Developing PHarmacie-R: A bedside risk prediction tool with a medicines management focus to identify risk of hospital readmission

BackgroundThe imperative to identify patients at risk of medication-related harm has never been greater. Hospital clinicians cannot easily predict risk of readmission or harm. Candidate variables associated with medication-related harm derived from the literature or significantly represented in a complex patient cohort have been previously described by PHarmacie-4. With a focus on polypharmacy and high-risk medicines in vulnerable patient cohorts, PHarmacie-4 was easy to use and highlighted risks. However it over-estimated risk, reducing its usefulness in stratifying risk of readmission.ObjectiveDevelop a risk prediction tool built into a smart phone app, enabling clinicians to identify and refer high-risk patients for an early post-discharge medicines review. Demonstrate usability, real world application and validity in an independent dataset.MethodsA retrospective, observational study was conducted with 1201 randomly selected patients admitted to Sir Charles Gairdner Hospital between June 1, 2016 to December 31, 2016. Patient characteristics and outcomes of interest were reported, including unplanned hospital utilisation at 30, 60 and 90 days post-discharge. Using multivariable logistic regression modelling, an algorithm was developed, built into a smart phone app and used and validated in an independent dataset.Results738 patients (61%) were included in the derivation sample. The best predictive performance was achieved by PHarmacie-R (C-statistic 0.72, 95% CI 0.68–0.75) which included PHarmacie-4 risk variables, a non-linear effect of age, unplanned hospital utilisation in the preceding six months and gender. The independent validation dataset had a C-statistic of 0.64 (95% CI 0.56–0.72).ConclusionPHarmacie-R is the first readmission risk prediction tool, built into a smart phone app, focussing on polypharmacy and high-risk medicines in vulnerable patients. It can assist clinical pharmacists to identify medical inpatients who may benefit from early post-discharge medication management services. External validation is needed to enable application in other clinical settings.     

Role of anti-stromal polypharmacy in increasing survival after pancreaticoduodenectomy for pancreatic ductal adenocarcinoma

AIM: To investigate the survival impact of common pharmaceuticals, which target stromal interactions, following a pancreaticoduodenectomy for pancreatic ductal adenocarcinoma.METHODS: Data was collected retrospectively for 164 patients who underwent a pancreaticoduodenectomy for pancreatic ductal adenocarcinoma (PDAC). Survival analysis was performed on patients receiving the following medications: angiotensin-converting enzyme inhibitors (ACEI)/angiotensin II receptor blockers (ARB), calcium channel blockers (CCB), aspirin, and statins. Statistical analysis included Kaplan-meier survival estimates and cox multivariate regression; the latter of which allowed for any differences in a range of prognostic indicators between groups. Medications showing a significant survival benefit were investigated in combination with other medications to evaluate synergistic effects.RESULTS: No survival benefit was observed with respect to ACEI/ARB (n = 41), aspirin or statins on individual drug analysis (n = 39). However, the entire CCB group (n = 26) showed a significant survival benefit on multivariate cox regression; hazard ratio (HR) of 0.475 (CI = 0.250-0.902, P = 0.023). Further analysis revealed that this was influenced by a group of patients who were taking aspirin in combination with CCB; median survival was significantly higher in the CCB + aspirin group (n = 15) compared with the group taking neither drug (n = 98); 1414 d vs 601 d (P = 0.029, log-rank test). Multivariate cox regression revealed neither aspirin nor CCB had a statistically significant impact on survival when given alone, however in combination the survival benefit was significant; HR = 0.332 (CI = 0.126-0.870, P = 0.025). None of the other medications showed a survival benefit in any combination.CONCLUSION: Aspirin + CCB in combination appears to increase survival in patients with PDAC, highlighting the potential clinical use of combination therapy to target stromal interactions in pancreatic cancer.     

