Abstract P011: Predictive Ability of 35 Frailty Scores for Cardiovascular Events in the General Population

Circulation ◽  
2017 ◽  
Vol 135 (suppl_1) ◽  
Author(s):  
Gloria A Aguayo ◽  
Anna Schritz ◽  
Anne-Françoise Donneau ◽  
Michel T Vaillant ◽  
Saverio Stranges ◽  
...  
Keyword(s):  
Proportional Hazards Model ◽  
Assessment Tool ◽  
Frailty Index ◽  
Predictive Ability ◽  
Continuous Variable ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Discriminative Ability ◽  
Basic Model ◽  
C Statistic

Introduction: Frailty is a state of vulnerability in elderly people linked to higher mortality risk. Cardiovascular disease (CVD) is highly prevalent in aged populations and associated with frailty. Thus, frailty state could predict higher risk of CVD. Many frailty scores (FS) have been developed, but none of them is considered the gold standard. We aimed to compare predictive and discriminative ability of an extensive list of FS with regard to incidence of CVD in a sample of the general elderly population in England. We assessed the hypothesis that some FS will have better predictive ability than others, depending on their characteristics. Methods: We performed a prospective analysis of the association between 35 FS in participants free of CVD at baseline wave 2 of the English Longitudinal Study of Ageing (2004-2005), and incident CVD assessed until February 2012. The sample consisted of 4,177 participants (43.0 % men). Hazard ratios (HR) and 95% confidence intervals (95% CI) were calculated for each FS using Cox proportional hazards model, adjusted for demographic, lifestyle and comorbidity variables. FS were analyzed on a continuous scale and using original cutoffs. The added predictive ability of FS beyond a basic model consisting of sex and age was studied using Harrel’s C statistic (the higher the better). Results: The median follow-up was 5.8 years, the incidence rate of CVD events was 301.2 /10,000 person-years and CVD represented 28% of the total cause of death. The mean age was 70.5 (SD: ±7.8) years. In fully-adjusted models with demographics, lifestyles and comorbidity, HRs ranged from: 1.0 (0.7; 1.6) to 12.7 (5.5; 29.3). Using cutoffs, HRs ranged from 0.7 (0.2; 1.9) to 1.8 (1.3; 2.5). Adjusted for sex and age, delta Harrel’s C statistic ranged from -0.8 (-3.4; 1.8) to 3.0 (-0.4; 6.4). The best CVD predictive ability was found for the Frailty Index with 70 variables and the Comprehensive Geriatric Assessment screening FS for continuous and cutoff analyses respectively. In conclusion, there is high variability in the association between different published FS and incident CVD. FS have better predictive ability used as continuous variable. Although most of the analyzed FS have good predictive ability with regard to incident CVD, they do not significantly improve on the discriminative capacity of a basic model. Our results will help to guide clinicians, researchers and public health practitioners in choosing the most informative frailty assessment tool.

scholarly journals Association of FIB-4 index and clinical outcomes in critically ill patients with acute kidney injury: a cohort study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yi-Yi Shi ◽  
Rui Zheng ◽  
Jie-Jie Cai ◽  
Zheng-Dong Fang ◽  
Wen-Jing Chen ◽  
...  
Keyword(s):  
Acute Kidney Injury ◽  
Clinical Outcomes ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Proportional Hazards Model ◽  
Kidney Injury ◽  
Continuous Variable ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
The Relationship

