How old are you, really? Communicating chronic risk through 'effective age' of your body and organs

Short-term effects of announcing spirometric lung-age on smokers' attitudes: results from a Tunisian real-life pilot study

BACKGROUND

To determine the effects of informing smokers of their spirometric lung-age (SLA) on smoking cessation (SC) rates and tobacco consumption.

RESEARCH DESIGN AND METHODS

An interventional study was conducted in real-life through a humanitarian event led by the Red Crescent on 9 January 2022, in Hammam-Sousse (Sousse, Tunisia). The study comprised four steps: i) Medical questionnaire (general questionnaire, Fagerström test for cigarette dependence, SC motivation questionnaire); ii) Measurement of spirometric data; iii) SLA estimation and its announcement to participants; and iv) Self-reported evaluation of smoking behavior three months later through telephonic recall. Smokers were divided into groups (nondependent vs. dependent groups and insufficient/moderate motivation vs. high/very high motivation groups) and categories (ceased smoking, reduced consumption, maintained stable or increased consumption).

RESULTS

Fifty-two smokers were included (94% were males). Three months after the event, i) 9 (17%) smokers ceased smoking, ii) 39 (75%) smokers reduced their daily smoking consumption by 12 ± 8 cigarettes/day, and iii) 4 (8%) smokers maintained stable (n = 3) or increased (n = 1) consumption.

CONCLUSIONS

Informing smokers of their SLA led 92% of them to cease smoking or reduce their consumption. Announcing SLA could be an effective motivational tool and an easy-to-understand concept to help smokers cease their habit.

Mortality of type 2 diabetes in Germany: additional insights from Gompertz models

AIMS

The Gompertz law of mortality proclaims that human mortality rates in middle to old ages grow log-linearly with age and this law has been confirmed at multiple instances. We investigated if diabetes mortality in Germany also obeys to the Gompertz law and how this information helps to communicate diabetes mortality more intuitively.

METHODS

We analyzed all statutory health-insured persons in Germany in 2013 that were aged 30 years or older. Deaths in 2014 were recorded and given in 5-year age groups. We fitted weighted linear regression models (separately for females and males and for people with and without diabetes) and additionally computed the probability that a person with diabetes dies before a person of the same age and sex without diabetes, and the "diabetes age", that is, the additional years of mortality risk added to an individual's chronological age due to diabetes-related excess mortality.

RESULTS

We included N = 47,365,120 individuals, 6,541,181 of them with diabetes. In 2014, 763,228 deaths were recorded, among them 288,515 with diabetes. Diabetes mortality followed nearly perfectly Gompertz distributions. The probability that a person with diabetes dies before a person without diabetes was 61.9% for females and 63.3% for males.

CONCLUSIONS

Diabetes mortality for females and males aged 30 years or older in Germany in 2014 followed the Gompertz law of mortality. The survival information of the population with diabetes during a large part of the lifespan can thus be reduced to the two parameters of the Gompertz distribution.

Left ventricular hypertrophy as a risk factor for accelerated brain aging: Results from the Study of Health in Pomerania

Previous studies provided evidence for the importance of cardiac structure abnormalities, in particular greater left ventricular (LV) mass, for brain aging, but longitudinal studies are lacking to date. We included 926 individuals (median age 48 years; 53% women) from the TREND cohort of the Study of Health in Pomerania (SHIP) without reduced ejection fraction or a history of myocardial infarction. LV mass index (LVMI) was determined by echocardiography at baseline. Brain morphometric measurements were derived from magnetic resonance images at baseline and 7-year follow-up. Direct effects of baseline LVMI on brain morphometry at follow-up were estimated using linear regression models with adjustment for baseline brain morphometry. At baseline, median LVMI was 40 g/m2.7 and 241 individuals (26%) met the criterion of LV hypertrophy. After correction for multiple testing, baseline LVMI was directly associated with reduced global cortical thickness and increased cortical brain age at follow-up independent from hypertension and blood pressure. Exposure-outcome relations were nonlinear and significantly stronger in the upper half of the exposure distribution. Specifically, an increase in baseline LVMI from the 50% quantile to the 95% quantile was associated additional 2.7 years (95% confidence interval = [1.5 years, 3.8 years]) of cortical brain age at follow-up. Additional regional analyses yielded bilateral effects on multiple frontal cortical regions. Our findings highlight the role of cardiac structure in brain aging. LVMI constitutes an easily measurable marker that might help to identify persons at risk for cognitive impairment and dementia.

Biological age as a predictor of unplanned intensive care readmission during the same hospitalization

BACKGROUND

Biological age is increasingly being recognized as an important predictor of health but its utility in acute care setting remains uncertain.

OBJECTIVE

We assessed whether biological age on intensive care unit (ICU) admission can predict unplanned ICU readmission during the same hospitalization.

METHODS

The Levine PhenoAge model based on biomarkers of DNA methylation was used to determine each patient's biological age. The difference between PhenoAge and chronological age was indexed to the local context by regressing PhenoAge on chronological age using linear regression. A positive residual implied one's biological age was older than the corresponding chronological age compared to other patients - defined as PhenoAgeAccel.

RESULTS

Of the 2950 patients included, 153 (5.2%) had unplanned ICU readmission. Chronological age, Acute Physiology and Chronic Health Evaluation II score, the use of mechanical ventilation, vasopressor, or renal replacement therapy were not significantly different between those with and without readmission. PhenoAgeAccel was, however, more common among those who had unplanned ICU readmission (52% vs 43%, p =0.031). Quantitatively, the degree of phenotypical age above chronological age exhibited a 'dose-related' relationship with the risk of readmission (odds ratio 1.12, 95% confidence interval 1.01-1.24; p=0.040) after adjusting for chronological age, comorbidities, and severity of acute illness in the index (first) ICU admission.

CONCLUSION

Biological age was predictive of unplanned ICU readmission during the same hospitalization.

