RESUMEN
RESEARCH QUESTION: Are age at last childbirth and number of children, as facets of female reproductive health, related to individual lifespan or familial longevity? DESIGN: This observational study included 10,255 female participants from a multigenerational historical cohort, the LINKing System for historical family reconstruction (LINKS), and 1258 female participants from 651 long-lived families in the Leiden Longevity Study (LLS). Age at last childbirth and number of children, as outcomes of reproductive success, were compared with individual and familial longevity using the LINKS dataset. In addition, the genetic predisposition in the form of a polygenic risk score (PRS) for age at menopause was studied in relation to familial longevity using the LLS dataset. RESULTS: For each year increase in the age of the birth of the last child, a woman's lifespan increased by 0.06 years (22 days; Pâ¯=â¯0.002). The yearly risk for having a last child was 9% lower in women who survived to the oldest 10% of their birth cohort (hazard ratio 0.91, 95% CI 0.86-0.95). Women who came from long-living families did not have a higher mean age of last childbirth. There was no significant association between familial longevity and genetic predisposition to age at menopause. CONCLUSIONS: Female reproductive health associates with a longer lifespan. Familial longevity does not associate to extended reproductive health. Other factors in somatic maintenance that support a longer lifespan are likely to have an impact on reproductive health.
Asunto(s)
Longevidad , Humanos , Femenino , Persona de Mediana Edad , Anciano , Adulto , Reproducción/fisiología , Menopausia/fisiología , Anciano de 80 o más Años , Edad Materna , Estudios de CohortesRESUMEN
BACKGROUND: Developing predictive models for precision psychiatry is challenging because of unavailability of the necessary data: extracting useful information from existing electronic health record (EHR) data is not straightforward, and available clinical trial datasets are often not representative for heterogeneous patient groups. The aim of this study was constructing a natural language processing (NLP) pipeline that extracts variables for building predictive models from EHRs. We specifically tailor the pipeline for extracting information on outcomes of psychiatry treatment trajectories, applicable throughout the entire spectrum of mental health disorders ("transdiagnostic"). METHODS: A qualitative study into beliefs of clinical staff on measuring treatment outcomes was conducted to construct a candidate list of variables to extract from the EHR. To investigate if the proposed variables are suitable for measuring treatment effects, resulting themes were compared to transdiagnostic outcome measures currently used in psychiatry research and compared to the HDRS (as a gold standard) through systematic review, resulting in an ideal set of variables. To extract these from EHR data, a semi-rule based NLP pipeline was constructed and tailored to the candidate variables using Prodigy. Classification accuracy and F1-scores were calculated and pipeline output was compared to HDRS scores using clinical notes from patients admitted in 2019 and 2020. RESULTS: Analysis of 34 questionnaires answered by clinical staff resulted in four themes defining treatment outcomes: symptom reduction, general well-being, social functioning and personalization. Systematic review revealed 242 different transdiagnostic outcome measures, with the 36-item Short-Form Survey for quality of life (SF36) being used most consistently, showing substantial overlap with the themes from the qualitative study. Comparing SF36 to HDRS scores in 26 studies revealed moderate to good correlations (0.62-0.79) and good positive predictive values (0.75-0.88). The NLP pipeline developed with notes from 22,170 patients reached an accuracy of 95 to 99 percent (F1 scores: 0.38 - 0.86) on detecting these themes, evaluated on data from 361 patients. CONCLUSIONS: The NLP pipeline developed in this study extracts outcome measures from the EHR that cater specifically to the needs of clinical staff and align with outcome measures used to detect treatment effects in clinical trials.
Asunto(s)
Procesamiento de Lenguaje Natural , Psiquiatría , Registros Electrónicos de Salud , Humanos , Almacenamiento y Recuperación de la Información , Calidad de VidaRESUMEN
OBJECTIVE: To assess characteristics of deployment of MRI of the SI joints (MR-SI) in patients with suspected axial spondyloarthritis (SpA) before and after a targeted intervention. METHODS: In a retrospective chart review study, all MR-SI performed in the period 1 April 2004 to 31 December 2010 were collected. Inclusion criteria were complete patient data and MR-SI ordered by a rheumatologist for suspicion of axial SpA. MR-SI reports were graded as normal, suspected sacroiliitis or sacroiliitis. In April 2007 an intervention was made to improve deployment. Rheumatologists were provided with data on ordering behaviour, patient characteristics and MRI outcomes. An introduction on the effect of pretest chance on positive and negative predictive value was given; the burden for patients and costs was illustrated. An alternative behavioural strategy was offered in the form of a simple diagnostic algorithm. Percentages of MRIs and positive MRI for sacroiliitis were compared before and after intervention. RESULTS: From April 2004 to April 2007, 198 MR-SIs were performed, of which 166 (83.9%) were normal, 5 (2.5%) were suspicious and 27 (13.6%) were positive. After the intervention, patients displayed significantly more SpA features. More optimal patient selection resulted in 79 MR-SI requests, a decrease of 60.1%. Fifty-seven (72.2%) reports were normal, 0 were suspicious and 22 (27.8%) were positive. CONCLUSION: A simple, one-time, five-step feedback intervention resulted in a 60% reduction in MR-SI requests with a doubling of the percentage of MR-SI positive for sacroiliitis. This approach may benefit future research in areas with diagnostic uncertainty and suboptimal testing.