Abstract Background The relationship between fibrosis-4 (FIB-4) index and clinical outcomes in patients with acute kidney injury (AKI) is unclear. We aimed to investigate the association between FIB-4 index and all-cause mortality in critically ill patients with AKI. Methods We used data from the Multiparameter Intelligent Monitoring in Intensive Care III (MIMIC-III) database (v1.4). The FIB-4 score was calculated using the existing formulas. logistic regression model, and Cox proportional hazards model were used to assessed the relationship between the FIB-4 index and in-hospital,28-day and 90-day mortality, respectively. Results A total of 3592 patients with AKI included in the data analysis. 395 (10.99%) patients died during hospitalization and 458 (12.74%) patients died in 28-day. During the 90-day follow-up, 893 (22.54%) patients were dead. An elevated FIB-4 value was significantly associated with increased in-hospital mortality when used as a continuous variable (odds ratio [OR] 1.183, 95% confidence interval [CI] 1.072–1.305, P = 0.002) and as a quartile variable (OR of Q2 to Q4 1.216–1.744, with Q1 as reference). FIB-4 was positively associated with 28-day mortality of AKI patients with hazard ratio (HR) of 1.097 (95% CI 1.008, 1.194) and 1.098 (95% 1.032, 1.167) for 90-day mortality, respectively. Conclusion This study demonstrated the FIB-4 index is associated with clinical outcomes in critically ill patients with acute kidney injury.

scholarly journals Time Scales in Epidemiological Analysis: An Empirical Comparison

2016 ◽  
Vol 5 (3) ◽  
pp. 91 ◽  
Author(s):  
Prabhakar Chalise ◽  
Eric Chicken ◽  
Daniel McGee
Keyword(s):  
Time Scales ◽  
Time Scale ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Predictive Ability ◽  
Observation Time ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Empirical Examination ◽  
Hazards Model

The Cox proportional hazards model is routinely used to analyze time-to-event data. This model requires the definition of a unique well-defined time scale. Most often, observation time is used as the time scale for both clinical and observational studies. Recently after a suggestion that it may be a more appropriate scale, chronological age has begun to appear as the time scale used in some reports. There appears to be no general consensus about which time scale is appropriate for any given analysis. It has been suggested that if the baseline hazard is exponential or if the age-at-entry is independent of covariates used in the model, then the two time scales provide similar results. In this report we provide an empirical examination of the results using the two different time scales using a large collection of data sets to examine the relationship between systolic blood pressure and coronary heart disease death. We demonstrate, in this empirical example that the two time-scales can sometimes lead to differing regression coefficient estimates but time-on-study model has better predictive ability in general.

scholarly journals Bleeding during critical illness: A prospective cohort study using a new measurement tool

2007 ◽  
Vol 30 (2) ◽  
pp. 93 ◽  
Author(s):  
Donald M. Arnold ◽  
Laura Donahoe ◽  
France J. Clarke ◽  
Andrea J. Tkaczyk ◽  
Diane Heels-Ansdell ◽  
...  
Keyword(s):  
Risk Factors ◽  
Critical Illness ◽  
Major Bleeding ◽  
Proportional Hazards Model ◽  
Assessment Tool ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Surgical Intensive Care Unit ◽  
Measurement Tool ◽  
Surgical Intensive Care

Purpose: To estimate the incidence, severity, duration and consequences of bleeding during critical illness, and to test the performance characteristics of a new bleeding assessment tool. Methods: Clinical bleeding assessments were performed prospectively on 100 consecutive patients admitted to a medical-surgical intensive care unit (ICU) using a novel bleeding measurement tool called HEmorrhage MEasurement (HEME). Bleeding assessments were done daily in duplicate and independently by blinded, trained assessors. Inter-rater agreement and construct validity of the HEME tool were calculated using φ. Risk factors for major bleeding were identified using a multivariable Cox proportional hazards model. Results: Overall, 90% of patients experienced a total of 480 bleeds of which 94.8% were minor and 5.2% were major. Inter-rater reliability of the HEME tool was excellent (φ = 0.98, 95% CI: 0.96 to 0.99). A decrease in platelet count and a prolongation of partial thromboplastin time were independent risk factors for major bleeding but neither were renal failure nor prophylactic anticoagulation. Patients with major bleeding received more blood transfusions and had longer ICU stays compared to patients with minor or no bleeding. Conclusions: Bleeding, although primarily minor, occurred in the majority of ICU patients. One of five patients experienced a major bleed which was associated with abnormal coagulation tests but not with prophylactic anticoagulants. These baseline bleeding rates can inform the design of future clinical trials in critical care that use bleeding as an outcome and HEME is a useful tool to measure bleeding in critically ill patients.