Biological age is superior to chronological age in predicting hospital mortality of the critically ill

Biological age is increasingly recognized as being more accurate than chronological age in determining chronic health outcomes. This study assessed whether biological age, assessed on intensive care unit (ICU) admission, can predict hospital mortality. This retrospective cohort study, conducted in a tertiary multidisciplinary ICU in Western Australia, used the Levine PhenoAge model to estimate each patient's biological age (also called PhenoAge). Each patient's PhenoAge was calibrated to generate a regression residual which was equivalent to biological age unexplained by chronological age in the local context. PhenoAgeAccel was a dichotomized measure of the residuals, and its presence suggested that one was biologically older than the corresponding chronological age. Of the 2950 critically ill adult patients analyzed, 291 died (9.9%) before hospital discharge. Both PhenoAge and its residuals (after regressing on chronological age) had a significantly better ability to differentiate between hospital survivors and non-survivors than chronological age (area under the receiver-operating-characteristic curve 0.648 and 0.654 vs. 0.547 respectively). Being phenotypically older than one's chronological age was associated with an increased risk of mortality (PhenoAgeAccel hazard ratio [HR] 1.997, 95% confidence interval [CI] 1.568-2.542; p = 0.001) in a dose-related fashion and did not reach a plateau until at least a 20-year gap. This adverse association remained significant (adjusted HR 1.386, 95% CI 1.077-1.784; p = 0.011) after adjusted for severity of acute illness and comorbidities. PhenoAgeAccel was more prevalent among those with pre-existing chronic cardiovascular disease, end-stage renal failure, cirrhosis, immune disease, diabetes mellitus, or those treated with immunosuppressive therapy. Being phenotypically older than one's chronological age was more common among those with comorbidities, and this was associated with an increased risk of mortality in a dose-related fashion in the critically ill that was not fully explained by comorbidities and severity of acute illness.

Repurposing electroencephalogram monitoring of general anaesthesia for building biomarkers of brain ageing: an exploratory study

Background

Electroencephalography (EEG) is increasingly used for monitoring the depth of general anaesthesia, but EEG data from general anaesthesia monitoring are rarely reused for research. Here, we explored repurposing EEG monitoring from general anaesthesia for brain-age modelling using machine learning. We hypothesised that brain age estimated from EEG during general anaesthesia is associated with perioperative risk.

Methods

We reanalysed four-electrode EEGs of 323 patients under stable propofol or sevoflurane anaesthesia to study four EEG signatures (95% of EEG power <8-13 Hz) for age prediction: total power, alpha-band power (8-13 Hz), power spectrum, and spatial patterns in frequency bands. We constructed age-prediction models from EEGs of a healthy reference group (ASA 1 or 2) during propofol anaesthesia. Although all signatures were informative, state-of-the-art age-prediction performance was unlocked by parsing spatial patterns across electrodes along the entire power spectrum (mean absolute error=8.2 yr; R2=0.65).

Results

Clinical exploration in ASA 1 or 2 patients revealed that brain age was positively correlated with intraoperative burst suppression, a risk factor for general anaesthesia complications. Surprisingly, brain age was negatively correlated with burst suppression in patients with higher ASA scores, suggesting hidden confounders. Secondary analyses revealed that age-related EEG signatures were specific to propofol anaesthesia, reflected by limited model generalisation to anaesthesia maintained with sevoflurane.

Conclusions

Although EEG from general anaesthesia may enable state-of-the-art age prediction, differences between anaesthetic drugs can impact the effectiveness and validity of brain-age models. To unleash the dormant potential of EEG monitoring for clinical research, larger datasets from heterogeneous populations with precisely documented drug dosage will be essential.

'Skeletal Age' for mapping the impact of fracture on mortality

Background

Fragility fracture is associated with an increased risk of mortality, but mortality is not part of doctor-patient communication. Here, we introduce a new concept called 'Skeletal Age' as the age of an individual's skeleton resulting from a fragility fracture to convey the combined risk of fracture and fracture-associated mortality for an individual.

Methods

We used the Danish National Hospital Discharge Register which includes the whole-country data of 1,667,339 adults in Denmark born on or before January 1, 1950, who were followed up to December 31, 2016 for incident low-trauma fracture and mortality. Skeletal age is defined as the sum of chronological age and the number of years of life lost (YLL) associated with a fracture. Cox's proportional hazards model was employed to determine the hazard of mortality associated with a specific fracture for a given risk profile, and the hazard was then transformed into YLL using the Gompertz law of mortality.

Results

During the median follow-up period of 16 years, there had been 307,870 fractures and 122,744 post-fracture deaths. A fracture was associated with between 1 and 7 years of life lost, with the loss being greater in men than women. Hip fractures incurred the greatest loss of life years. For instance, a 60-year-old individual with a hip fracture is estimated to have a skeletal age of 66 for men and 65 for women. Skeletal Age was estimated for each age and fracture site stratified by gender.

Conclusions

We propose 'Skeletal Age' as a new metric to assess the impact of a fragility fracture on an individual's life expectancy. This approach will enhance doctor-patient risk communication about the risks associated with osteoporosis.

Funding

National Health and Medical Research Council in Australia and Amgen Competitive Grant Program 2019.

Fukushima study for Engaging people with type 2 Diabetes in Behaviour Associated Change (FEEDBACK): study protocol for a cluster randomised controlled trial

BACKGROUND

The growing burden of type 2 diabetes mellitus (T2DM) and the rising cost of healthcare worldwide make it imperative to identify interventions that can promote sustained self-management behaviour in T2DM populations while minimising costs for healthcare systems. The present FEEDBACK study (Fukushima study for Engaging people with type 2 Diabetes in Behaviour Associated Change) aims to evaluate the effects of a novel behaviour change intervention designed to be easily implemented and scaled across a wide range of primary care settings.

METHODS

A cluster randomised controlled trial (RCT) with a 6-month follow-up will be conducted to evaluate the effects of the FEEDBACK intervention. FEEDBACK is a personalised, multi-component intervention intended to be delivered by general practitioners during a routine diabetes consultation. It consists of five steps aimed at enhancing doctor-patient partnership to motivate self-management behaviour: (1) communication of cardiovascular risks using a 'heart age' tool, (2) goal setting, (3) action planning, (4) behavioural contracting, and (5) feedback on behaviour. We aim to recruit 264 adults with T2DM and suboptimal glycaemic control from 20 primary care practices in Japan (cluster units) that will be randomly assigned to either the intervention or control group. The primary outcome measure will be the change in HbA1c levels at 6-month follow-up. Secondary outcome measures include the change in cardiovascular risk score, the probability to achieve the recommended glycaemic target (HbA1c <7.0% [53mmol/mol]) at 6-month follow-up, and a range of behavioural and psychosocial variables. The planned primary analyses will be carried out at the individual level, according to the intention-to-treat principle. Between-group comparisons for the primary outcome will be analysed using mixed-effects models. This study protocol received ethical approval from the research ethics committee of Kashima Hospital, Fukushima, Japan (reference number: 2022002).

