Cognition in People Aged 80 Years and Older: Determinants and Predictors of Change from a Population-Based Representative Study in Germany.

BACKGROUND: The number of people aged 80 years and older (80+) will increase drastically in the upcoming decades. The preservation of cognitive functions will contribute to their quality of life and independence. OBJECTIVES: To identify determinants of cognition and predictors of change in cognitive performance in the population 80+. DESIGN: Cross-sectional and longitudinal population-based on the representative NRW80+ survey. SETTING: Randomly drawn cases of people aged 80+ from the municipal registration offices, including people living in private homes and institutional settings. PARTICIPANTS: The participants in the cross-sectional sample (N=1503, 65.5%female) were 84.7 years old (95%CI[84.5,85.0]) and had 12.3 years of education (95%CI[12.1,12.4]). The participants in the longitudinal sample (N=840, 62.5%female) were 84.9 years old (95%CI[84.6,85.2]) and had 12.3 years of education (95%CI[12.0,12.5]). MEASUREMENTS: The cognitive screening DemTect, age, sex, education, and social, physical, and cognitive lifestyle activities, as well as subjective general health status and depressive symptoms, were assessed at baseline and 24-month follow-up. RESULTS: Younger age, more years of education, and more cognitive lifestyle activities were identified as the most consistent determinants of both better cognitive performance and preservation of cognitive performance for both global cognition as well as the DemTect subtests on memory and executive functions. CONCLUSIONS: Our findings reveal that commonly investigated determinants of, and change in, cognitive performance are valid for the people 80+ and highlight the importance of cognitive lifestyle activities for cognitive health. The maintenance of cognitive functions is a key aspect of healthy aging in terms of preserving independence in people 80+.

Translational research in nephrology: prognosis.

Translational research aims at reducing the gap between the results of studies focused on diagnosis, prognosis and therapy, and every day clinical practice. Prognosis is an essential component of clinical medicine. It aims at estimating the risk of adverse health outcomes in individuals, conditional to their clinical and non-clinical characteristics. There are three fundamental steps in prognostic research: development studies, in which the researcher identifies predictors, assigns the weights to each predictor, and assesses the model's accuracy through calibration, discrimination and risk reclassification; validation studies, in which investigators test the model's accuracy in an independent cohort of individuals; and impact studies, in which researchers evaluate whether the use of a prognostic model by clinicians improves their decision-making and patient outcome. This article aims at clarifying how to reduce the disconnection between the promises of prognostic research and the delivery of better individual health.

Prognostic value of a brief loneliness questionnaire for patients with coronary heart disease: Proposal for a prediction model.

BACKGROUND: In patients with coronary heart disease (CHD), loneliness is associated with increased risk of morbidity and mortality. No predictive tool is available to detect patients who are influenced by loneliness to a degree that impacts mortality. AIM: To: (i) propose a prediction model that detects patients influenced by loneliness to a degree that increases one-year all-cause mortality, (ii) evaluate model classification performance of the prediction model, and (iii) investigate potential questionnaire response errors. METHOD: A cohort of patients with CHD (n = 7169) responded to a national cross-sectional survey, including two questions on loneliness. Information on cohabitation and follow-up information on one-year all-cause mortality were obtained from national registers. Prediction model development was based on the prognostic values of item responses in the questionnaire on loneliness and of cohabitation, evaluated with Cox-proportional Hazards Ratio (HR). Item responses which significantly predicted one-year mortality were included in the high-risk loneliness (HiRL) prediction model. Sensitivity, specificity and likelihood ratio were calculated to evaluate model classification performance. Sources of response errors were evaluated using verbal probing technique in an additional cohort (n = 7). The TRIPOD checklist has been used to ensure transparent reporting. RESULTS: Two item responses significantly predicted one-year mortality HR = 2.24 (95%CI = 1.24-4.03) and HR = 2.65 (95%CI = 1.32-5.32) and were thus included in the model. Model classification performance showed a likelihood ratio of 1.89. Response error was evaluated as low. CONCLUSION: Based on the prognostic value in a loneliness questionnaire, a prediction model suitable to screen patients with CHD for high-risk loneliness was suggested. RELEVANCE TO CLINICAL PRACTICE: The HiRL prediction model is a short and easy-to-use screening tool that offers clinical staff to identify patients with CHD who are influenced by loneliness to a degree that impacts mortality. However, further evaluation of model performance and questionnaire validation is recommended before integrating the model into clinical practice.

Predictors for mortality due to acute exacerbation of COPD in primary care: Derivation of a clinical prediction rule in a multicentre cohort study.