scholarly journals A Prediction Model for Prediabetes Risk in Middle-Aged and Elderly Populations: A Prospective Cohort Study in China

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Jiahua Wu ◽  
Jiaqiang Zhou ◽  
Xueyao Yin ◽  
Yixin Chen ◽  
Xihua Lin ◽  
...  
Keyword(s):  
Prediction Model ◽  
Plasma Glucose ◽  
Proportional Hazards Model ◽  
Predictive Accuracy ◽  
Serum Insulin ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Discriminative Ability ◽  
Predictive Values ◽  
Family History Of Diabetes

Background. To investigate indicators for prediabetes risk and construct a prediction model for prediabetes incidences in China. Methods. In this study, 551 adults aged 40–70 years had normal glucose tolerance (NGT) and normal hemoglobin A1c (HbA1c) levels at baseline. Baseline data including demographic information, anthropometric measurements, and metabolic profile measurements were collected. The associations between possible indicators and prediabetes were assessed by the Cox proportional-hazards model. The predictive values were evaluated by the area under the receiver operating characteristic (ROC) curve (AUC). Results. During an average of 3.35 years of follow-up, the incidence of prediabetes was found to be 19.96% (n = 110). In the univariate analyses, fasting plasma glucose (FPG), fasting serum insulin (FINS), 2 h plasma glucose (2hPG), HbA1c, serum uric acid (SUA), waist circumference (WC), smoking, and family history of diabetes (FHD) were found to be significantly correlated with prediabetes. In the multivariable analyses, WC (hazard ratio (HR): 1.032; 95% confidence interval (CI): 1.010, 1.053; p = 0.003 ), FHD (HR: 1.824; 95% CI: 1.250, 2.661; p = 0.002 ), HbA1c (HR: 1.825; 95% CI: 1.227, 2.714; p = 0.003 ), and FPG (HR: 2.284; 95% CI: 1.556, 3.352; p < 0.001 ) were found to be independent risk factors for prediabetes. A model that encompassed WC, FHD, HbA1c, and FPG for predicting prediabetes exhibited the largest discriminative ability (AUC: 0.702). Conclusions. WC, FHD, HbA1c, and FPG are independently correlated with the risk of prediabetes. Furthermore, the combination of these predictors enhances the predictive accuracy of prediabetes.

scholarly journals Prevalence of frailty in patients referred to the kidney transplant waitlist

Kidney360 ◽  
2021 ◽  
pp. 10.34067/KID.0001892021
Author(s):  
George Worthen ◽  
Amanda Vinson ◽  
Héloise Cardinal ◽  
Steve Doucette ◽  
Nessa Gogan ◽  
...  
Keyword(s):  
Kidney Transplant ◽  
Proportional Hazards Model ◽  
Frailty Index ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Assessment Tools ◽  
Measurement Tool ◽  
Clinical Frailty Scale ◽  
Frailty Phenotype ◽  
The Mean

Background: Comparisons between frailty assessment tools for waitlist candidates are a recognized priority area for kidney transplantation. We compared the prevalence of frailty using three established tools in a cohort of waitlist candidates. Methods: Waitlist candidates were prospectively enrolled from 2016-2020 across five centers. Frailty was measured using the Frailty Phenotype (FP, as well as a 37 variable Frailty Index (FI), and the Clinical Frailty Scale (CFS). The FI and CFS were dichotomized using established cut-offs. Agreement was compared using kappa coefficients. Area under the receiver operator characteristic curves were generated to compare the FI and CFS (treated as continuous measures) to the FP. Unadjusted associations between each frailty measure and time to death or waitlist withdrawal were determined using an unadjusted Cox proportional hazards model. Results: Of 542 enrolled patients, 64% were male, 80% were white, and the mean age was 54+/-14. The prevalence of frailty by the FP was 16%. The mean FI score was 0.23+/-0.14 and the prevalence of frailty was 38% (score of >0.25). The median CFS score was 3 (IQR 2,3), and the prevalence was 15% (score of ≥4). Kappa values comparing the FP to the FI (0.438) and CFS (0.272) showed fair to moderate agreement. Area under the ROC curve for the FP and FI/CFS were 0.86 (good) and 0.69 (poor) respectively. Frailty by the CFS (HR 2.10; 95% CI (1.04, 4.24) and FI (HR 1.79; 95% CI 1.00,3.21) was associated with death or permanent withdrawal. The association between frailty by the FP and death/withdrawal was not statistically significant (HR 1.78; CI 0.786, 3.71). Conclusion: Frailty prevalence varies by measurement tool used, and agreement between these measurements is fair to moderate. This has implications for determining the optimal frailty screening tool for use in those being evaluated for kidney transplant.

