDMRGPRX1MRGPRX2P2RY10TAAR2 In addition the orphan receptors GPR18, GPR55 and GPR119 which are reported to respond to endogenous agents analogous to the endogenous cannabinoid ligands have been grouped together (GPR18, GPR55 and GPR119)

Class A Orphans (version 2020.5) in the IUPHAR/BPS Guide to Pharmacology Database

Table 1 lists a number of putative GPCRs identified by NC-IUPHAR [194], for which preliminary evidence for an endogenous ligand has been published, or for which there exists a potential link to a disease, or disorder. These GPCRs have recently been reviewed in detail [150]. The GPCRs in Table 1 are all Class A, rhodopsin-like GPCRs. Class A orphan GPCRs not listed in Table 1 are putative GPCRs with as-yet unidentified endogenous ligands.Table 1: Class A orphan GPCRs with putative endogenous ligands GPR3 GPR4 GPR6 GPR12 GPR15 GPR17 GPR20 GPR22 GPR26 GPR31 GPR34 GPR35 GPR37 GPR39 GPR50 GPR63 GRP65 GPR68 GPR75 GPR84 GPR87 GPR88 GPR132 GPR149 GPR161 GPR183 LGR4 LGR5 LGR6 MAS1 MRGPRD MRGPRX1 MRGPRX2 P2RY10 TAAR2 In addition the orphan receptors GPR18, GPR55 and GPR119 which are reported to respond to endogenous agents analogous to the endogenous cannabinoid ligands have been grouped together (GPR18, GPR55 and GPR119)

Class A Orphans in GtoPdb v.2021.3

Table 1 lists a number of putative GPCRs identified by NC-IUPHAR [161], for which preliminary evidence for an endogenous ligand has been published, or for which there exists a potential link to a disease, or disorder. These GPCRs have recently been reviewed in detail [121]. The GPCRs in Table 1 are all Class A, rhodopsin-like GPCRs. Class A orphan GPCRs not listed in Table 1 are putative GPCRs with as-yet unidentified endogenous ligands.Table 1: Class A orphan GPCRs with putative endogenous ligands GPR3GPR4GPR6GPR12GPR15GPR17GPR20 GPR22GPR26GPR31GPR34GPR35GPR37GPR39 GPR50GPR63GRP65GPR68GPR75GPR84GPR87 GPR88GPR132GPR149GPR161GPR183LGR4LGR5 LGR6MAS1MRGPRDMRGPRX1MRGPRX2P2RY10TAAR2 In addition the orphan receptors GPR18, GPR55 and GPR119 which are reported to respond to endogenous agents analogous to the endogenous cannabinoid ligands have been grouped together (GPR18, GPR55 and GPR119)

Class A Orphans in GtoPdb v.2022.3

Table 1 lists a number of putative GPCRs identified by NC-IUPHAR [161], for which preliminary evidence for an endogenous ligand has been published, or for which there exists a potential link to a disease, or disorder. These GPCRs have recently been reviewed in detail [121]. The GPCRs in Table 1 are all Class A, rhodopsin-like GPCRs. Class A orphan GPCRs not listed in Table 1 are putative GPCRs with as-yet unidentified endogenous ligands.Table 1: Class A orphan GPCRs with putative endogenous ligands GPR3GPR4GPR6GPR12GPR15GPR17GPR20 GPR22GPR26GPR31GPR34GPR35GPR37GPR39 GPR50GPR63GPR65GPR68GPR75GPR84GPR87 GPR88GPR132GPR149GPR161GPR183LGR4LGR5 LGR6MAS1MRGPRDMRGPRX1MRGPRX2P2RY10TAAR2 In addition the orphan receptors GPR18, GPR55 and GPR119 which are reported to respond to endogenous agents analogous to the endogenous cannabinoid ligands have been grouped together (GPR18, GPR55 and GPR119)

Class A Orphans (version 2019.4) in the IUPHAR/BPS Guide to Pharmacology Database

Table 1 lists a number of putative GPCRs identified by NC-IUPHAR [191], for which preliminary evidence for an endogenous ligand has been published, or for which there exists a potential link to a disease, or disorder. These GPCRs have recently been reviewed in detail [148]. The GPCRs in Table 1 are all Class A, rhodopsin-like GPCRs. Class A orphan GPCRs not listed in Table 1 are putative GPCRs with as-yet unidentified endogenous ligands.Table 1: Class A orphan GPCRs with putative endogenous ligands GPR3GPR4GPR6GPR12GPR15GPR17GPR20 GPR22GPR26GPR31GPR34GPR35GPR37GPR39 GPR50GPR63GRP65GPR68GPR75GPR84GPR87 GPR88GPR132GPR149GPR161GPR183LGR4LGR5 LGR6MAS1MRGPRDMRGPRX1MRGPRX2P2RY10TAAR2 In addition the orphan receptors GPR18, GPR55 and GPR119 which are reported to respond to endogenous agents analogous to the endogenous cannabinoid ligands have been grouped together (GPR18, GPR55 and GPR119)

Smoke toxicity of rainscreen façades

The toxic smoke production of four rainscreen façade systems were compared during large-scale fire performance testing on a reduced height BS 8414 test wall. Systems comprising 'non-combustible' aluminium composite material (ACM) with polyisocyanurate (PIR), phenolic foam (PF) and stone wool (SW) insulation, and polyethylene-filled ACM with PIR insulation were tested. Smoke toxicity was measured by sampling gases at two points - the exhaust duct of the main test room and an additional 'kitchen vent', which connects the rainscreen cavity to an occupied area. Although the toxicity of the smoke was similar for the three insulation products with non-combustible ACM, the toxicity of the smoke flowing from the burning cavity through the kitchen vent was greater by factors of 40 and 17 for PIR and PF insulation respectively, when compared to SW. Occupants sheltering in a room connected to the vent are predicted to collapse, and then inhale a lethal concentration of asphyxiant gases. This is the first report quantifying fire conditions within the cavity and assessing smoke toxicity within a rainscreen façade cavity. [Abstract copyright: Copyright © 2020 Elsevier B.V. All rights reserved.

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2–4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease