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Hajjar ER, Hanlon JT, Sloane RJ, Lindblad CI, Pieper CF, Ruby CM, et al. Unnecessary drug use in frail older people at hospital discharge. J Am Geriatr Soc. 2005;53:1518–23. https://doi.org/10.1111/j.1532-5415.2005.53523.x.
Among studies using objective non-standard measures of COC ( n = 16), the majority ( n = 11) used the number of prescribers [ 46, 48, 51, 55, 56, 59, 60, 61, 63, 68, 69] to measure COC, with a high number of prescribers indicating low COC. Further measures were the number of treating physicians [ 49, 61], the number of providers [ 57, 67], the number of specialties [ 58], the tendency to visit multiple providers [ 62], and having a single primary care physician [ 61]. Exposure variables were treated as binary, ordinal, or continuous (Table 2; Tables S1 and S2 in the ESM). In the population studied, men had a larger volume of distribution and a higher oral clearance compared to women, and sex was by itself a significant covariate for both volume of distribution and oral clearance. Population mean values for men were 1.94 × 10 3 L ( V2/ F) and 34 L/hr (CL/ F), while for women, the values were 1.81 × 10 3 L ( V2/ F) and 30 L/hr (CL/ F). However, when the effect of height on V2/ F was incorporated in the model, sex was no longer a significant covariate, that is, body size (in this case, height) explains the difference between the sexes in V2/ F and CL/ F. Fig. 7 Quantitative analysis of the substructure attention mechanism. (a) The relationship between accuracy and the number of iterations for the SA-DDI and SA-DDI_noSA. (b) The relationship between the F1-score and the number of iterations. (c) The distribution of substructure attention scores for the 10 iterations/steps SA-DDI in the DrugBank dataset. (d) The improvement of accuracy by increasing the number of iterations from 1 to 6 for SA-DDI_noSA. TWOSIDES is constructed by Zitnik et al. 39 after filtering and preprocessing the original TWOSIDES dataset. 40 It includes 645 drugs with 963 interaction types and 4 576 287 DDI tuples. As against the DrugBank dataset, these interactions are at the phenotypic level ( i.e., headache, pain in the throat, and others) rather than metabolic. The negative samples are generated by a procedure the same as the DrugBank dataset. 3.2 Experimental setup We compared the proposed SA-DDI with state-of-the-art methods, namely, DeepCCI, 26 MR-GNN, 29 SSI-DDI, 28 GAT-DDI, 30 and GMPNN-CS. 30 These baselines only consider chemical structure information as input and can work in both warm and cold start scenarios. The parameter settings for MR-GNN, SSI-DDI, and GMPNN-CS are consistent with their published source codes. As the source codes for DeepCCI and GAT-DDI are not provided, we implemented them with parameters recommended by the papers. 26,30 To investigate how the D-MPNN, substructure attention and substructure–substructure interaction module improve the model performance, we also consider the following variants of SA-DDI: Cadogan CA, Ryan C, Hughes CM. Appropriate polypharmacy and medicine safety: when many is not too many. Drug Saf. 2016;39:109–16. https://doi.org/10.1007/s40264-015-0378-5.Masnoon N, Shakib S, Kalisch-Ellett L, Caughey GE. What is polypharmacy? A systematic review of definitions. BMC Geriatr. 2017;17:230. https://doi.org/10.1186/s12877-017-0621-2. Fig. 9 Heat maps of the atom similarity matrix for compounds (a) glycopyrronium, (b) procyclidine, (c) dyclonine, and (d) benzatropine. The atoms in the compounds are automatically grouped into clusters during the learning process where the corresponding substructures for clusters are highlighted in the drugs. Patterson SM, Cadogan CA, Kerse N, Cardwell CR, Bradley MC, Ryan C, Hughes C. Interventions to improve the appropriate use of polypharmacy for older people. Cochrane Database Syst Rev. 2014. https://doi.org/10.1002/14651858.CD008165.pub3. Fried TR, O’Leary J, Towle V, Goldstein MK, Trentalange M, Martin DK. Health outcomes associated with polypharmacy in community-dwelling older adults: a systematic review. J Am Geriatr Soc. 2014;62:2261–72. https://doi.org/10.1111/jgs.13153.
