Browsing by Subject "comorbidities"

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  • Kauppila, Timo; Laine, Merja K.; Honkasalo, Mikko; Raina, Marko; Eriksson, Johan G. (2016)
    Objective: To characterize dropouts from type-2 diabetes (T2D) care in communal primary health care. Design: An observational study. Setting: In a Finnish city, patients with T2D who had not contacted the public primary health care system during the past 12 months were identified with a computer based search and contacted by a trained diabetes nurse. Subjects: Dropouts from T2D treatment. Main outcome measures: Demographic factors, laboratory parameters, examinations, medications, and comorbidities. Results: Of the patients with T2D, 10% (n=356) were dropouts and 60% of them were men. Median HbA(1c) was 6.5 (QR for 25% and 75%: 6.0, 7.7) %, (45 [42,61] mmol/mol). Of the dropouts, 14% had HbA(1c)9.0% (75mmol/mol), and these patients were younger than the other dropouts (mean age 54.4 [SD 10.8] years vs. 60.6 [9.4] years, p Conclusions: Ten percent of T2D patients were dropouts of whom those with a poor glycaemic control were younger than the other dropouts. BP and LDL cholesterol concentrations were non-optimal among the majority of the dropouts. Metformin was prescribed less frequently to the dropouts than is usual for T2D patients. The comorbidities were equally common among the dropouts as among the other T2D patients.
  • Suvisaari, Jaana; Mantere, Outi; Keinänen, Jaakko; Mäntylä, Teemu; Rikandi, Eva; Lindgren, Maija; Kieseppä, Tuula; Raij, Tuukka T. (2018)
    The outcome of first-episode psychosis (FEP) is highly variable, ranging from early sustained recovery to antipsychotic treatment resistance from the onset of illness. For clinicians, a possibility to predict patient outcomes would be highly valuable for the selection of antipsychotic treatment and in tailoring psychosocial treatments and psychoeducation. This selective review summarizes current knowledge of prognostic markers in FEP. We sought potential outcome predictors from clinical and sociodemographic factors, cognition, brain imaging, genetics, and blood-based biomarkers, and we considered different outcomes, like remission, recovery, physical comorbidities, and suicide risk. Based on the review, it is currently possible to predict the future for FEP patients to some extent. Some clinical features-like the longer duration of untreated psychosis (DUP), poor premorbid adjustment, the insidious mode of onset, the greater severity of negative symptoms, comorbid substance use disorders (SUDs), a history of suicide attempts and suicidal ideation and having non-affective psychosis-are associated with a worse outcome. Of the social and demographic factors, male gender, social disadvantage, neighborhood deprivation, dysfunctional family environment, and ethnicity may be relevant. Treatment non-adherence is a substantial risk factor for relapse, but a small minority of patients with acute onset of FEP and early remission may benefit from antipsychotic discontinuation. Cognitive functioning is associated with functional outcomes. Brain imaging currently has limited utility as an outcome predictor, but this may change with methodological advancements. Polygenic risk scores (PRSs) might be useful as one component of a predictive tool, and pharmacogenetic testing is already available and valuable for patients who have problems in treatment response or with side effects. Most blood-based biomarkers need further validation. None of the currently available predictive markers has adequate sensitivity or specificity used alone. However, personalized treatment of FEP will need predictive tools. We discuss some methodologies, such as machine learning (ML), and tools that could lead to the improved prediction and clinical utility of different prognosticmarkers in FEP. Combination of differentmarkers inMLmodels with a user friendly interface, or novel findings from e.g., molecular genetics or neuroimaging, may result in computer-assisted clinical applications in the near future.
  • Baker, Jason V.; Sharma, Shweta; Grund, Birgit; Rupert, Adam; Metcalf, Julia A.; Schechter, Mauro; Munderi, Paula; Aho, Inka; Emery, Sean; Babiker, Abdel; Phillips, Andrew; Lundgren, Jens D.; Neaton, James D.; Lane, H. Clifford; INSIGHT START Strategic Timing (2017)
    Background. The Strategic Timing of AntiRetroviral Treatment (START) trial demonstrated that immediate (at CD4+ > 500 cells/mu L) vs deferred (to CD4+ Methods. Biomarkers were measured from stored plasma prior to randomization and at month 8. Associations of baseline biomarkers with event risk were estimated with Cox regression, pooled across groups, adjusted for age, gender, and treatment group, and stratified by region. Mean changes over 8 months were estimated and compared between the immediate and deferred ART arms using analysis of covariance models, adjusted for levels at entry. Results. Baseline biomarker levels were available for 4299 START participants (92%). Mean follow-up was 3.2 years. Higher levels of IL-6 and D-dimer were the only biomarkers associated with risk for AIDS, SNA or death, as well as the individual components of SNA and AIDS events (HRs ranged 1.37-1.41 per 2-fold higher level), even after adjustment for baseline CD4+ count, HIV RNA level, and other biomarkers. At month 8, biomarker levels were lower in the immediate arm by 12%-21%. Conclusions. These data, combined with evidence from prior biomarker studies, demonstrate that IL-6 and D-dimer consistently predict clinical risk across a broad spectrum of CD4 counts for those both ART-naive and treated. Research is needed to identify disease-modifying treatments that target inflammation beyond the effects of ART.