DISCUSSION

This article describes the design of a cluster RCT that will evaluate the effects of FEEDBACK, a personalised, multicomponent intervention aimed at enhancing doctor-patient partnership to engage adults with T2DM more effectively in self-management behaviour.

TRIAL REGISTRATION

The study protocol was prospectively registered in the UMIN Clinical Trials Registry (UMIN-CTR ID UMIN000049643 assigned on 29/11/2022). On submission of this manuscript, recruitment of participants is ongoing.

Biological age calculators to motivate lifestyle change: Environmental scan of online tools and evaluation of behaviour change techniques

OBJECTIVE

"Biological age" calculators are widely used as a way of communicating health risk. This study evaluated the behaviour change techniques (BCTs) within such tools, underlying algorithm differences and suitability for people with varying health literacy.

METHODS

Two authors entered terms into Google (eg, biological/heart age) and recorded the first 50 results. A standard patient profile was entered into eligible biological age calculators. Evaluation was based on Michie et al's BCT taxonomy and a readability calculator.

RESULTS

From 4000 search results, 20 calculators were identified: 11 for cardiovascular age, 7 for general biological age and 2 for fitness age. The calculators gave variable results for the same 65-year-old profile: biological age ranged from younger to older (57-87 years), while heart age was always older (69-85+ years). Only 11/20 (55%) provided a reference explaining the underlying algorithm. The average reading level was Grade 10 (range 8.7-12.4; SD 1.44). The most common BCTs were salience of consequences, information about health consequences and credible source.

CONCLUSIONS

Biological age tools have highly variable results, BCTs and readability.

PRACTICE IMPLICATIONS

Developers are advised to use validated models, explain the result at the average Grade 8 reading level, and incorporate a clear call to action using evidence-based behaviour change techniques.

Artificial intelligence-estimated biological heart age using a 12-lead electrocardiogram predicts mortality and cardiovascular outcomes

Background

There is a paucity of data on artificial intelligence-estimated biological electrocardiography (ECG) heart age (AI ECG-heart age) for predicting cardiovascular outcomes, distinct from the chronological age (CA). We developed a deep learning-based algorithm to estimate the AI ECG-heart age using standard 12-lead ECGs and evaluated whether it predicted mortality and cardiovascular outcomes.

Methods

We trained and validated a deep neural network using the raw ECG digital data from 425,051 12-lead ECGs acquired between January 2006 and December 2021. The network performed a holdout test using a separate set of 97,058 ECGs. The deep neural network was trained to estimate the AI ECG-heart age [mean absolute error, 5.8 ± 3.9 years; R-squared, 0.7 (r = 0.84, p < 0.05)].

Findings

In the Cox proportional hazards models, after adjusting for relevant comorbidity factors, the patients with an AI ECG-heart age of 6 years older than the CA had higher all-cause mortality (hazard ratio (HR) 1.60 [1.42-1.79]) and more major adverse cardiovascular events (MACEs) [HR: 1.91 (1.66-2.21)], whereas those under 6 years had an inverse relationship (HR: 0.82 [0.75-0.91] for all-cause mortality; HR: 0.78 [0.68-0.89] for MACEs). Additionally, the analysis of ECG features showed notable alterations in the PR interval, QRS duration, QT interval and corrected QT Interval (QTc) as the AI ECG-heart age increased.

Conclusion

Biological heart age estimated by AI had a significant impact on mortality and MACEs, suggesting that the AI ECG-heart age facilitates primary prevention and health care for cardiovascular outcomes.

The Development and Evaluation of "Life Age"-a Primary Prevention and Population-Focused Risk Communication Tool: Feasibility Study With a Single-Arm Repeated Measures Design

BACKGROUND

Communicating cardiovascular risk to the general population requires forms of communication that can enhance risk perception and stimulate lifestyle changes associated with reduced cardiovascular risk.

OBJECTIVE

The aim of this study was to evaluate the motivational potential of a novel lifestyle risk assessment ("Life Age") based on factors predictive of both premature mortality and psychosocial well-being.

METHODS

A feasibility study with a single-arm repeated measures design was conducted to evaluate the potential efficacy of Life Age on motivating lifestyle changes. Participants were recruited via social media, completed a web-based version of the Life Age questionnaire at baseline and at follow-up (8 weeks), and received 23 e-newsletters based on their Life Age results along with a mobile tracker. Participants' estimated Life Age scores were analyzed for evidence of lifestyle changes made. Quantitative feedback of participants was also assessed.

RESULTS

In total, 18 of 27 participants completed the two Life Age tests. The median baseline Life Age was 1 year older than chronological age, which was reduced to -1.9 years at follow-up, representing an improvement of 2.9 years (P=.02). There were also accompanying improvements in Mediterranean diet score (P=.001), life satisfaction (P=.003), and sleep (P=.05). Quantitative feedback assessment indicated that the Life Age tool was easy to understand, helpful, and motivating.

CONCLUSIONS

This study demonstrated the potential benefit of a novel Life Age tool in generating a broad set of lifestyle changes known to be associated with clinical risk factors, similar to "Heart Age." This was achieved without the recourse to expensive biomarker tests. However, the results from this study suggest that the motivated lifestyle changes improved both healthy lifestyle risks and psychosocial well-being, consistent with the approach of Life Age in merging the importance of a healthy lifestyle and psychosocial well-being. Further evaluation using a larger randomized controlled trial is required to fully evaluate the impact of the Life Age tool on lifestyle changes, cardiovascular disease prevention, and overall psychosocial well-being.

A reusable benchmark of brain-age prediction from M/EEG resting-state signals

Population-level modeling can define quantitative measures of individual aging by applying machine learning to large volumes of brain images. These measures of brain age, obtained from the general population, helped characterize disease severity in neurological populations, improving estimates of diagnosis or prognosis. Magnetoencephalography (MEG) and Electroencephalography (EEG) have the potential to further generalize this approach towards prevention and public health by enabling assessments of brain health at large scales in socioeconomically diverse environments. However, more research is needed to define methods that can handle the complexity and diversity of M/EEG signals across diverse real-world contexts. To catalyse this effort, here we propose reusable benchmarks of competing machine learning approaches for brain age modeling. We benchmarked popular classical machine learning pipelines and deep learning architectures previously used for pathology decoding or brain age estimation in 4 international M/EEG cohorts from diverse countries and cultural contexts, including recordings from more than 2500 participants. Our benchmarks were built on top of the M/EEG adaptations of the BIDS standard, providing tools that can be applied with minimal modification on any M/EEG dataset provided in the BIDS format. Our results suggest that, regardless of whether classical machine learning or deep learning was used, the highest performance was reached by pipelines and architectures involving spatially aware representations of the M/EEG signals, leading to R^2 scores between 0.60-0.71. Hand-crafted features paired with random forest regression provided robust benchmarks even in situations in which other approaches failed. Taken together, this set of benchmarks, accompanied by open-source software and high-level Python scripts, can serve as a starting point and quantitative reference for future efforts at developing M/EEG-based measures of brain aging. The generality of the approach renders this benchmark reusable for other related objectives such as modeling specific cognitive variables or clinical endpoints.