Plasma VEGF-a, angiopoietin-2 (ANG2) and soluble(s)TIE2 in patients (pts) with HER2-negative locally recurrent or metastatic breast cancer (LR/MBC) treated with first-line bevacizumab (A) and paclitaxel (T) without or with capecitabine (X).

2013 ◽  
Vol 31 (15_suppl) ◽  
pp. 1072-1072
Author(s):  
Siu W. Lam ◽  
Steffen M. de Groot ◽  
Aafke H. Honkoop ◽  
Nienke M. Nota ◽  
A. Jager ◽  
...  
Keyword(s):  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Objective Response ◽  
Metastatic Breast ◽  
Progression Free Survival ◽  
Response Duration ◽  
Continuous Variable ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
First Line

1072 Background: In the randomized phase II ATX trial, pts with HER2-negative LR/MBC were treated with first-line AT or ATX. We determined the prognostic value for outcome of VEGF-A, ANG2 and sTIE2 measured at baseline on cycle 1 day 1 (C1D1) and after cycle 1 (C2D1). Methods: 312 pts were randomized in 1:1 ratio to AT (T 90 mg/m2 on d1, 8, 15 and A 10 mg/kg on d1, 15 q4w x 6 cycles, followed by A 15 mg/kg on d1 q3w for next cycles) or ATX (T 90 mg/m2 on d1, 8, A 15 mg/kg on d1 and X 825 mg/m2bid on d1–14 q3w x 8 cycles, followed by the same dose of A and X q3w for next cycles). The primary endpoint was progression-free survival (PFS). Secondary endpoints were objective response rate (ORR), response duration (RD), overall survival (OS) and safety. Plasma proteins on C1D1 (N = 173) and on C2D1 (N = 142) were measured by ELISA. The association of protein levels (continuous variable) with PFS and OS was evaluated by Cox proportional hazards model and Martingale residual plot. Results: At a median follow-up of 39 months (mo), there were 292 PFS events and 242 deaths. ATX significantly improved PFS as compared to AT (median 11 vs. 8.4 mo, stratified HR = 0.52; 95% CI, 0.41 – 0.67; P < .001). The confirmed ORR in measurable disease (N = 268) was 67% in ATX vs. 50% in AT. Median RD was 6.4 mo (95% CI, 6.1 – 8.3) in ATX v 5.4 mo (95% CI, 5.1 – 6.0) in AT. Median OS was 24.1 mo in ATX vs. 23.1 mo in AT (P= .44). The aselected ‘biomarker’ cohort (N = 173) and overall trial cohort had similar baseline characteristics. ANG2 on C1D1 moderately correlated with sTIE2 on C1D1 (Pearson’s r = .44, P < .001). High ANG2 on C1D1 was significantly associated with poor OS (HR = 1.6; 95% CI, 1.1 – 2.3; P = .01), but not with poor PFS (HR = 1.3; 95% CI, 1.0 – 1.3; P = .07). ANG2 on C2D1 was not significantly associated with OS (HR = 1.55; 95% CI, 0.99 – 2.4; P = .057) or with PFS (P= .6). sTIE2 and VEGF-A were not associated with outcome. All pts had very low levels of free VEGF-A on C2D1 (median 8 pg/ml). Conclusions: In HER2-negative LR/MBC, ATX is more effective (PFS, ORR and RD) than AT. A very high plasma level of ANG2 at baseline indicates a high risk for poor survival. Clinical trial information: NTR1348.

scholarly journals Does the Anti-Mullerian Hormone Decline Rate Improve the Prediction of Age at Menopause?

