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Development and validation of a predictive mortality risk score from a European hemodialysis cohort.

ABSTRACT: Although mortality risk scores for chronic hemodialysis (HD) patients should have an important role in clinical decision-making, those currently available have limited applicability, robustness, and generalizability. Here we applied a modified Framingham Heart Study approach to derive 1- and 2-year all-cause mortality risk scores using a 11,508 European incident HD patient database (AROii) recruited between 2007 and 2009. This scoring model was validated externally using similar-sized Dialysis Outcomes and Practice Patterns Survey (DOPPS) data. For AROii, the observed 1- and 2-year mortality rates were 13.0 (95% confidence interval (CI; 12.3-13.8)) and 11.2 (10.4-12.1)/100 patient years, respectively. Increasing age, low body mass index, history of cardiovascular disease or cancer, and use of a vascular access catheter during baseline were consistent predictors of mortality. Among baseline laboratory markers, hemoglobin, ferritin, C-reactive protein, serum albumin, and creatinine predicted death within 1 and 2 years. When applied to the DOPPS population, the predictive risk score models were highly discriminatory, and generalizability remained high when restricted by incidence/prevalence and geographic location (C-statistics 0.68-0.79). This new model offers improved predictive power over age/comorbidity-based models and also predicted early mortality (C-statistic 0.71). Our new model delivers a robust and reproducible mortality risk score, based on readily available clinical and laboratory data.

SUBMITTER: Floege J

PROVIDER: S-EPMC4424813 | biostudies-other | 2015 May

REPOSITORIES: biostudies-other

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Development and validation of a predictive mortality risk score from a European hemodialysis cohort.

Floege Jürgen J Gillespie Iain A IA Kronenberg Florian F Anker Stefan D SD Gioni Ioanna I Richards Sharon S Pisoni Ronald L RL Robinson Bruce M BM Marcelli Daniele D Froissart Marc M Eckardt Kai-Uwe KU

Kidney international 20150204 5

Although mortality risk scores for chronic hemodialysis (HD) patients should have an important role in clinical decision-making, those currently available have limited applicability, robustness, and generalizability. Here we applied a modified Framingham Heart Study approach to derive 1- and 2-year all-cause mortality risk scores using a 11,508 European incident HD patient database (AROii) recruited between 2007 and 2009. This scoring model was validated externally using similar-sized Dialysis O ...[more]

PMID: 25651366

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Project description:BackgroundPatients suffering from major trauma often experience complications such as sepsis. The early recognition of patients at high risk of sepsis after trauma is critical for precision therapy. We aimed to derive and validate a novel predictive score for sepsis risk using electronic medical record (EMR) data following trauma.Materials and methodsClinical and laboratory variables of 684 trauma patients within 24 h after admission were collected, including 411 patients in the training cohort and 273 in the validation cohort. The least absolute shrinkage and selection operator (LASSO) technique was adopted to identify variables contributing to the early prediction of traumatic sepsis. Then, we constructed a traumatic sepsis score (TSS) using a logistic regression model based on the variables selected in the LASSO analysis. Moreover, we evaluated the discrimination and calibration of the TSS using the area under the curve (AUC) and the Hosmer-Lemeshow (H-L) goodness-of-fit test.ResultsBased on the LASSO, seven variables (injury severity score, Glasgow Coma Scale, temperature, heart rate, albumin, international normalized ratio, and C-reaction protein) were selected for construction of the TSS. Our results indicated that the incidence of sepsis after trauma increased with an increasing TSS (P trend = 7.44 × 10-21 for the training cohort and P trend = 1.16 × 10-13 for the validation cohort). The areas under the receiver operating characteristic (ROC) curve of TSS were 0.799 (0.757-0.837) and 0.790 (0.736-0.836) for the training and validation datasets, respectively. The discriminatory power of our model was superior to that of a single variable and the sequential organ failure assessment (SOFA) score (P < 0.001). Moreover, the TSS was well calibrated (P > 0.05).ConclusionsWe developed and validated a novel TSS with good discriminatory power and calibration for the prediction of sepsis risk in trauma patients based on the EMR data.

Project description:BackgroundWorldwide, nearly 800,000 individuals die by suicide each year; however, longitudinal prediction of suicide attempts remains a major challenge within the field of psychiatry. The objective of the present research was to develop and evaluate an evidence-based suicide attempt risk checklist [i.e., the Durham Risk Score (DRS)] to aid clinicians in the identification of individuals at risk for attempting suicide in the future.Methods and findingsThree prospective cohort studies, including a population-based study from the United States [i.e., the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) study] as well as 2 smaller US veteran cohorts [i.e., the Assessing and Reducing Post-Deployment Violence Risk (REHAB) and the Veterans After-Discharge Longitudinal Registry (VALOR) studies], were used to develop and validate the DRS. From a total sample size of 35,654 participants, 17,630 participants were selected to develop the checklist, whereas the remaining participants (N = 18,024) were used to validate it. The main outcome measure was future suicide attempts (i.e., actual suicide attempts that occurred after the baseline assessment during the 1- to 3-year follow-up period). Measure development began with a review of the extant literature to identify potential variables that had substantial empirical support as longitudinal predictors of suicide attempts and deaths. Next, receiver operating characteristic (ROC) curve analysis was utilized to identify variables from the literature review that uniquely contributed to the longitudinal prediction of suicide attempts in the development cohorts. We observed that the DRS was a robust prospective predictor of future suicide attempts in both the combined development (area under the curve [AUC] = 0.91) and validation (AUC = 0.92) cohorts. A concentration of risk analysis found that across all 35,654 participants, 82% of prospective suicide attempts occurred among individuals in the top 15% of DRS scores, whereas 27% occurred in the top 1%. The DRS also performed well among important subgroups, including women (AUC = 0.91), men (AUC = 0.93), Black (AUC = 0.92), White (AUC = 0.93), Hispanic (AUC = 0.89), veterans (AUC = 0.91), lower-income individuals (AUC = 0.90), younger adults (AUC = 0.88), and lesbian, gay, bisexual, transgender, and queer or questioning (LGBTQ) individuals (AUC = 0.88). The primary limitation of the present study was its its reliance on secondary data analyses to develop and validate the risk score.ConclusionsIn this study, we observed that the DRS was a strong predictor of future suicide attempts in both the combined development (AUC = 0.91) and validation (AUC = 0.92) cohorts. It also demonstrated good utility in many important subgroups, including women, men, Black, White, Hispanic, veterans, lower-income individuals, younger adults, and LGBTQ individuals. We further observed that 82% of prospective suicide attempts occurred among individuals in the top 15% of DRS scores, whereas 27% occurred in the top 1%. Taken together, these findings suggest that the DRS represents a significant advancement in suicide risk prediction over traditional clinical assessment approaches. While more work is needed to independently validate the DRS in prospective studies and to identify the optimal methods to assess the constructs used to calculate the score, our findings suggest that the DRS is a promising new tool that has the potential to significantly enhance clinicians' ability to identify individuals at risk for attempting suicide in the future.

Dataset Information

Development and validation of a predictive mortality risk score from a European hemodialysis cohort.

Publications

Development and validation of a predictive mortality risk score from a European hemodialysis cohort.

Similar Datasets

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