Decline in forced vital capacity in subjects with systemic sclerosis-associated interstitial lung disease in the SENSCIS trial compared with healthy reference subjects

BACKGROUND

The forced vital capacity (FVC) of healthy individuals depends on their age, sex, ethnicity and height. Systemic sclerosis-associated interstitial lung disease (SSc-ILD) is characterised by loss of FVC. We compared FVC values in the subjects with SSc-ILD in the SENSCIS trial of nintedanib versus placebo with values from hypothetical matched healthy references.

METHODS

The SENSCIS trial enrolled subjects with SSc with first non-Raynaud symptom in the prior ≤ 7 years, extent of fibrotic ILD on HRCT ≥ 10%, and FVC ≥ 40% predicted. FVC at baseline and decline in FVC over 52 weeks were compared with FVC values in hypothetical healthy reference subjects matched 1:1 to the subjects in the trial for age, sex, ethnicity and height, determined using equations published by the European Respiratory Society Global Lung Function Initiative.

RESULTS

At baseline, mean (SD) FVC was 2460 (737) mL in the nintedanib group (n = 287) compared with 3403 (787) mL in the hypothetical matched healthy references. Mean (SD) FVC was 2544 (817) mL in the placebo group (n = 286) compared with 3516 (887) mL in the hypothetical matched healthy references. Mean (SE) changes in FVC at week 52, i.e., age-related loss of lung function, in the hypothetical healthy references matched to the nintedanib and placebo groups, respectively, were - 26.3 (0.5) mL and - 25.8 (0.5) mL. The difference in the change in FVC at week 52 between the nintedanib group and the hypothetical healthy references was 26.6 mL (95% CI: 1.2, 52.0; p = 0.04). The difference in the change in FVC at week 52 between the placebo group and the hypothetical healthy references was 77.5 mL (95% CI: 51.4, 103.7; p < 0.0001).

CONCLUSIONS

Subjects with SSc-ILD in the SENSCIS trial had impaired lung function at baseline and experienced further deterioration over 52 weeks. The decline in FVC in the placebo group was four-fold greater than in a hypothetical group of matched healthy references, whereas the decline in FVC in patients who received nintedanib was two-fold greater than in hypothetical healthy references. These data highlight the clinical relevance of the slowing of FVC decline provided by nintedanib. Trial registration Registered 5 November 2015, https://clinicaltrials.gov/ct2/show/NCT02597933 .

The Role of Personal Risk Experience-An Investigation of Health and Terrorism Risk Perception in Germany and Israel

The present study examined the relationship between risk experience and risk perceptions in relation to the target (risk to the self vs. others) and for two different types of risk: acute risks (i.e., terrorist attacks) and cumulative health risks (i.e., alcohol consumption, tobacco consumption, and unhealthy eating) in two countries (Israel and Germany). An online survey (N = 571) was conducted to assess participants' previous personal experience with acute and cumulative risks and their personal and general risk perceptions. The results showed that personal experience with terrorism was related to increased personal and general risk perceptions, while personal experience with cumulative health risks was related to increased personal but not general risk perceptions. It is argued that an increase in risk perception with more risk experience can be explained by the amount of available information about people's personal as well as other people's risk status. The findings emphasize that the experience-risk perception relationship depends on the target of the risk and the type of risk experience.

"Translating" All-Cause Mortality Rate Ratios or Hazard Ratios to Age-, Longevity-, and Probability-Based Measures

Epidemiologists commonly use an adjusted hazard ratio or incidence density ratio, or a standardized mortality ratio, to measure a difference in all-cause mortality rates. They seldom translate it into an age-, time-, or probability-based measure that would be easier to communicate and to relate to. Several articles have shown how to translate from a standardized mortality ratio or hazard ratio to a longevity difference, a difference in actuarial ages, or a probability of being outlived. In this paper, we describe the settings where these translations are and are not appropriate and provide some of the heuristics behind the formulae. The tools that yield differences in "effective age" and in longevity are applicable when both 1) the mortality rate ratio (hazard ratio) is constant over age and 2) the rates themselves are log-linear in age. The "probability/odds of being outlived" metric is applicable whenever the first condition holds, and thus it provides no direct information on the magnitude of the effective age/longevity difference.

Interventions Using Heart Age for Cardiovascular Disease Risk Communication: Systematic Review of Psychological, Behavioral, and Clinical Effects

BACKGROUND

Cardiovascular disease (CVD) risk communication is a challenge for clinical practice, where physicians find it difficult to explain the absolute risk of a CVD event to patients with varying health literacy. Converting the probability to heart age is increasingly used to promote lifestyle change, but a rapid review of biological age interventions found no clear evidence that they motivate behavior change.

OBJECTIVE

In this review, we aim to identify the content and effects of heart age interventions.

METHODS

We conducted a systematic review of studies presenting heart age interventions to adults for CVD risk communication in April 2020 (later updated in March 2021). The Johanna Briggs risk of bias assessment tool was applied to randomized studies. Behavior change techniques described in the intervention methods were coded.

RESULTS

From a total of 7926 results, 16 eligible studies were identified; these included 5 randomized web-based experiments, 5 randomized clinical trials, 2 mixed methods studies with quantitative outcomes, and 4 studies with qualitative analysis. Direct comparisons between heart age and absolute risk in the 5 web-based experiments, comprising 5514 consumers, found that heart age increased positive or negative emotional responses (4/5 studies), increased risk perception (4/5 studies; but not necessarily more accurate) and recall (4/4 studies), reduced credibility (2/3 studies), and generally had no effect on lifestyle intentions (4/5 studies). One study compared heart age and absolute risk to fitness age and found reduced lifestyle intentions for fitness age. Heart age combined with additional strategies (eg, in-person or phone counseling) in applied settings for 9582 patients improved risk control (eg, reduced cholesterol levels and absolute risk) compared with usual care in most trials (4/5 studies) up to 1 year. However, clinical outcomes were no different when directly compared with absolute risk (1/1 study). Mixed methods studies identified consultation time and content as important outcomes in actual consultations using heart age tools. There were differences between people receiving an older heart age result and those receiving a younger or equal to current heart age result. The heart age interventions included a wide range of behavior change techniques, and conclusions were sometimes biased in favor of heart age with insufficient supporting evidence. The risk of bias assessment indicated issues with all randomized clinical trials.

