Differences in Health at Age 100 According to Sex: Population-Based Cohort Study of Centenarians Using Electronic Health Records.
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ABSTRACT:
SUBMITTER: Hazra NC
PROVIDER: S-EPMC4745036 | biostudies-other | 2015 Jul
REPOSITORIES: biostudies-other
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Project description:BackgroundThe existence of a super-select group of centenarians that demonstrates increased survivorship has been hypothesized. However, it is unknown if this super-select group possesses similar characteristics apart from extreme longevity.MethodsIn this study, we analyse high-quality health and survival data of Danish centenarians born in 1895, 1905 and 1910. We use Latent Class Analysis to identify unobserved health classes and to test whether these super-select lives share similar health characteristics.ResultsWe find that, even after age 100, a clear and distinct gradient in health exists and that this gradient is remarkably similar across different birth cohorts of centenarians. Based on the level of health, we identify three clusters of centenarians - robust, frail and intermediate - and show that these groups have different survival prospects. The most distinctive characteristic of the robust centenarians is the outperformance in different health dimensions (physical, functional and cognitive). Finally, we show that our health class categorizations are good predictors of the survival prospects of centenarians.ConclusionsThere is a clear stratification in health and functioning among those over 100 years of age and these differences are associated with survival beyond age 100.
| S-EPMC8252309 | biostudies-literature
Project description:AimsSeveral risk factors for incident heart failure (HF) have been previously identified, however large electronic health records (EHR) datasets may provide the opportunity to examine the consistency of risk factors across different subgroups from the general population.Methods and resultsWe used linked EHR data from 2000 to 2010 as part of the UK-based CALIBER resource to select a cohort of 871?687 individuals 55?years or older and free of HF at baseline. The primary endpoint was the first record of HF from primary or secondary care. Cox proportional hazards analysis was used to estimate hazard ratios for associations between risk factors and incident HF, separately for men and women and by age category: 55-64, 65-74, and >?75?years. During 5.8?years of median follow-up, a total of 47?987 incident HF cases were recorded. Age, social deprivation, smoking, sedentary lifestyle, diabetes, atrial fibrillation, chronic obstructive pulmonary disease, body mass index, haemoglobin, total white blood cell count and creatinine were associated with HF. Smoking, atrial fibrillation and diabetes showed stronger associations with incident HF in women compared to men.ConclusionWe confirmed associations of several risk factors with HF in this large population-based cohort across age and sex subgroups. Mainly modifiable risk factors and comorbidities are strongly associated with incident HF, highlighting the importance of preventive strategies targeting such risk factors for HF.
| S-EPMC7074015 | biostudies-literature
Project description:BackgroundWomen <50 years of age with coronary artery disease may represent a group at higher risk for recurrent ischemic events after percutaneous coronary intervention (PCI); however, no long-term, multicenter outcomes assessment exists in this population.Methods and resultsUsing the National Heart, Lung, and Blood Institute Dynamic Registry, we evaluated the association of sex and age on cardiovascular-related outcomes in 10,963 patients (3797 women, 394 <50 years) undergoing PCI and followed for 5 years. Death, myocardial infarction, coronary artery bypass graft surgery, and repeat PCI were primary outcomes comprising major adverse cardiovascular events. Although procedural success rates were similar by sex, the cumulative rate of major adverse cardiovascular events at 1 year was higher in young women (27.8 versus 19.9%; P=0.003), driven largely by higher rates of repeat revascularizations for target vessel or target lesion failure (coronary artery bypass graft surgery: 8.9% versus 3.9%, P<0.001, adjusted hazard ratio 2.4, 95% confidence interval 1.5-4.0; PCI: 19.0% versus 13.0%, P=0.005, adjusted hazard ratio 1.6, 95% confidence interval 1.2-2.2). At 5 years, young women remained at higher risk for repeat procedures (coronary artery bypass graft surgery: 10.7% versus 6.8%, P=0.04, adjusted hazard ratio 1.71, 95% confidence interval 1.01-2.88; repeat PCI [target vessel]: 19.7% versus 11.8%, P=0.002, adjusted hazard ratio 1.8, 95% confidence interval 1.24-2.82). Compared with older women, younger women remained at increased risk of major adverse cardiovascular events, whereas all outcome rates were similar in older women and men.ConclusionsYoung women, despite having less severe angiographic coronary artery disease, have an increased risk of target vessel and target lesion failure. The causes of this difference deserve further investigation.Clinical trial registrationURL: http://www.clinicaltrials.gov. Unique identifier: NCT00005677.
