Dynamic prediction of competing risk events using landmark sub-distribution hazard model with multiple longitudinal biomarkers

Predicting kidney graft function and failure among kidney transplant recipients

BACKGROUND

Graft loss is a major health concern for kidney transplant (KTx) recipients. It is of clinical interest to develop a prognostic model for both graft function, quantified by estimated glomerular filtration rate (eGFR), and the risk of graft failure. Additionally, the model should be dynamic in the sense that it adapts to accumulating longitudinal information, including time-varying at-risk population, predictor-outcome association, and clinical history. Finally, the model should also properly account for the competing risk by death with a functioning graft. A model with the features above is not yet available in the literature and is the focus of this research.

METHODS

We built and internally validated a prediction model on 3,893 patients from the Wisconsin Allograft Recipient Database (WisARD) who had a functioning graft 6 months after kidney transplantation. The landmark analysis approach was used to build a proof-of-concept dynamic prediction model to address the aforementioned methodological issues: the prediction of graft failure, accounted for competing risk of death, as well as the future eGFR value, are updated at each post-transplant time. We used 21 predictors including recipient characteristics, donor characteristics, transplant-related and post-transplant factors, longitudinal eGFR, hospitalization, and rejection history. A sensitivity analysis explored a less conservative variable selection rule that resulted in a more parsimonious model with reduced predictors.

RESULTS

For prediction up to the next 1 to 5 years, the model achieved high accuracy in predicting graft failure, with the AUC between 0.80 and 0.95, and moderately high accuracy in predicting eGFR, with the root mean squared error between 10 and 18 mL/min/1.73m2 and 70%-90% of predicted eGFR falling within 30% of the observed eGFR. The model demonstrated substantial accuracy improvement compared to a conventional prediction model that used only baseline predictors.

CONCLUSION

The model outperformed conventional prediction model that used only baseline predictors. It is a useful tool for patient counseling and clinical management of KTx and is currently available as a web app.

Dynamic survival prediction of end-stage kidney disease using random survival forests for competing risk analysis

Background and hypothesis

A static predictive model relying solely on baseline clinicopathological data cannot capture the heterogeneity in predictor trajectories observed in the progression of chronic kidney disease (CKD). To address this, we developed and validated a dynamic survival prediction model using longitudinal clinicopathological data to predict end-stage kidney disease (ESKD), with death as a competing risk.

Methods

We trained a sequence of random survival forests using a landmarking approach and optimized the model with a pre-specified prediction horizon of 5 years. The predicted cumulative incidence function (CIF) values were used to generate a personalized dynamic prediction plot.

Results

The model was developed using baseline demographics and 13 longitudinal clinicopathological variables from 4,950 patients. Variable importance analysis for ESKD and death informed the creation of a sequence of reduced models that utilized six key variables: age, serum albumin, bicarbonate, chloride, eGFR, and hemoglobin. The models demonstrated robust predictive performance, with a median concordance index of 84.84% for ESKD and 84.1% for death. The median integrated Brier scores were 0.03 for ESKD and 0.038 for death across all landmark times. External validation with 8,729 patients confirmed these results.

Conclusion

We successfully developed and validated a dynamic survival prediction model using common longitudinal clinicopathological data. This model predicts ESKD with death as a competing risk and aims to assist clinicians in dialysis planning for patients with CKD.

Dynamic effects of prognostic factors and individual survival prediction for amyotrophic lateral sclerosis disease

OBJECTIVE

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease affecting motor neurons, with broad heterogeneity in disease progression and survival in different patients. Therefore, an accurate prediction model will be crucial to implement timely interventions and prolong patient survival time.

METHODS

A total of 1260 ALS patients from the PRO-ACT database were included in the analysis. Their demographics, clinical variables, and death reports were included. We constructed an ALS dynamic Cox model through the landmarking approach. The predictive performance of the model at different landmark time points was evaluated by calculating the area under the curve (AUC) and Brier score.

RESULTS

Three baseline covariates and seven time-dependent covariates were selected to construct the ALS dynamic Cox model. For better prognostic analysis, this model identified dynamic effects of treatment, albumin, creatinine, calcium, hematocrit, and hemoglobin. Its prediction performance (at all landmark time points, AUC ≥ 0.70 and Brier score ≤ 0.12) was better than that of the traditional Cox model, and it predicted the dynamic 6-month survival probability according to the longitudinal information of individual patients.