CONCLUSIONS

The findings of this review provide little evidence that heart age motivates lifestyle behavior change more than absolute risk, but either format can improve clinical outcomes when combined with other behavior change strategies. The label for the heart age concept can affect outcomes and should be pretested with the intended audience. Future research should consider consultation time and differentiate between results of older and younger heart age.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

NPRR2-10.1101/2020.05.03.20089938.

Interventions Using Heart Age for Cardiovascular Disease Risk Communication: Systematic Review of Psychological, Behavioral, and Clinical Effects

BACKGROUND

Cardiovascular disease (CVD) risk communication is a challenge for clinical practice, where physicians find it difficult to explain the absolute risk of a CVD event to patients with varying health literacy. Converting the probability to heart age is increasingly used to promote lifestyle change, but a rapid review of biological age interventions found no clear evidence that they motivate behavior change.

OBJECTIVE

In this review, we aim to identify the content and effects of heart age interventions.

METHODS

We conducted a systematic review of studies presenting heart age interventions to adults for CVD risk communication in April 2020 (later updated in March 2021). The Johanna Briggs risk of bias assessment tool was applied to randomized studies. Behavior change techniques described in the intervention methods were coded.

RESULTS

From a total of 7926 results, 16 eligible studies were identified; these included 5 randomized web-based experiments, 5 randomized clinical trials, 2 mixed methods studies with quantitative outcomes, and 4 studies with qualitative analysis. Direct comparisons between heart age and absolute risk in the 5 web-based experiments, comprising 5514 consumers, found that heart age increased positive or negative emotional responses (4/5 studies), increased risk perception (4/5 studies; but not necessarily more accurate) and recall (4/4 studies), reduced credibility (2/3 studies), and generally had no effect on lifestyle intentions (4/5 studies). One study compared heart age and absolute risk to fitness age and found reduced lifestyle intentions for fitness age. Heart age combined with additional strategies (eg, in-person or phone counseling) in applied settings for 9582 patients improved risk control (eg, reduced cholesterol levels and absolute risk) compared with usual care in most trials (4/5 studies) up to 1 year. However, clinical outcomes were no different when directly compared with absolute risk (1/1 study). Mixed methods studies identified consultation time and content as important outcomes in actual consultations using heart age tools. There were differences between people receiving an older heart age result and those receiving a younger or equal to current heart age result. The heart age interventions included a wide range of behavior change techniques, and conclusions were sometimes biased in favor of heart age with insufficient supporting evidence. The risk of bias assessment indicated issues with all randomized clinical trials.

CONCLUSIONS

The findings of this review provide little evidence that heart age motivates lifestyle behavior change more than absolute risk, but either format can improve clinical outcomes when combined with other behavior change strategies. The label for the heart age concept can affect outcomes and should be pretested with the intended audience. Future research should consider consultation time and differentiate between results of older and younger heart age.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

NPRR2-10.1101/2020.05.03.20089938.

Self-Reported Smoking Status 10-Months After a Single Session Intervention Including an Education Conference About Smoking Harms and Announcement of Spirometric Lung-Age

Background

Studies investigating the effects of announcing spirometric lung-age (SLA) on the smokers' self-reported smoking status reported conflicting results.

Main objective

To evaluate the effects of a single session intervention including an education conference about smoking harms and announcement of SLA on the participants' self-reported smoking status.

Methodology

An interventional study was conducted in a cable factory. The intervention included four steps: PowerPoint presentation about raising smoking hazards awareness; general questionnaire; measurement of the anthropometric and spirometric data, and announcement of SLA; and evaluation of the smokers' self-reported smoking status 10 months later (quitted smoking, decreased consumption; stable consumption, increased consumption).

Results

Thirty-six smokers completed the four steps. Ten months after the intervention, 11.1% of smokers quitted smoking, 52.7% decreased their consumption by 7  ±  4 cigarettes/day, 30.5% kept a stable consumption, and 5.5% increased their consumption by 9  ±  6 cigarettes/day.

Conclusion

Providing an education conference combined with announcing SLA motivated 64% of smokers to quit smoking or to reduce their cigarette consumption.

Population modeling with machine learning can enhance measures of mental health

BACKGROUND

Biological aging is revealed by physical measures, e.g., DNA probes or brain scans. In contrast, individual differences in mental function are explained by psychological constructs, e.g., intelligence or neuroticism. These constructs are typically assessed by tailored neuropsychological tests that build on expert judgement and require careful interpretation. Could machine learning on large samples from the general population be used to build proxy measures of these constructs that do not require human intervention?

RESULTS

Here, we built proxy measures by applying machine learning on multimodal MR images and rich sociodemographic information from the largest biomedical cohort to date: the UK Biobank. Objective model comparisons revealed that all proxies captured the target constructs and were as useful, and sometimes more useful, than the original measures for characterizing real-world health behavior (sleep, exercise, tobacco, alcohol consumption). We observed this complementarity of proxy measures and original measures at capturing multiple health-related constructs when modeling from, both, brain signals and sociodemographic data.

CONCLUSION

Population modeling with machine learning can derive measures of mental health from heterogeneous inputs including brain signals and questionnaire data. This may complement or even substitute for psychometric assessments in clinical populations.

The association between mechanical ventilator compatible bed occupancy and mortality risk in intensive care patients with COVID-19: a national retrospective cohort study

BACKGROUND

The literature paints a complex picture of the association between mortality risk and ICU strain. In this study, we sought to determine if there is an association between mortality risk in intensive care units (ICU) and occupancy of beds compatible with mechanical ventilation, as a proxy for strain.

METHODS

A national retrospective observational cohort study of 89 English hospital trusts (i.e. groups of hospitals functioning as single operational units). Seven thousand one hundred thirty-three adults admitted to an ICU in England between 2 April and 1 December, 2020 (inclusive), with presumed or confirmed COVID-19, for whom data was submitted to the national surveillance programme and met study inclusion criteria. A Bayesian hierarchical approach was used to model the association between hospital trust level (mechanical ventilation compatible), bed occupancy, and in-hospital all-cause mortality. Results were adjusted for unit characteristics (pre-pandemic size), individual patient-level demographic characteristics (age, sex, ethnicity, deprivation index, time-to-ICU admission), and recorded chronic comorbidities (obesity, diabetes, respiratory disease, liver disease, heart disease, hypertension, immunosuppression, neurological disease, renal disease).