BACKGROUND: In primary care (PC), 80% of the acute exacerbations of chronic obstructive pulmonary disease (AECOPD) are treated. However, no predictive model has been derived or validated for use in PC to help general practitioners make decisions about these patients. OBJECTIVES: To derive a clinical prediction rule for mortality from any cause 30 days after the last PC visit. METHODS: Between December 2013 and November 2014, we performed a cohort study with people aged 40 and over who were treated for AECOPD in 148 health centres in Spain. We recorded demographic variables, past medical history, signs, and symptoms of the patients and derived a logistic regression model. RESULTS: In the analysis, 1,696 cases of AECOPD were included and 17 patients (1%) died during follow-up. A clinical prediction rule was derived based on the exacerbations suffered in the last 12 months, age, and heart rate, displaying an area under the receiver operating characteristic curve of 0.792 (95% confidence interval, 0.692-0.891) and good calibration. CONCLUSION: This rule stratifies patients into three categories of risk and suggests to the physician a different action for each category: managing low-risk patients in PC, referring high-risk patients to hospitals and taking other criteria into account for decision-making in patients with moderate risk. These findings suggest that it is possible to accurately estimate the risk of death due to AECOPD without complex devices. Future studies on external validation and impact assessment are needed before this prediction rule may be used in clinical practice.

Supplementing prediction by EuroSCORE with social and patient-reported measures among patients undergoing cardiac surgery.

OBJECTIVES: The risk of poor outcomes is traditionally attributed to biological and physiological processes in cardiac surgery. However, evidence exists that other factors, such as emotional, behavioral, social, and functional, are predictive of poor outcomes. Objectives were to evaluate the predictive value of several emotional, social, functional, and behavioral factors on four outcomes: death within 90 days, prolonged stay in intensive care, prolonged hospital admission, and readmission within 90 days following cardiac surgery. METHODS: This prospective study included adults undergoing cardiac surgery 2013-2014, including information on register-based socioeconomic factors and self-reported health in a nested subsample. Logistic regression analyses to determine the association and incremental value of each candidate predictor variable were conducted. Multiple regression analyses were used to determine the incremental value of each candidate predictor variable, as well as discrimination and calibration based on the area under the curve (AUC) and Brier score. RESULTS: Of 3217 patients, 3% died, 9% had prolonged intensive care stay, 51% had prolonged hospital admission, and 39% were readmitted to hospital. Patients living alone (odds ratio, 1.19; 95% confidence interval, 1.02-1.38), with lower educational levels (1.27; 1.04-1.54) and low health-related quality of life (1.43; 1.02-2.01) had prolonged hospital admission. Analyses revealed living alone as predictive of prolonged intensive care unit (ICU) stay (Brier, 0.08; AUC, 0.68), death (0.03; 0.71), and prolonged hospital admission (0.24; 0.62). CONCLUSION: Living alone was found to supplement EuroSCORE in predicting death, prolonged hospital admission, and prolonged ICU stay following cardiac surgery. Low educational level and impaired health-related quality of life were, furthermore, predictive of prolonged hospital admission.

Prognostic Research in Traumatic Brain Injury: Markers, Modeling, and Methodological Principles.

Prognostic assessment in traumatic brain injury (TBI) is embedded deeply in clinical care. Considering the limitations of current prognostic indicators, there is increasing interest in understanding the role of new biomarkers, and in finding other prognostic indicators of long-term outcomes following TBI. New prognostic indicators may result in the development of more accurate prediction models that could be useful for both risk stratification and clinical decision making. We aimed to review methodological issues and provide tentative guidelines for prognostic research in TBI. Prognostic factor research focuses on the role of a specific patient or disease-related characteristic in relation to outcome. Typically, univariable relations of the prognostic factor are studied, followed by analyses adjusting for other variables related to the outcome. Following existing guidelines, we emphasize the importance of transparent reporting of patient and specimen characteristics, study design, clinical end-points, and statistical analysis. Prognostic model research considers combinations of predictors, with challenges for model specification, estimation, evaluation, validation, and presentation. We highlight modern approaches and opportunities related to missing values, exploration of non-linear effects, and assessing between-study heterogeneity. Prognostic research in TBI can be improved if key methodological principles are adhered to and when research is performed in collaboration among multiple centers to ensure generalizability.

Statistical methods to assess the prognostic value of risk prediction rules in clinical research.

Prognosis aims at estimating the future course of a given disease in probabilistic terms. As in diagnosis, where clinicians are interested in knowing the accuracy of a new test to identify patients affected by a given disease, in prognosis they wish to accurately identify patients at risk of a future event conditional to one or more prognostic factors. Thus, accurate risk predictions play a primary role in all fields of clinical medicine and in geriatrics as well because they can help clinicians to tailor the intensity of a treatment and to schedule clinical surveillance according to the risk of the concerned patient. Statistical methods able to evaluate the prognostic accuracy of a risk score demand the assessment of discrimination (the Harrell's C-index), calibration (Hosmer-May test) and risk reclassification abilities (IDI, an index of risk reclassification) of the same risk prediction rule whereas, in spite of the popular belief that traditional statistical techniques providing relative measures of effect (such as the hazard ratio derived by Cox regression analysis or the odds ratio obtained by logistic regression analysis) could be per se enough to assess the prognostic value of a biomarker or of a risk score. In this paper we provide a brief theoretical background of each statistical test and a practical approach to the issue. For didactic purposes, in the paper we also provide a dataset (n = 40) to allow the reader to train in the application of the proposed statistical methods.