INTERPRETATION

We developed an ALS dynamic Cox model with ALS longitudinal clinical trial datasets as the inputs. This model can not only capture the dynamic prognostic effect of both baseline and longitudinal covariates but also make individual survival predictions in real time, which are valuable for improving the prognosis of ALS patients and providing a reference for clinicians to make clinical decisions.

Predicting the risk of a clinical event using longitudinal data: the generalized landmark analysis

BACKGROUND

In the development of prediction models for a clinical event, it is common to use the static prediction modeling (SPM), a regression model that relates baseline predictors to the time to event. In many situations, the data used in training and validation are from longitudinal studies, where predictor variables are time-varying and measured at clinical visits. But these data are not used in SPM. The landmark analysis (LA), previously proposed for dynamic prediction with longitudinal data, has interpretational difficulty when the baseline is not a risk-changing clinical milestone, as is often the case in observational studies of chronic disease without intervention.

METHODS

This paper studies the generalized landmark analysis (GLA), a statistical framework to develop prediction models for longitudinal data. The GLA includes the LA as a special case, and generalizes it to situations where the baseline is not a risk-changing clinical milestone with a more useful interpretation. Unlike the LA, the landmark variable does not have to be time since baseline in the GLA, but can be any time-varying prognostic variable. The GLA can also be viewed as a longitudinal generalization of localized prediction, which has been studied in the context of low-dimensional cross-sectional data. We studied the GLA using data from the Chronic Renal Insufficiency Cohort (CRIC) Study and the Wisconsin Allograft Replacement Database (WisARD) and compared the prediction performance of SPM and GLA.

RESULTS

In various validation populations from longitudinal data, the GLA generally had similarly or better predictive performance than SPM, with notable improvement being seen when the validation population deviated from the baseline population. The GLA also demonstrated similar or better predictive performance than LA, due to its more general model specification.

CONCLUSIONS

GLA is a generalization of the LA such that the landmark variable does not have to be the time since baseline. It has better interpretation when the baseline is not a risk-changing clinical milestone. The GLA is more adaptive to the validation population than SPM and is more flexible than LA, which may help produce more accurate prediction.

Development and validation of a dynamic 48-hour in-hospital mortality risk stratification for COVID-19 in a UK teaching hospital: a retrospective cohort study

OBJECTIVES

To develop a disease stratification model for COVID-19 that updates according to changes in a patient's condition while in hospital to facilitate patient management and resource allocation.

DESIGN

In this retrospective cohort study, we adopted a landmarking approach to dynamic prediction of all-cause in-hospital mortality over the next 48 hours. We accounted for informative predictor missingness and selected predictors using penalised regression.

SETTING

All data used in this study were obtained from a single UK teaching hospital.

PARTICIPANTS

We developed the model using 473 consecutive patients with COVID-19 presenting to a UK hospital between 1 March 2020 and 12 September 2020; and temporally validated using data on 1119 patients presenting between 13 September 2020 and 17 March 2021.

PRIMARY AND SECONDARY OUTCOME MEASURES

The primary outcome is all-cause in-hospital mortality within 48 hours of the prediction time. We accounted for the competing risks of discharge from hospital alive and transfer to a tertiary intensive care unit for extracorporeal membrane oxygenation.

RESULTS

Our final model includes age, Clinical Frailty Scale score, heart rate, respiratory rate, oxygen saturation/fractional inspired oxygen ratio, white cell count, presence of acidosis (pH <7.35) and interleukin-6. Internal validation achieved an area under the receiver operating characteristic (AUROC) of 0.90 (95% CI 0.87 to 0.93) and temporal validation gave an AUROC of 0.86 (95% CI 0.83 to 0.88).

CONCLUSIONS

Our model incorporates both static risk factors (eg, age) and evolving clinical and laboratory data, to provide a dynamic risk prediction model that adapts to both sudden and gradual changes in an individual patient's clinical condition. On successful external validation, the model has the potential to be a powerful clinical risk assessment tool.

TRIAL REGISTRATION

The study is registered as 'researchregistry5464' on the Research Registry (www.researchregistry.com).