RESULTS

One hundred thirty-five thousand six hundred patient days were observed, with a mortality rate of 19.4 per 1000 patient days. Adjusting for patient-level factors, mortality was higher for admissions during periods of high occupancy (> 85% occupancy versus the baseline of 45 to 85%) [OR 1.23 (95% posterior credible interval (PCI): 1.08 to 1.39)]. In contrast, mortality was decreased for admissions during periods of low occupancy (< 45% relative to the baseline) [OR 0.83 (95% PCI 0.75 to 0.94)].

CONCLUSION

Increasing occupancy of beds compatible with mechanical ventilation, a proxy for operational strain, is associated with a higher mortality risk for individuals admitted to ICU. Further research is required to establish if this is a causal relationship or whether it reflects strain on other operational factors such as staff. If causal, the result highlights the importance of strategies to keep ICU occupancy low to mitigate the impact of this type of resource saturation.

A Biomarker-based Biological Age in UK Biobank: Composition and Prediction of Mortality and Hospital Admissions

Background

Chronological age is the strongest risk factor for most chronic diseases. Developing a biomarker-based age and understanding its most important contributing biomarkers may shed light on the effects of age on later-life health and inform opportunities for disease prevention.

Methods

A subpopulation of 141 254 individuals healthy at baseline were studied, from among 480 019 UK Biobank participants aged 40–70 recruited in 2006–2010, and followed up for 6–12 years via linked death and secondary care records. Principal components of 72 biomarkers measured at baseline were characterized and used to construct sex-specific composite biomarker ages using the Klemera Doubal method, which derived a weighted sum of biomarker principal components based on their linear associations with chronological age. Biomarker importance in the biomarker ages was assessed by the proportion of the variation in the biomarker ages that each explained. The proportions of the overall biomarker and chronological age effects on mortality and age-related hospital admissions explained by the biomarker ages were compared using likelihoods in Cox proportional hazard models.

Results

Reduced lung function, kidney function, reaction time, insulin-like growth factor 1, hand grip strength, and higher blood pressure were key contributors to the derived biomarker age in both men and women. The biomarker ages accounted for >65% and >84% of the apparent effect of age on mortality and hospital admissions for the healthy and whole populations, respectively, and significantly improved prediction of mortality (p < .001) and hospital admissions (p < 1 × 10⁻¹⁰) over chronological age alone.

Conclusions

This study suggests that a broader, multisystem approach to research and prevention of diseases of aging warrants consideration.

Effect of periodontal treatment on preclinical Alzheimer's disease-Results of a trial emulation approach

INTRODUCTION

We investigated the relationship between periodontal treatment and pre-clinical Alzheimer's disease (AD).

METHODS

In this quasi-experimental design, 177 periodontally treated patients from the "Greifswald Approach to Individualized Medicine" cohort, which used the same protocols as the population-based Study of Health in Pomerania TREND (SHIP-TREND), and 409 untreated subjects from SHIP-TREND were analyzed. Subjects were younger than 60 years at the magnetic resonance imaging examination, with a median observation period of 7.3 years. Imaging markers for brain atrophy in late-onset AD and brain aging were used as the outcomes.

RESULTS

Robust to sensitivity analyses, periodontal treatment had a favorable effect on AD-related brain atrophy (-0.41; 95% confidence interval: -0.70 to -0.12; P = .0051), which corresponds to a shift from the 50th to the 37th percentile of the outcome distribution. For brain aging, the treatment effect was uncertain.

CONCLUSION

Periodontitis is related to pre-clinical AD in our population.

Current Best Practice for Presenting Probabilities in Patient Decision Aids: Fundamental Principles

BACKGROUND

Shared decision making requires evidence to be conveyed to the patient in a way they can easily understand and compare. Patient decision aids facilitate this process. This article reviews the current evidence for how to present numerical probabilities within patient decision aids.

METHODS

Following the 2013 review method, we assembled a group of 9 international experts on risk communication across Australia, Germany, the Netherlands, the United Kingdom, and the United States. We expanded the topics covered in the first review to reflect emerging areas of research. Groups of 2 to 3 authors reviewed the relevant literature based on their expertise and wrote each section before review by the full authorship team.

RESULTS

Of 10 topics identified, we present 5 fundamental issues in this article. Although some topics resulted in clear guidance (presenting the chance an event will occur, addressing numerical skills), other topics (context/evaluative labels, conveying uncertainty, risk over time) continue to have evolving knowledge bases. We recommend presenting numbers over a set time period with a clear denominator, using consistent formats between outcomes and interventions to enable unbiased comparisons, and interpreting the numbers for the reader to meet the needs of varying numeracy.

DISCUSSION

Understanding how different numerical formats can bias risk perception will help decision aid developers communicate risks in a balanced, comprehensible manner and avoid accidental "nudging" toward a particular option. Decisions between probability formats need to consider the available evidence and user skills. The review may be useful for other areas of science communication in which unbiased presentation of probabilities is important.

Epidemiological transition to mortality and refracture following an initial fracture

This study sought to redefine the concept of fracture risk that includes refracture and mortality, and to transform the risk into "skeletal age". We analysed data obtained from 3521 women and men aged 60 years and older, whose fracture incidence, mortality, and bone mineral density (BMD) have been monitored since 1989. During the 20-year follow-up period, among 632 women and 184 men with a first incident fracture, the risk of sustaining a second fracture was higher in women (36%) than in men (22%), but mortality risk was higher in men (41%) than in women (25%). The increased risk of mortality was not only present with an initial fracture, but was accelerated with refractures. Key predictors of post-fracture mortality were male gender (hazard ratio [HR] 2.4; 95% CI, 1.79–3.21), advancing age (HR 1.67; 1.53–1.83), and lower femoral neck BMD (HR 1.16; 1.01–1.33). A 70-year-old man with a fracture is predicted to have a skeletal age of 75. These results were incorporated into a prediction model to aid patient-doctor discussion about fracture vulnerability and treatment decisions.