| S-EPMC4772169 | biostudies-literature
Project description:BACKGROUND:Validated algorithms to classify type 1 and 2 diabetes (T1D, T2D) are mostly limited to white pediatric populations. We conducted a large study in Hong Kong among children and adults with diabetes to develop and validate algorithms using electronic health records (EHRs) to classify diabetes type against clinical assessment as the reference standard, and to evaluate performance by age at diagnosis. METHODS:We included all people with diabetes (age at diagnosis 1.5-100?years during 2002-15) in the Hong Kong Diabetes Register and randomized them to derivation and validation cohorts. We developed candidate algorithms to identify diabetes types using encounter codes, prescriptions, and combinations of these criteria ("combination algorithms"). We identified 3 algorithms with the highest sensitivity, positive predictive value (PPV), and kappa coefficient, and evaluated performance by age at diagnosis in the validation cohort. RESULTS:There were 10,196 (T1D n?=?60, T2D n?=?10,136) and 5101 (T1D n?=?43, T2D n?=?5058) people in the derivation and validation cohorts (mean age at diagnosis 22.7, 55.9?years; 53.3, 43.9% female; for T1D and T2D respectively). Algorithms using codes or prescriptions classified T1D well for age at diagnosis <?20?years, but sensitivity and PPV dropped for older ages at diagnosis. Combination algorithms maximized sensitivity or PPV, but not both. The "high sensitivity for type 1" algorithm (ratio of type 1 to type 2 codes ? 4, or at least 1 insulin prescription within 90?days) had a sensitivity of 95.3% (95% confidence interval 84.2-99.4%; PPV 12.8%, 9.3-16.9%), while the "high PPV for type 1" algorithm (ratio of type 1 to type 2 codes ? 4, and multiple daily injections with no other glucose-lowering medication prescription) had a PPV of 100.0% (79.4-100.0%; sensitivity 37.2%, 23.0-53.3%), and the "optimized" algorithm (ratio of type 1 to type 2 codes ? 4, and at least 1 insulin prescription within 90?days) had a sensitivity of 65.1% (49.1-79.0%) and PPV of 75.7% (58.8-88.2%) across all ages. Accuracy of T2D classification was high for all algorithms. CONCLUSIONS:Our validated set of algorithms accurately classifies T1D and T2D using EHRs for Hong Kong residents enrolled in a diabetes register. The choice of algorithm should be tailored to the unique requirements of each study question.
| S-EPMC7038546 | biostudies-literature
Project description:Clinically recorded pain scores are abundant in patient health records but are rarely used in research. The use of this information could help improve clinical outcomes. For example, a recent report by the Institute of Medicine stated that ineffective use of clinical information contributes to undertreatment of patient subpopulations--especially women. This study used diagnosis-associated pain scores from a large hospital database to document sex differences in reported pain. We used de-identified electronic medical records from Stanford Hospital and Clinics for more than 72,000 patients. Each record contained at least 1 disease-associated pain score. We found over 160,000 pain scores in more than 250 primary diagnoses, and analyzed differences in disease-specific pain reported by men and women. After filtering for diagnoses with minimum encounter numbers, we found diagnosis-specific sex differences in reported pain. The most significant differences occurred in patients with disorders of the musculoskeletal, circulatory, respiratory and digestive systems, followed by infectious diseases, and injury and poisoning. We also discovered sex-specific differences in pain intensity in previously unreported diseases, including disorders of the cervical region, and acute sinusitis (P = .01, .017, respectively). Pain scores were collected during hospital encounters. No information about the use of pre-encounter over-the-counter medications was available. To our knowledge, this is the largest data-driven study documenting sex differences of disease-associated pain. It highlights the utility of electronic medical record data to corroborate and expand on results of smaller clinical studies. Our findings emphasize the need for future research examining the mechanisms underlying differences in pain.This article highlights the potential of electronic medical records to conduct large-scale pain studies. Our results are consistent with previous studies reporting pain differences between sexes and also suggest that clinicians should pay increased attention to this idea.