Effect of diabetes medications and glycemic control on risk of hepatocellular cancer in patients with nonalcoholic fatty liver disease

BACKGROUND AND AIMS

In patients with NAFLD, those with type 2 diabetes mellitus (DM) have a high risk of progression to HCC. However, the determinants of HCC risk in these patients remain unclear.

APPROACH AND RESULTS

We assembled a retrospective cohort of patients with NAFLD and DM diagnosed at 130 facilities in the Veterans Administration between 1/1/2004 and 12/31/2008. We followed patients from the date of NAFLD diagnosis to HCC, death, or 12/31/2018. We used landmark Cox proportional hazards models to determine the effects of anti-DM medications (metformin, insulin, sulfonylureas) and glycemic control (percent of follow-up time with hemoglobin A1c < 7%) on the risk of HCC while adjusting for demographics and other metabolic traits (hypertension, obesity, dyslipidemia). We identified 85,963 patients with NAFLD and DM. In total, 524 patients developed HCC during a mean of 10.3 years of follow-up. Most common treatments were metformin monotherapy (19.7%), metformin-sulfonylureas (19.6%), insulin (9.3%), and sulfonylureas monotherapy (13.6%). Compared with no medication, metformin was associated with 20% lower risk of HCC (HR, 0.80; 95% CI, 0.93-0.98). Insulin had no effect on HCC risk (HR, 1.02; 95% CI, 0.85-1.22; p = 0.85). Insulin in combination with other oral medications was associated with a 1.6 to 1.7-fold higher risk of HCC. Adequate glycemic control was associated with a 31% lower risk of HCC (HR, 0.69; 95% CI, 0.62-0.78).

CONCLUSIONS

In this large cohort of patients with NAFLD and DM, use of metformin was associated with a reduced risk of HCC, whereas use of combination therapy was associated with increased risk. Glycemic control can serve as a biomarker for HCC risk stratification in patients with NAFLD and diabetes.

Time-Updated Changes in Estimated GFR and Proteinuria and Major Adverse Cardiac Events: Findings from the Chronic Renal Insufficiency Cohort (CRIC) Study

RATIONALE & OBJECTIVE

Evaluating repeated measures of estimated glomerular filtration rate (eGFR) and urinary protein-creatinine ratio (UPCR) over time may enhance our ability to understand the association between changes in kidney parameters and cardiovascular disease risk.

STUDY DESIGN

Prospective cohort study.

SETTING & PARTICIPANTS

Annual visit data from 2,438 participants in the Chronic Renal Insufficiency Cohort (CRIC).

EXPOSURES

Average and slope of eGFR and UPCR in time-updated, 1-year exposure windows.

OUTCOMES

Incident heart failure, atherosclerotic cardiovascular disease events, death, and a composite of incident heart failure, atherosclerotic cardiovascular disease events, and death.

ANALYTICAL APPROACH

A landmark analysis, a dynamic approach to survival modeling that leverages longitudinal, iterative profiles of laboratory and clinical information to assess the time-updated 3-year risk of adverse cardiovascular outcomes.

RESULTS

Adjusting for baseline and time-updated covariates, every standard deviation lower mean eGFR (19mL/min/1.73m²) and declining slope of eGFR (8mL/min/1.73m² per year) were independently associated with higher risks of heart failure (hazard ratios [HRs] of 1.82 [95% CI, 1.39-2.44] and 1.28 [95% CI, 1.12-1.45], respectively) and the composite outcome (HRs of 1.32 [95% CI, 1.11-1.54] and 1.11 [95% CI, 1.03-1.20], respectively). Every standard deviation higher mean UPCR (136mg/g) and increasing UPCR (240mg/g per year) were also independently associated with higher risks of heart failure (HRs of 1.58 [95% CI, 1.28-1.97] and 1.20 [95% CI, 1.10-1.29], respectively) and the composite outcome (HRs of 1.33 [95% CI, 1.17-1.50] and 1.12 [95% CI, 1.06-1.18], respectively).

LIMITATIONS

Limited generalizability of annual eGFR and UPCR assessments; several biomarkers for cardiovascular disease risk were not available annually.

CONCLUSIONS

Using the landmark approach to account for time-updated patterns of kidney function, average and slope of eGFR and proteinuria were independently associated with 3-year cardiovascular risk. Short-term changes in kidney function provide information about cardiovascular risk incremental to level of kidney function, representing possible opportunities for more effective management of patients with chronic kidney disease.