Susceptibility to Coronavirus (COVID-19) in Occupational Settings: The Complex Interplay between Individual and Workplace Factors

In the current coronavirus (COVID-19) pandemic, the definition of risk factors for susceptibility to adverse outcomes seems essential to support public and occupational health policies. Some specific issues need to be addressed to understand vulnerability in occupational settings. Among these, individual factors, e.g., age, sex, and preexisting comorbidities (hypertension, cardiovascular diseases, diabetes, obesity, cancer), that can predispose individuals to more severe outcomes and post-COVID-19 symptoms that may represent conditions of acquired susceptibility, possibly impacting the return to-and fitness for-work. Additionally, the risk of contracting COVID-19 through work should be addressed, considering the probability of being in contact with infected people, physical proximity to others, and social aggregation during work. Occupational health settings may represent appropriate scenarios for the early identification of vulnerable subjects, with the final aim to guide risk assessment and management procedures. These should include the systematic surveillance of work-related risk factors, collective preventive policies, stringent actions for specific groups of workers, decisions on occupational placement of employees, and health promotion activities. Concerted actions of general practitioners, hospital specialists, occupational physicians, and all the stakeholders involved in the occupational health and safety management should be focused on planning suitable preventive measures for susceptible subjects.

Association of sleep duration with all-cause and disease-specific mortality in US adults

BACKGROUND

Previous studies revealed inconsistent findings regarding the association between sleep duration and all-cause and disease-specific mortality. This study aimed to clarify the association of sleep duration with mortality using a large population-based prospective cohort study from the USA.

METHODS

We used data from the National Health Interview Survey (2004-2014) linked to National Death Index records to 31 December 2015. A total of 284 754 participants aged ≥18 years were included. Self-reported sleep duration (average time slept in a 24-hour period) was categorised into seven groups: ≤4 hours, 5 hours, 6 hours, 7 hours (reference), 8 hours, 9 hours and ≥10 hours. Study outcomes included all-cause, cardiovascular disease-specific and cancer-specific mortality. Cox proportional hazards models were used to examine the association between sleep duration and mortality.

RESULTS

During a median follow-up of 5.25 years, we identified 20 872 deaths, of which 4 129 were cardiovascular disease-related and 5 217 were cancer-related. Compared with 7 hours/day of sleep, both short and long sleep durations were associated with an increased risk of all-cause mortality (≤4 hours: HR=1.46, 95% CI=1.33-1.61; 5 hours: HR=1.22, 95% CI=1.13-1.32; 6 hours: HR=1.10, 95% CI=1.05-1.17; 8 hours: HR=1.22, 95% CI=1.17-1.28; 9 hours: HR=1.41, 95% CI=1.31-1.51; ≥10 hours: HR=2.00, 95% CI=1.88-2.13). Similar results were observed for cardiovascular disease-specific and cancer-specific mortality.

CONCLUSIONS

Our study indicates that both short (≤6 hours/day) and long (≥8 hours/day) sleep durations increase the risk of mortality compared with sleep of 7 hours/day. A normal sleep duration (about 7 hours) every day is recommended for health benefits.

Risk to health from COVID-19 for anaesthetists and intensivists - a narrative review

Healthcare workers are at an increased risk of infection, harm and death from COVID‐19. Close and prolonged exposure to individuals infectious with SARS‐CoV‐2 leads to infection. A person’s individual characteristics (age, sex, ethnicity and comorbidities) then influence the subsequent risk of COVID‐19 leading to hospitalisation, critical care admission or death. While relative risk is often reported as a measure of individual danger, absolute risk is more important and dynamic, particularly in the healthcare setting. Individual risk interacts with exposure and environmental risk‐factors, and the extent of mitigation to determine overall risk. Hospitals are a unique environment in which there is a significantly increased risk of infection for all healthcare workers. Anaesthetists and intensivists particularly are at high risk of exposure to SARS‐CoV‐2 infected patients due to their working environments and exposure to certain patient groups. However, the available evidence suggests that the risk for this group of individuals is not currently increased. This review examines factors associated with increased risk of infection with SARS‐CoV‐2, increasing severity of COVID‐19 and death. A risk tool is proposed that includes personal, environmental and mitigating factors, and enables an individualised dynamic ‘point‐of‐time’ risk assessment.

How effective are 'age' tools at changing patient behaviour? A rapid review

BACKGROUND

A common form of risk communication is to relay the relative risk (%) of an adverse outcome based on surrogate markers associated with the outcome. A novel way of communicating risk is through 'effective age' of a person or specific organ. These tools can be used to change patient behaviour.

OBJECTIVE

To determine the effect of 'effective age' tools on patient behaviour as compared with more traditional methods of risk communication.

STUDY SELECTION

We performed a search of the PubMed database up to February 2019 for systematic reviews and randomised controlled trials (RCT) that answered our question. Interventions were 'effective age' tools, comparators were usual care or alternative risk communication tools. Primary outcomes were behavioural change measures.

FINDINGS

We included 1 overview of systematic reviews (level 1 evidence), 2 systematic reviews (level 1 evidence) and 13 RCTs (level 2 evidence). Both systematic reviews concluded the evidence base was not conclusive enough to make specific recommendations.Age tools assessed in the 13 RCTs were: 'lung age' (n=5), 'heart age' (n=3), 'health age' (n=2), 'cardiovascular age' (n=1), 'body age' (n=1) and 'net present value' (n=1). 7/13 (54%) RCTs demonstrated a clinical effect on behaviour change favouring the 'age' tool; 2/13 (15%) demonstrated a null effect; 4/13 (31%) favoured control.

CONCLUSIONS

Our findings indicate that systematic review evidence needs updating. The evidence from RCTs on the effect of using age metrics on patient behaviour is poor. There is a need for high-quality trials to decrease uncertainty in the available evidence.

Coronary vascular age: An alternate means for predicting stress-induced myocardial ischemia in patients with suspected coronary artery disease

BACKGROUND

Coronary artery calcium (CAC) can be used to estimate vascular age in adults, providing a convenient transformation of CAC from Agatston units into a year's scale. We investigated the role of coronary vascular age in predicting stress-induced myocardial ischemia in subjects with suspected coronary artery disease (CAD).

METHODS

A total of 717 subjects referred to CAC scoring and 82Rb PET/CT stress-rest myocardial perfusion imaging for suspected CAD were studied. CAC score was measured according to the Agatston method and coronary vascular age by equating estimated CAD risk for chronological age and CAC using the formula 39.1 + 7.25 × ln(CAC + 1).