| S-EPMC3293998 | biostudies-literature
Project description:Determining the discrepancy between chronological and physiological age of patients is central to preventative and personalized care. Electronic medical records (EMR) provide rich information about the patient physiological state, but it is unclear whether such information can be predictive of chronological age. Here we present a deep learning model that uses vital signs and lab tests contained within the EMR of Mount Sinai Health System (MSHS) to predict chronological age. The model is trained on 377,686 EMR from patients of ages 18-85 years old. The discrepancy between the predicted and real chronological age is then used as a proxy to estimate physiological age. Overall, the model can predict the chronological age of patients with a standard deviation error of ∼7 years. The ages of the youngest and oldest patients were more accurately predicted, while patients of ages ranging between 40 and 60 years were the least accurately predicted. Patients with the largest discrepancy between their physiological and chronological age were further inspected. The patients predicted to be significantly older than their chronological age have higher systolic blood pressure, higher cholesterol, damaged liver, and anemia. In contrast, patients predicted to be younger than their chronological age have lower blood pressure and shorter stature among other indicators; both groups display lower weight than the population average. Using information from ∼10,000 patients from the entire cohort who have been also profiled with SNP arrays, genome-wide association study (GWAS) uncovers several novel genetic variants associated with aging. In particular, significant variants were mapped to genes known to be associated with inflammation, hypertension, lipid metabolism, height, and increased lifespan in mice. Several genes with missense mutations were identified as novel candidate aging genes. In conclusion, we demonstrate how EMR data can be used to assess overall health via a scale that is based on deviation from the patient's predicted chronological age.
| S-EPMC5716867 | biostudies-literature
Project description:BACKGROUND:Clinical trials show benefit from lowering systolic blood pressure (SBP) in people ?80 years of age, but nonrandomized epidemiological studies suggest lower SBP may be associated with higher mortality. This study aimed to evaluate associations of SBP with all-cause mortality by frailty category >80 years of age and to evaluate SBP trajectories before death. METHODS:A population-based cohort study was conducted using electronic health records of 144?403 participants ?80 years of age registered with family practices in the United Kingdom from 2001 to 2014. Participants were followed for ?5 years. Clinical records of SBP were analyzed. Frailty status was classified using the e-Frailty Index into the categories of fit, mild, moderate, and severe. All-cause mortality was evaluated by frailty status and mean SBP in Cox proportional-hazards models. SBP trajectories were evaluated using person months as observations, with mean SBP and antihypertensive treatment status estimated for each person month. Fractional polynomial models were used to estimate SBP trajectories over 5 years before death. RESULTS:During follow-up, 51?808 deaths occurred. Mortality rates increased with frailty level and were greatest at SBP <110 mm?Hg. In fit women, mortality was 7.7 per 100 person years at SBP 120 to 139 mm?Hg, 15.2 at SBP 110 to 119 mm?Hg, and 22.7 at SBP <110 mm?Hg. For women with severe frailty, rates were 16.8, 25.2, and 39.6, respectively. SBP trajectories showed an accelerated decline in the last 2 years of life. The relative odds of SBP <120 mm?Hg were higher in the last 3 months of life than 5 years previously in both treated (odds ratio, 6.06; 95% confidence interval, 5.40-6.81) and untreated (odds ratio, 6.31; 95% confidence interval, 5.30-7.52) patients. There was no evidence of intensification of antihypertensive therapy in the final 2 years of life. CONCLUSIONS:A terminal decline of SBP in the final 2 years of life suggests that nonrandomized epidemiological associations of low SBP with higher mortality may be accounted for by reverse causation if participants with lower blood pressure values are closer, on average, to the end of life.
| S-EPMC5472195 | biostudies-literature
Project description:Although the incidence of dementia increases exponentially with age, some individuals reach more than 100 years with fully retained cognitive abilities. To identify the characteristics associated with the escape or delay of cognitive decline, we initiated the 100-plus Study ( www.100plus.nl ). The 100-plus Study is an on-going prospective cohort study of Dutch centenarians who self-reported to be cognitively healthy, their first-degree family members and their respective partners. We collect demographics, life history, medical history, genealogy, neuropsychological data and blood samples. Centenarians are followed annually until death. PET-MRI scans and feces donation are optional. Almost 30% of the centenarians agreed to post-mortem brain donation. To date (September 2018), 332 centenarians were included in the study. We analyzed demographic statistics of the first 300 centenarians (25% males) included in the cohort. Centenarians came from higher socio-economic classes and had higher levels of education compared to their birth cohort; alcohol consumption of centenarians was similar, and most males smoked during their lifetime. At baseline, the centenarians had a median MMSE score of 25 points (IQR 22.0-27.5); most centenarians lived independently, retained hearing and vision abilities and were independently mobile. Mortality was associated with cognitive functioning: centenarians with a baseline MMSE score ≥ 26 points had a mortality percentage of 17% per annual year in the second year after baseline, while centenarians with a baseline MMSE score < 26 points had a mortality of 42% per annual year (p = 0.003). The cohort was 2.1-fold enriched with the neuroprotective APOE-ε2 allele relative to 60-80 year-old population controls (p = 4.8 × 10-7), APOE-ε3 was unchanged and the APOE-ε4 allele was 2.3-fold depleted (p = 6.3 × 10-7). Comprehensive characterization of the 100-plus cohort of cognitively healthy centenarians might reveal protective factors that explain the physiology of long-term preserved cognitive health.