2021 ◽  
Vol 12 ◽  
Author(s):  
Fahimeh Ramezani Tehrani ◽  
Ali Sheidaei ◽  
Faezeh Firouzi ◽  
Maryam Tohidi ◽  
Fereidoun Azizi ◽  
...  
Keyword(s):  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Cox Model ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Time Dependent ◽  
Age At Menopause ◽  
C Statistic ◽  
Hazards Model ◽  
Decline Rate

ObjectivesThere are controversial studies investigating whether multiple anti-Mullerian hormone (AMH) measurements can improve the individualized prediction of age at menopause in the general population. This study aimed to reexplore the additive role of the AMH decline rate in single AMH measurement for improving the prediction of age at physiological menopause, based on two common statistical models for analysis of time-to-event data, including time-dependent Cox regression and Cox proportional-hazards regression models.MethodsA total of 901 eligible women, aged 18–50 years, were recruited from the Tehran Lipid and Glucose Study (TLGS) population and followed up every 3 years for 18 years. The serum AMH level was measured at the time of recruitment and twice after recruitment within 6-year intervals using the Gen II AMH assay. The added value of repeated AMH measurements for the prediction of age at menopause was explored using two different statistical approaches. In the first approach, a time-dependent Cox model was plotted, with all three AMH measurements as time-varying predictors and the baseline age and logarithm of annual AMH decline as time-invariant predictors. In the second approach, a Cox proportional-hazards model was fitted to the baseline data, and improvement of the complex model, which included repeated AMH measurements and the logarithm of the AMH annual decline rate, was assessed using the C-statistic.ResultsThe time-dependent Cox model showed that each unit increase in the AMH level could reduce the risk of menopause by 87%. The Cox proportional-hazards model also improved the prediction of age at menopause by 3%, according to the C-statistic. The subgroup analysis for the prediction of early menopause revealed that the risk of early menopause increased by 10.8 with each unit increase in the AMH annual decline rate.ConclusionThis study confirmed that multiple AMH measurements could improve the individual predictions of the risk of at physiological menopause compared to single AMH measurements. Different alternative statistical approaches can also offer the same interpretations if the essential assumptions are met.

scholarly journals A Prediction Model with Lifestyle in Addition to Previously Known Risk Factors Improves Its Predictive Ability for Cardiovascular Death

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Masatoshi Nishimoto ◽  
Miho Tagawa ◽  
Masaru Matsui ◽  
Masahiro Eriguchi ◽  
Ken-ichi Samejima ◽  
...  
Keyword(s):  
Risk Factors ◽  
Prediction Model ◽  
Proportional Hazards Model ◽  
Predictive Ability ◽  
Cardiovascular Death ◽  
Lifestyle Factors ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Derivation Cohort ◽  
Kaplan Meier

Abstract This longitudinal cohort study aimed to create a novel prediction model for cardiovascular death with lifestyle factors. Subjects aged 40–74 years in the Japanese nationwide Specific Health Checkup Database in 2008 were included. Subjects were randomly assigned to the derivation and validation cohorts by a 2:1 ratio. Points for the prediction model were determined using regression coefficients that were derived from the Cox proportional hazards model in the derivation cohort. Models 1 and 2 were developed using known risk factors and known factors with lifestyle factors, respectively. The models were validated by comparing Kaplan-Meier curves between the derivation and validation cohorts, and by calibration plots in the validation cohort. Among 295,297 subjects, data for 120,823 were available. There were 310 cardiovascular deaths during a mean follow-up of 3.6 years. Model 1 included known risk factors. In model 2, weight gain, exercise habit, gait speed, and drinking alcohol were additionally included as protective factors. Kaplan-Meier curves matched better between the derivation and validation cohorts in model 2, and model 2 was better calibrated. In conclusion, our prediction model with lifestyle factors improved the predictive ability for cardiovascular death.