RESULTS

Stress-induced ischemia was present in 105 (15%) patients. Mean chronological age, CAC score, and coronary vascular age were higher (all P < .001) in patients with ischemia compared to those without. At incremental analysis, the global Chi square increased from 41.26 to 68.77 (P < .001) when chronological age was added to clinical variables. Including vascular age in the model, the global Chi square further increased from 68.77 to 106.38 (P < .001). Adding chronological age to clinical data, continuous net reclassification improvement (cNRI) was 0.57, while adding vascular age to clinical data and chronological age cNRI was 0.62. At decision curve analysis, the model including vascular age was associated with the highest net benefit compared to the model including only clinical data, to the model including chronological age and clinical data, and to a strategy considering that all patients had ischemia. The model including vascular age also showed the largest reduction in false-positive rate without missing any ischemic patients.

CONCLUSIONS

In subjects with suspected CAD, coronary vascular age is strongly associated with stress-induced ischemia. The communication of a given vascular age would have a superior emotive impact improving observance of therapies and healthier lifestyles.

Heart Age, Cardiovascular Disease Risk, and Disparities by Sex and Race/Ethnicity Among New York City Adults

OBJECTIVES

Cardiovascular disease (CVD) is the leading cause of mortality in the United States. The risk for developing CVD is usually calculated and communicated to patients as a percentage. The calculation of heart age-defined as the predicted age of a person's vascular system based on the person's CVD risk factor profile-is an alternative method for expressing CVD risk. We estimated heart age among adults aged 30-74 in New York City and examined disparities in excess heart age by race/ethnicity and sex.

METHODS

We applied data from the 2011, 2013, and 2015 New York State Behavioral Risk Factor Surveillance System to the non-laboratory-based Framingham risk score functions to calculate 10-year CVD risk and heart age by sex, race/ethnicity, and selected sociodemographic groups and risk factors.

RESULTS

Of 6117 men and women in the study sample, the average heart age was 5.7 years higher than the chronological age, and 2631 (43%) adults had a predicted heart age ≥5 years older than their chronological age. Mean excess heart age increased with age (from 0.7 year among adults aged 30-39 to 11.2 years among adults aged 60-74) and body mass index (from 1.1 year among adults with normal weight to 11.8 years among adults with obesity). Non-Latino white women had the lowest mean excess heart age (2.3 years), and non-Latino black men and women had the highest excess heart age (8.4 years).

CONCLUSIONS

Racial/ethnic and sex disparities in CVD risk persist among adults in New York City. Use of heart age at the population level can support public awareness and inform targeted programs and interventions for population subgroups most at risk for CVD.

The effects of infographics and several quantitative versus qualitative formats for cardiovascular disease risk, including heart age, on people's risk understanding

OBJECTIVE

To study how comprehension of cardiovascular disease (CVD) risk is influenced by: (1) infographics about qualitative risk information, with/without risk numbers; (2) which qualitative risk dimension is emphasized; (3) heart age vs. traditional risk format.

METHODS

For aim 1, a 2 (infographics versus text) x 2 (risk number versus no risk number) between-subjects design was used. For aim 2, three pieces of information were tested within-subjects. Aim 3 used a simple comparison group. Participants (45-65 yrs old) were recruited through an online access panel; low educated people were oversampled. They received hypothetical risk information (20%/61yrs). Primary outcomes: recall, risk appraisals, subjective/objective risk comprehension.

SECONDARY OUTCOMES

behavioral intentions, information evaluations.

RESULTS

Infographics of qualitative risk dimensions negatively affected recall, subjective risk comprehension and information evaluations. No effect of type of risk dimension was found on risk perception. Heart age influenced recall, comprehension, evaluations and affective risk appraisals.

CONCLUSION

Infographics of hypothetical CVD risk information had detrimental effects on measures related to risk perception/comprehension, but effects were mainly seen in undereducated participants. Heart age influenced perceptions/comprehension of hypothetical risk in a way that seemed to support understanding.

PRACTICE IMPLICATIONS

Heart age seems a fruitful risk communication approach in disease risk calculators.

Nudging people with Type 2 diabetes towards better self-management through personalized risk communication: A pilot randomized controlled trial in primary care

OBJECTIVES

To assess the feasibility in routine primary care consultation and investigate the effect on risk recall and self-management of a new type of risk communication intervention based on behavioural economics ("nudge-based") for people with Type 2 diabetes mellitus (T2DM).

METHODS

Forty adults with poorly controlled T2DM (HbA1c > 7.5%) were randomized to receive a personalized, nudge-based risk communication intervention (n = 20) or standard care (n = 20). Risk recall and self-management were evaluated at baseline and 12 weeks after the intervention.

RESULTS

Both in terms of feasibility and acceptability, this new risk communication intervention was very satisfactory. Study retention rate after 12 weeks was very high (90%) and participants were highly satisfied with the intervention (4.4 out of 5 on the COMRADE scale). Although not powered to identify significant between-group effects, the intervention significantly improved risk recall after 12 weeks and intentions to make lifestyle changes (dietary behaviour) compared to standard care.

CONCLUSIONS

This pilot study provides the first evidence of the feasibility of implementing in primary care a nudge-based risk communication intervention for people with T2DM. Based on the promising results observed, an adequately powered trial to determine the effectiveness of the intervention on long-term self-management is judged feasible. As a result of this feasibility study, some minor adaptations to the intervention and study methods that would help to facilitate a definitive trial are also reported.

Loss of life expectancy derived from a standardized mortality ratio in Denmark, Finland, Norway and Sweden

Aims: The standardized mortality ratio (SMR) is a widely used measure. A recent methodological study provided an accurate approximate relationship between an SMR and difference in lifetime expectancies. This study examines the usefulness of the theoretical relationship, when comparing historic mortality data in four Scandinavian populations. Methods: For Denmark, Finland, Norway and Sweden, data on mortality every fifth year in the period 1950 to 2010 were obtained. Using 1980 as the reference year, SMRs and difference in life expectancy were calculated. The assumptions behind the theoretical relationship were examined graphically. The theoretical relationship predicts a linear association with a slope, [Formula: see text], between log(SMR) and difference in life expectancies, and the theoretical prediction and calculated differences in lifetime expectancies were compared. We examined the linear association both for life expectancy at birth and at age 30. All analyses were done for females, males and the total population. Results: The approximate relationship provided accurate predictions of actual differences in lifetime expectancies. The accuracy of the predictions was better when age was restricted to above 30, and improved if the changes in mortality rate were close to a proportional change. Slopes of the linear relationship were generally around 9 for females and 10 for males. Conclusions: The theoretically derived relationship between SMR and difference in life expectancies provides an accurate prediction for comparing populations with approximately proportional differences in mortality, and was relatively robust. The relationship may provide a useful prediction of differences in lifetime expectancies, which can be more readily communicated and understood.