RESUMO
Polypharmacy has been linked to cognitive decline. However, interventions targeting modifiable risk factors, some of which are targets of the most commonly used drugs, could reduce the prevalence of dementia. Our aim was to determine the drug prescription regimen at baseline, prior to the diagnosis of mild cognitive impairment (MCI), and its possible association with progression to dementia. Data were collected from the electronic medical records of 342 MCI outpatients diagnosed during 2006-2017 at their first neurology consultation. We followed the classical three-step method of statistical analysis, starting with a Latent Class Analysis (LCA) to discover subgroups of drug prescription probability. Half of the patients were under polypharmacy (≥5 drugs), 17.5% had no recorded medication, 33.3% progressed to dementia (94.7% in ≤5 years), and 84.1% of them to Alzheimer's disease (AD). According to the LCA and based on 20 therapeutic indicators obtained from 240 substances and regrouped according the Anatomical Therapeutic Chemical Classification, we identified a four-profile model: (1) low (35.7% of patients); (2) mixed (28.7%); (3) cardio-metabolic (19.3%); and (4) psychotropic (16.4%). The binomial regression logistic model showed that profiles 2 and 3 (and 4 for AD), with a higher drug prescription conditioned probability against classic risk factors, were protective than profile 1 (OR = 0.421, p = 0.004; OR = 0.278, p = 0.000; OR = 0.457, p = 0.040, respectively), despite polypharmacy being significant in profiles 2 and 3 (mean > 7 drugs) vs. profile 1 (1.4 ± 1.6) (p = 0.000). Patients in the latter group were not significantly older, although being aged 65-79 years old quadrupled (OR = 4.217, p = 000) and being >79 tripled (OR = 2.945, p = 0.010) the conversion risk compared to patients <65 years old. According to the proposed analytical model, profiling the heterogeneous association of risk factors, which were taken prior to diagnosis, could be explored as an indicator of prior care and a predictor of conversion to dementia.
RESUMO
Neoplasm development in Multiple Sclerosis (MS) patients treated with disease-modifying therapies (DMTs) has been widely discussed. The aim of this work is to determine neoplasm frequency, relationship with the prescription pattern of DMTs, and influence of the patients' baseline characteristics. Data from 250 MS outpatients were collected during the period 1981-2019 from the medical records of the Neurology Service of the HUPM (Hospital Universitario Puerta del Mar)-in Southern Spain-and analysed using Cox models. Neoplasm prevalence was 24%, mainly located on the skin, with cancer prevalence as expected for MS (6.8%). Latency period from MS onset to neoplasm diagnosis was 10.4 ± 6.9 years (median 9.30 [0.9-30.5]). During the observation period ß-IFN (70.4% of patients), glatiramer acetate (30.4%), natalizumab (16.8%), fingolimod (24.8%), dimethyl fumarate (24.0%), alemtuzumab (6.0%), and teriflunomide (4.8%) were administered as monotherapy. Change of pattern in step therapy was significantly different in cancer patients vs unaffected individuals (p = 0.011) (29.4% did not receive DMTs [p = 0.000]). Extended Cox model: Smoking (HR = 3.938, CI 95% 1.392-11.140, p = 0.010), being female (HR = 2.006, 1.070-3.760, p = 0.030), and age at MS diagnosis (AGE-DG) (HR = 1.036, 1.012-1.061, p = 0.004) were risk factors for neoplasm development. Secondary progressive MS (SPMS) phenotype (HR = 0.179, 0.042-0.764, p = 0.020) and treatment-time with IFN (HR = 0.923, 0.873-0.977, p = 0.006) or DMF (HR = 0.725, 0.507-1.036, p = 0.077) were protective factors. Tobacco and IFN lost their negative/positive influence as survival time increased. Cox PH model: Tobacco/AGE-DG interaction was a risk factor for cancer (HR = 1.099, 1.001-1.208, p = 0.049), followed by FLM treatment-time (HR = 1.219, 0.979-1.517). In conclusion, smoking, female sex, and AGE-DG were risk factors, and SPMS and IFN treatment-time were protective factors for neoplasm development; smoking/AGE-DG interaction was the main cancer risk factor.