| S-EPMC6290855 | biostudies-literature
Project description:BackgroundHip fracture is associated with immobility, morbidity, mortality, and high medical cost. Due to limited availability of dual-energy X-ray absorptiometry (DXA), hip fracture prediction models without using bone mineral density (BMD) data are essential. We aimed to develop and validate 10-year sex-specific hip fracture prediction models using electronic health records (EHR) without BMD.MethodsIn this retrospective, population-based cohort study, anonymized medical records were retrieved from the Clinical Data Analysis and Reporting System for public healthcare service users in Hong Kong aged ≥60 years as of 31 December 2005. A total of 161,051 individuals (91,926 female; 69,125 male) with complete follow-up from 1 January 2006 till the study end date on 31 December 2015 were included in the derivation cohort. The sex-stratified derivation cohort was randomly divided into 80% training and 20% internal testing datasets. An independent validation cohort comprised 3046 community-dwelling participants aged ≥60 years as of 31 December 2005 from the Hong Kong Osteoporosis Study, a prospective cohort which recruited participants between 1995 and 2010. With 395 potential predictors (age, diagnosis, and drug prescription records from EHR), 10-year sex-specific hip fracture prediction models were developed using stepwise selection by logistic regression (LR) and four machine learning (ML) algorithms (gradient boosting machine, random forest, eXtreme gradient boosting, and single-layer neural networks) in the training cohort. Model performance was evaluated in both internal and independent validation cohorts.FindingsIn female, the LR model had the highest AUC (0.815; 95% Confidence Interval [CI]: 0.805-0.825) and adequate calibration in internal validation. Reclassification metrics showed the LR model had better discrimination and classification performance than the ML algorithms. Similar performance was attained by the LR model in independent validation, with high AUC (0.841; 95% CI: 0.807-0.87) comparable to other ML algorithms. In internal validation for male, LR model had high AUC (0.818; 95% CI: 0.801-0.834) and it outperformed all ML models as indicated by reclassification metrics, with adequate calibration. In independent validation, the LR model had high AUC (0.898; 95% CI: 0.857-0.939) comparable to ML algorithms. Reclassification metrics demonstrated that LR model had the best discrimination performance.InterpretationEven without using BMD data, the 10-year hip fracture prediction models developed by conventional LR had better discrimination performance than the models developed by ML algorithms. Upon further validation in independent cohorts, the LR models could be integrated into the routine clinical workflow, aiding the identification of people at high risk for DXA scan.FundingHealth and Medical Research Fund, Health Bureau, Hong Kong SAR Government (reference: 17181381).
| S-EPMC9989633 | biostudies-literature
Project description:OBJECTIVE:This study aimed to estimate the association of frailty with incidence and mortality of fractures at different sites in people aged over 80 years. DESIGN:Cohort study. SETTING:UK family practices from 2001 to 2014. PARTICIPANTS:265?195 registered participants aged 80 years and older. MEASUREMENTS:Frailty status classified into 'fit', 'mild', 'moderate' and 'severe' frailty. Fractures, classified into non-fragility and fragility, including fractures of femur, pelvis, shoulder and upper arm, and forearm/wrist. Incidence of fracture, and mortality within 90 days and 1?year, were estimated. RESULTS:There were 28?643 fractures including: non-fragility fractures, 9101; femur, 12 501; pelvis, 2172; shoulder and upper arm, 4965; and forearm/wrist, 6315. The incidence of each fracture type was higher in women and increased with frailty category (femur, severe frailty compared with 'fit', incidence rate ratio (IRR) 2.4, 95% CI 2.3 to 2.6). Fractures of the femur (95-99 years compared with 80-84 years, IRR 2.7, 95% CI 2.6 to 2.9) and pelvis (IRR 2.9, 95% CI 2.5 to 3.3) were strongly associated with age but non-fragility and forearm fractures were not. Mortality within 90 days was greatest for femur fracture (adjusted HR, compared with forearm fracture 4.3, 95% CI 3.7 to 5.1). Mortality was higher in men and increased with age (HR 5.3, 95% CI 4.3 to 6.5 in those over 100 years compared with 80-84 years) but was less strongly associated with frailty category. Similar associations with fractures were seen at 1-year mortality. CONCLUSIONS:The incidence of fractures at all sites was higher in women and strongly associated with advancing frailty status, while the risk of mortality after a fracture was greater in men and was associated with age rather than frailty category.
| S-EPMC5781050 | biostudies-literature