Levels of Systolic and Diastolic Blood Pressure and Their Relation to Incident Metabolic Syndrome

Cardiology ◽  
2019 ◽  
Vol 142 (4) ◽  
pp. 224-231 ◽  
Author(s):  
Ju Young Jung ◽  
Chang-Mo Oh ◽  
Joong-Myung Choi ◽  
Jae-Hong Ryoo ◽  
Pil-Wook Chung ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Metabolic Syndrome ◽  
Subgroup Analysis ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Predictive Ability ◽  
Heart Association ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
The Metabolic Syndrome

Background: Elevated blood pressure (BP) is a component of the metabolic syndrome (MetS), and one third of individuals with hypertension simultaneously have MetS. However, the evidence is still unclear regarding the predictive ability of BP for incident MetS. Methods: In total, 5,809 Koreans without baseline MetS were grouped by baseline systolic (SBP) and diastolic BP (DBP) and monitored for 10 years to identify incident MetS. A Cox proportional hazards model was used to evaluate the HR and 95% CI for MetS according to SBP and DBP. Subgroup analysis was conducted in the normotensive population based on a new guideline of the American College of Cardiology and the American Heart Association. Results: High-BP groups tended to have worse metabolic profiles than the lowest-BP group in both SBP and DBP categories. In all of the participants, elevated SBP and DBP levels were significantly associated with the increased HR for MetS, even after adjusting for covariates. Subgroup analysis for normotensive participants indicated that the HR for MetS increased proportionally to both SBP (<110 mm Hg: reference, 110–119 mm Hg: HR = 1.60 [95% CI 1.40–1.84], and 120–129 mm Hg: HR = 2.12 [95% CI 1.82–2.48]) and DBP levels (<70 mm Hg: reference, 71–74 mm Hg: HR = 1.31 [95% CI 1.09–1.58], and 75–79 mm Hg: HR = 1.51 [95% CI 1.25–1.81]). Conclusion: The risk of incident MetS increased proportionally to baseline SBP and DBP, and this was identically observed even in normotensive participants.

Age at Exposure to Parental Suicide and the Subsequent Risk of Suicide in Young People

Crisis ◽  
2018 ◽  
Vol 39 (1) ◽  
pp. 27-36 ◽  
Author(s):  
Kuan-Ying Lee ◽  
Chung-Yi Li ◽  
Kun-Chia Chang ◽  
Tsung-Hsueh Lu ◽  
Ying-Yeh Chen
Keyword(s):  
Young People ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Birth Registry ◽  
Data Set ◽  
Parental Suicide ◽  
The Impact ◽  
Age At Exposure

Abstract. Background: We investigated the age at exposure to parental suicide and the risk of subsequent suicide completion in young people. The impact of parental and offspring sex was also examined. Method: Using a cohort study design, we linked Taiwan's Birth Registry (1978–1997) with Taiwan's Death Registry (1985–2009) and identified 40,249 children who had experienced maternal suicide (n = 14,431), paternal suicide (n = 26,887), or the suicide of both parents (n = 281). Each exposed child was matched to 10 children of the same sex and birth year whose parents were still alive. This yielded a total of 398,081 children for our non-exposed cohort. A Cox proportional hazards model was used to compare the suicide risk of the exposed and non-exposed groups. Results: Compared with the non-exposed group, offspring who were exposed to parental suicide were 3.91 times (95% confidence interval [CI] = 3.10–4.92 more likely to die by suicide after adjusting for baseline characteristics. The risk of suicide seemed to be lower in older male offspring (HR = 3.94, 95% CI = 2.57–6.06), but higher in older female offspring (HR = 5.30, 95% CI = 3.05–9.22). Stratified analyses based on parental sex revealed similar patterns as the combined analysis. Limitations: As only register-­based data were used, we were not able to explore the impact of variables not contained in the data set, such as the role of mental illness. Conclusion: Our findings suggest a prominent elevation in the risk of suicide among offspring who lost their parents to suicide. The risk elevation differed according to the sex of the afflicted offspring as well as to their age at exposure.

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