Nothing wrong about change: the adequate choice of the dependent variable and design in prediction of cognitive training success.

BACKGROUND: Even though investigating predictors of intervention success (e.g Cognitive Training, CT) is gaining more and more interest in the light of an individualized medicine, results on specific predictors of intervention success in the overall field are mixed and inconsistent due to different and sometimes inappropriate statistical methods used. Therefore, the present paper gives a guidance on the appropriate use of multiple regression analyses to identify predictors of CT and similar non-pharmacological interventions. METHODS: We simulated data based on a predefined true model and ran a series of different analyses to evaluate their performance in retrieving the true model coefficients. The true model consisted of a 2 (between: experimental vs. control group) × 2 (within: pre- vs. post-treatment) design with two continuous predictors, one of which predicted the success in the intervention group and the other did not. In analyzing the data, we considered four commonly used dependent variables (post-test score, absolute change score, relative change score, residual score), five regression models, eight sample sizes, and four levels of reliability. RESULTS: Our results indicated that a regression model including the investigated predictor, Group (experimental vs. control), pre-test score, and the interaction between the investigated predictor and the Group as predictors, and the absolute change score as the dependent variable seemed most convenient for the given experimental design. Although the pre-test score should be included as a predictor in the regression model for reasons of statistical power, its coefficient should not be interpreted because even if there is no true relationship, a negative and statistically significant regression coefficient commonly emerges. CONCLUSION: Employing simulation methods, theoretical reasoning, and mathematical derivations, we were able to derive recommendations regarding the analysis of data in one of the most prevalent experimental designs in research on CT and external predictors of CT success. These insights can contribute to the application of considered data analyses in future studies and facilitate cumulative knowledge gain.

Pragmatic methods for reviewing exceptionally large bodies of evidence: systematic mapping review and overview of systematic reviews using lung cancer survival as an exemplar.

INTRODUCTION: Lung cancer (LC) is the most common cause of cancer death in the world and associated with significant economic burden. We conducted a review of published literature to identify prognostic factors associated with LC survival and determine which may be modifiable and could be targeted to improve outcomes. METHODS: The exceptionally large volume of LC prognostic research required a new staged approach to reviewing the literature. This comprised an initial mapping review of existing reviews or meta-analyses, based on titles and abstracts, followed by an overview of systematic reviews evaluating factors that independently contribute to lung cancer survival. The overview of reviews was based on full text papers and incorporated a more in-depth assessment of reviews evaluating modifiable factors. RESULTS: A large volume of published systematic reviews and meta-analyses were identified, but very few focused on modifiable factors for LC survival. Several modifiable factors were identified, which are potential candidates for targeted interventions aiming to improve cancer outcomes. The mapping review included 398 reviews, of which 207 investigated the independent effect of prognostic factors on lung cancer survival. The most frequently evaluated factors were novel biomarkers (86 biomarkers in 138 reviews). Only 15 modifiable factors were investigated in 20 reviews. Those associated with significant survival improvement included normal BMI/less weight loss, good performance status, not smoking/quitting after diagnosis, good pre-treatment quality of life, small gross volume tumour, early-stage tumour, lung resection undertaken by a thoracic/cardiothoracic surgeon, care being discussed by a multidisciplinary team, and timeliness of care. CONCLUSIONS: The study utilised a novel approach for reviewing an extensive and complicated body of research evidence. It enabled us to address a broad research question and focus on a specific area of priority. The staged approach ensured the review remained relevant to the stakeholders throughout, whilst maintaining the use of objective and transparent methods. It also provided important information on the needs of future research. However, it required extensive planning, management, and ongoing reviewer training.

Development of Prediction Models for Sick Leave Due to Musculoskeletal Disorders.