Tetzlaff F, Singer A, Swart E, Robra B-P, Herrmann MLH. Polypharmazie in der nachstationären Versorgung: Eine Analyse mit Daten der AOK Sachsen-Anhalt. [Polypharmacy after Discharge from Hospital: An Analysis Using Data of the Statutory Health Insurance (AOK) of Saxony-Anhalt]. Gesundheitswesen. 2018;80:557–63. https://doi.org/10.1055/s-0042-113599.
where X e is the bond-level hidden feature matrix and A e is the adjacency matrix in which the nonzero position indicates that two bonds share a common vertex. GNN is an arbitrary GNN layer for calculating projection scores. We then assigned an attention score to each graph-level representation g ( t) at the step t as follows:
Hanlon JT, Schmader KE, Samsa GP, Weinberger M, Uttech KM, Lewis IK, et al. A method for assessing drug therapy appropriateness. J Clin Epidemiol. 1992;45:1045–51. https://doi.org/10.1016/0895-4356(92)90144-C. Fig. 2 An overview of the proposed SA-DDI for DDI prediction. The model takes a pair of drugs as input and then feeds them into a feed-forward layer, followed by a D-MPNN equipped with the substructure attention to extract the size- and shape-adaptive substructures. The directed message passing network updates the node-level features with T iterations where T is 6 in this example. The extracted substructures are then fed into the SSIM to learn the substructure–substructure interactions. Finally, the model predicts DDI based on the result of substructure–substructure interactions. Sponsler KC, Neal EB, Kripalani S. Improving medication safety during hospital-based transitions of care. CCJM. 2015;82:351–60. https://doi.org/10.3949/ccjm.82a.14025. b) A novel substructure–substructure interaction module (SSIM) was introduced to model the chemical reactions among functional substructures of a drug pair. SSIM leverages the structure information of a drug to identify the important substructures of another drug for a drug pair. This overcomes the limitation of global mean/sum pooling, which regards each substructure as equally important.ROCHA, José Manuel (30 de outubro de 1999). «O número que marco foi alterado». Público . Consultado em 25 de junho de 2015 Regarding the operationalization of PIM, different versions of the Beers criteria [ 71] were applied [ 46, 47, 64]. Other instruments were used, such as the Japanese STOPP-J list [ 59], the Norwegian General Practice (NORGEP) criteria, which are based on the Beers criteria [ 56], the German PRISCUS list [ 49], and the STOPP/START criteria [ 52]. PIM was always analyzed by using a binary (yes vs no) variable. Concerning DDI, the outcome variable was dichotomized (yes vs no) in all but one included study, which treated DDI as a continuous variable [ 45]. PIDC, as used by Tamblyn et al. [ 61], is a combination of PIM and DDI, identified by an expert review. Duplicated medications were used as outcomes by Cheng and Chen [ 43] and Chu et al. [ 44]. ADE were defined as either the presence of an ADE-specific code [ 58] or as a binary (yes vs no) outcome self-reported by the study participants [ 63]. One study [ 60] measured unnecessary drug use based on the Medication Appropriateness Index [ 72]. Finally, overdose as an outcome was defined as the occurrence of one or more medical claims containing a diagnosis code for opioid or benzodiazepine poisoning on a person-day of opioid-benzodiazepine overlap [ 51] (Table S2, see ESM). 3.3 Association Between COC and Polypharmacy By implementing DDI numbers, you enable faster communications between team members, customers and employees. You remove the need for people to first speak to a receptionist or switchboard, which means less time spent waiting and more time spent having conversations. 2. Better for customer experience Effect of NRTIs on the in vivo formation of 14CO 2 from [U- 14C]palmitate in mice. Mice were treated or not for 2 weeks with AZT (100 mg/kg/day), 3TC (50 mg/kg/day), ddI (66 mg/kg/day), d4T (13.5 mg/kg/day), ddC (0.36 mg/kg/day), or three combinations of two NRTIs (same doses as for the single-drug treatments) and fasted for the last 48 h of treatment. A tracer dose of [U- 14C]palmitate was administered, and 14CO 2 exhalation was measured for 120 min. Values for treated animals were expressed as percentages of the values for the corresponding controls. Each of the eight different control groups included 7 to 11 mice. Results for treated animals are means ± SEMs for 8 to 12 mice. The asterisk indicates a significant difference from results for the corresponding controls ( P< 0.05).
where and are two weight matrices. s ( y) i can be viewed as the importance for substructure centering at i-th atom of d y. The overall computational steps for s ( y) i are depicted in Fig. 4. Finally, the graph-level representation of d y can be computed by the following: van den Akker M, Vaes B, Goderis G, van Pottelbergh G, de Burghgraeve T, Henrard S. Trends in multimorbidity and polypharmacy in the Flemish-Belgian population between 2000 and 2015. PLoS ONE. 2019;14:e0212046. https://doi.org/10.1371/journal.pone.0212046.
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a) A directed message passing neural network (D-MPNN) 32 equipped with a novel substructure attention mechanism was presented to extract flexible-sized and irregular-shaped substructures. In SA-DDI, different scores determined by the substructure attention mechanism were assigned to substructures with different radii ( i.e., different receptive fields). The weighted sum of substructures centering at an atom with different radii results in a size-adaptive molecular substructure, as shown in Fig. 2. The substructure attention was also expected to assign a lower score to a substructure from a higher level to prevent over-smoothing. 33
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