Medication-Related Problems in Older People with Multimorbidity in Catalonia: A Real-World Data Study with 5 Years' Follow-Up.

Aging, multimorbidity, and polypharmacy are associated with medication-related problems (MRPs). This study aimed to assess the association that multimorbidity and mortality have with MRPs in older people over time. We followed multimorbid, older (65-99 years) people in Catalonia from 2012 to 2016, using longitudinal data and Cox models to estimate adjusted hazard ratios (HR). We reviewed electronic health records to collect explanatory variables and MRPs (duplicate therapy, drug-drug interactions, potentially inappropriate medications (PIM), and contraindicated drugs in chronic kidney disease (CKD) or liver disease). There were 723,016 people (median age: 74 years; 58.9% women) who completed follow-up. We observed a significant (p < 0.001) increase in the proportion with at least one MRP (2012: 66.9% to 2016: 75.5%); contraindicated drugs in CKD (11.1 to 18.5%) and liver disease (3.9 to 5.3%); and PIMs (62.5 to 71.1%), especially drugs increasing fall risk (67.5%). People with ≥10 diseases had more MRPs (in 2016: PIMs, 89.6%; contraindicated drugs in CKD, 34.4%; and in liver disease, 9.3%). All MRPs were independently associated with mortality, from duplicate therapy (HR 1.06; 95% confidence interval (CI) 1.04-1.08) to interactions (HR 1.60; 95% CI 1.54-1.66). Ensuring safe pharmacological treatment in elderly, multimorbid patient remains a challenge for healthcare systems.

Missing data imputation and synthetic data simulation through modeling graphical probabilistic dependencies between variables (ModGraProDep): An application to breast cancer survival.

BACKGROUND: Two common issues may arise in certain population-based breast cancer (BC) survival studies: I) missing values in a survivals' predictive variable, such as "Stage" at diagnosis, and II) small sample size due to "imbalance class problem" in certain subsets of patients, demanding data modeling/simulation methods. METHODS: We present a procedure, ModGraProDep, based on graphical modeling (GM) of a dataset to overcome these two issues. The performance of the models derived from ModGraProDep is compared with a set of frequently used classification and machine learning algorithms (Missing Data Problem) and with oversampling algorithms (Synthetic Data Simulation). For the Missing Data Problem we assessed two scenarios: missing completely at random (MCAR) and missing not at random (MNAR). Two validated BC datasets provided by the cancer registries of Girona and Tarragona (northeastern Spain) were used. RESULTS: In both MCAR and MNAR scenarios all models showed poorer prediction performance compared to three GM models: the saturated one (GM.SAT) and two with penalty factors on the partial likelihood (GM.K1 and GM.TEST). However, GM.SAT predictions could lead to non-reliable conclusions in BC survival analysis. Simulation of a "synthetic" dataset derived from GM.SAT could be the worst strategy, but the use of the remaining GMs models could be better than oversampling. CONCLUSION: Our results suggest the use of the GM-procedure presented for one-variable imputation/prediction of missing data and for simulating "synthetic" BC survival datasets. The "synthetic" datasets derived from GMs could be also used in clinical applications of cancer survival data such as predictive risk analysis.

Clinical relevance of histologic subtypes in locally advanced esophageal carcinoma treated with pre-operative chemoradiotherapy: Experience of a monographic oncologic centre.

BACKGROUND: Locally advanced esophageal carcinoma (LAEC) represents less than 30% of all diagnosed esophageal carcinoma worldwide. The standard of care for resectable tumours consists of preoperative chemoradiotherapy (CRT) followed by surgery. Despite the curative intent, the prognosis is still poor mainly due to relapse. A multidisciplinary approach is required in order to optimize the therapeutic strategy and follow-up. Differences in outcomes between the two main histological subtypes, adenocarcinoma (ADC) and squamous cell carcinoma (SCC), have been reported. Nevertheless, the heterogeneity in trials design and data available have hampered the achievement of clear conclusions. The purpose of this study is to report the outcomes from a cohort of patients with LAEC treated with a multidisciplinary approach and to remark the differences observed between the two main histologic subtypes and their clinical implications. METHODS: We retrospectively reviewed 100 patients diagnosed with LAEC that were treated with preoperative CRT at our institution and integrated centres. Histopathological characteristics and toxicities during treatment were recorded. Patterns of recurrence at the first relapse were analysed. Survival curves were plotted using the Kaplan Meier method and multivariate Cox proportional hazards models were used. RESULTS: Among the patients who received preoperative CRT, 83% underwent surgery. The median overall survival (mOS) was 31.7 months, 26.9 months for ADC and 45.5 for SCC (p-value = 0.33). In the multivariate Cox regression analysis, ypN+ was the only factor that negatively influenced in OS (OR = 4.1, p-value = 0.022). Patterns of recurrence differed according to histologic subtype. Distant relapse was more frequent in ADC (62%), whereas locoregional relapse was higher in SCC (50%) (p-value = 0.027). Second line therapeutic strategies could be offered to 50% of those patients who relapsed. CONCLUSIONS: Differences in outcomes and recurrence pattern could be observed between the two main histologic subtypes of LAEC. A better molecular characterization, adapted therapeutic regimens and follow up strategies should be adopted in order to improve survival of these patients.