Purpose The aim of this study was to develop prediction models to determine the risk of sick leave due to musculoskeletal disorders (MSD) in non-sick listed employees and to compare models for short-term (i.e., 3 months) and long-term (i.e., 12 months) predictions. Methods Cohort study including 49,158 Dutch employees who participated in occupational health checks between 2009 and 2015 and sick leave data recorded during 12 months follow-up. Prediction models for MSD sick leave within 3 and 12 months after the health check were developed with logistic regression analysis using routinely assessed health check variables. The performance of the prediction models was evaluated with explained variance (Nagelkerke's R-square), calibration (Hosmer-Lemeshow test) and discrimination (area under the receiver operating characteristic curve, AUC) measures. Results A total of 376 (0.8%) and 1193 (2.4%) employees had MSD sick leave within 3 and 12 months after the health check. The prediction models included similar predictor variables (educational level, musculoskeletal complaints, distress, supervisor social support, work-home interference, intrinsic motivation, development opportunities, and work pace). The explained variances were 7.6% and 8.8% for the model with 3 and 12 months follow-up, respectively. Both prediction models showed adequate calibration and discriminated between employees with and without MSD sick leave 3 months (AUC = 0.761; Interquartile range [IQR] 0.759-0.763) and 12 months (AUC = 0.740; IQR 0.738-0.741) after the health check. Conclusion The prediction models could be used to determine the risk of MSD sick leave in non-sick listed employees and invite them to preventive consultations with occupational health providers.

Meta-analysis of non-linear exposure-outcome relationships using individual participant data: A comparison of two methods.

Non-linear exposure-outcome relationships such as between body mass index (BMI) and mortality are common. They are best explored as continuous functions using individual participant data from multiple studies. We explore two two-stage methods for meta-analysis of such relationships, where the confounder-adjusted relationship is first estimated in a non-linear regression model in each study, then combined across studies. The "metacurve" approach combines the estimated curves using multiple meta-analyses of the relative effect between a given exposure level and a reference level. The "mvmeta" approach combines the estimated model parameters in a single multivariate meta-analysis. Both methods allow the exposure-outcome relationship to differ across studies. Using theoretical arguments, we show that the methods differ most when covariate distributions differ across studies; using simulated data, we show that mvmeta gains precision but metacurve is more robust to model mis-specification. We then compare the two methods using data from the Emerging Risk Factors Collaboration on BMI, coronary heart disease events, and all-cause mortality (>80 cohorts, >18 000 events). For each outcome, we model BMI using fractional polynomials of degree 2 in each study, with adjustment for confounders. For metacurve, the powers defining the fractional polynomials may be study-specific or common across studies. For coronary heart disease, metacurve with common powers and mvmeta correctly identify a small increase in risk in the lowest levels of BMI, but metacurve with study-specific powers does not. For all-cause mortality, all methods identify a steep U-shape. The metacurve and mvmeta methods perform well in combining complex exposure-disease relationships across studies.

Reply: Prediction of the Left Ventricular Functional Outcome by Myocardial Extracellular Volume Fraction Measured Using Magnetic Resonance Imaging; Methodological Issue

No abstract available.

Bridging the etiologic and prognostic outlooks in individualized assessment of absolute risk of an illness: application in lung cancer.

Assessment of individual risk of illness is an important activity in preventive medicine. Development of risk-assessment models has heretofore relied predominantly on studies involving follow-up of cohort-type populations, while case-control studies have generally been considered unfit for this purpose. To present a method for individualized assessment of absolute risk of an illness (as illustrated by lung cancer) based on data from a 'non-nested' case-control study. We used data from a case-control study conducted in Montreal, Canada in 1996-2001. Individuals diagnosed with lung cancer (n = 920) and age- and sex-matched lung-cancer-free subjects (n = 1288) completed questionnaires documenting life-time cigarette-smoking history and occupational, medical, and family history. Unweighted and weighted logistic models were fitted. Model overfitting was assessed using bootstrap-based cross-validation and 'shrinkage.' The discriminating ability was assessed by the c-statistic, and the risk-stratifying performance was assessed by examination of the variability in risk estimates over hypothetical risk-profiles. In the logistic models, the logarithm of incidence-density of lung cancer was expressed as a function of age, sex, cigarette-smoking history, history of respiratory conditions and exposure to occupational carcinogens, and family history of lung cancer. The models entailed a minimal degree of overfitting ('shrinkage' factor: 0.97 for both unweighted and weighted models) and moderately high discriminating ability (c-statistic: 0.82 for the unweighted model and 0.66 for the weighted model). The method's risk-stratifying performance was quite high. The presented method allows for individualized assessment of risk of lung cancer and can be used for development of risk-assessment models for other illnesses.

Risk prediction models.

Prognostic research focuses on the prediction of the future course of a given disease in probability terms. Prognostication is performed by clinical decision makers by using risk prediction models that allow us to estimate the probability that a specific event occurs in a given patient over a predefined time period conditional on prognostic factors (predictors). Before application in clinical practice, risk prediction models should be properly validated by assessing their discrimination and calibration, or explained variation. Reclassification analyses allow us to evaluate the gain in risk prediction by using a new model compared with an established one. We discuss the concepts of developing and validating risk prediction models by means of two examples, the Framingham risk calculator for prediction of coronary heart disease (CHD), and the recently published Renal Risk Score to predict progression of chronic kidney disease (CKD).