老年高血压药物治疗进展

高血压是冠心病、慢性心力衰竭、房颤和脑卒中等疾病的高危发病因子,在老年人群中的发生率高达2/3。噻嗪类利尿剂、钙拮抗剂、血管紧张素转换酶抑制剂、血管紧张素受体抑制剂和β受体阻滞剂等5类药物均能有效控制高血压并减少其不良后果,但各有其治疗优势。应重视多药治疗对老年高血压患者的安全性、依从性和经济负担所造成的不利影响。     

多重用药对冠心病患者发生氯吡格雷抵抗的影响研究

背景　多重用药是药物相互作用的主要危险因素,冠心病患者常面临多重用药的问题;冠心病二级预防基础药物氯吡格雷的活性极易受到药物相互作用的影响,但多重用药对于氯吡格雷疗效的影响尚未见报道。目的　分析多重用药对冠心病患者发生氯吡格雷抵抗（CR）的相关影响因素。方法　选取南京医科大学附属无锡市人民医院2015年9月-2018年3月符合纳入标准的冠心病患者333例,根据血小板聚集率（PAR）分为CR组（PAR>50%）132例与对照组（PAR≤50%）201例。通过医院信息系统收集基本资料,包括基本信息〔性别、年龄、吸烟、饮酒、合并疾病、心功能分级、是否行冠状动脉介入术治疗〕;体格检查及实验室检查〔体质指数（BMI）、血压、总胆固醇（TC）、低密度脂蛋白胆固醇（LDL-C）、空腹血糖（FBG）、血肌酐（Scr）、丙氨酸氨基转移酶（ALT）、PAR〕;用药情况〔质子泵抑制剂（PPI）、地尔硫、他汀类药物（阿托伐他汀及辛伐他汀）、钙通道拮抗剂（CCB）、血管紧张素受体拮抗剂（ARB）、美托洛尔、硝酸酯类、曲美他嗪、含抗栓成分中药注射剂、低分子肝素〕。采用多因素Logistic回归分析探讨冠心病患者发生CR的影响因素。结果　CR组患者高血压合并率、BMI≥25.0 kg/m2发生率、LDL-C≥2.07 mmol/L比例、FBG、多重用药（≥7种）比例高于对照组（P<0.05）。多因素Logistic回归分析结果显示,FBG（≥6.1 mmol/L）（OR=1.121,P=0.033）、多重用药（≥7种）（OR=5.224,P=0.003）是CR的影响因素。CR组患者含抗栓成分中药注射剂、低分子肝素使用率高于对照组（P<0.05）。结论　FBG、多重用药（≥7种）是冠心病患者发生CR的影响因素,合并使用含抗栓成分中药注射剂、低分子肝素可能与冠心病患者发生CR有关,临床应予以重视。     

Therapy related hospital admission in patients on polypharmacy in Singapore: a pilot study

Objective: To estimate the incidence of drug-related problems (DRPs)associated hospital admission, and its correlation to polypharmacy and age.Method: A retrospective, crosssectional study in in-patients on polypharmacy in Singapore. Significant differences (p < 0.05) between number of medications taken and age of patients were tested with the chisquare test.Results: The study population consisted of 347 patients (aged 16–97) on a mean of 7.4 ± 2.1 medications. 10.8% of the study population had DRPs on admission: 71.9% of which were dominant reasons for admission, and DRPs contributed partly in the remaining cases. These DRPs were mostly avoidable, and can be broadly classified into noncompliance, adverse drug reactions, require synergistic therapy, inappropriate dose and untreated condition. 52% of these cases were made up of geriatric patients. No statistical difference was found between patients on polypharmacy and those on major polypharmacy (10 and more drugs) in having a DRP.Conclusion: In this study, DRPs contributing to hospital admission appeared to be avoidable. Geriatrics were more susceptible to DRPs and future efforts are required in managing medications prescribed for these patients to reduce such incidences.     

Application of data mining for examining polypharmacy and adverse effects in cardiology patients

This article comments upon the use of data mining tools to examine clinical data. Many cardiovascular patients have co-morbid diseases that put them at risk for polypharmacy, or severe adverse reactions from the interactions of multiple medications. Clinical trials typically use too few patients with stringent inclusion/exclusion criteria that prevent an examination of the issue of polypharmacy. However, clinical data collected in the course of patient treatment can be used in conjunction with data mining to find meaningful results.