 Research
 Open access
 Published:
Comparison of StateoftheArt Neural Network Survival Models with the Pooled Cohort Equations for Cardiovascular Disease Risk Prediction
BMC Medical Research Methodology volume 23, Article number: 22 (2023)
Abstract
Background
The Pooled Cohort Equations (PCEs) are race and sexspecific Cox proportional hazards (PH)based models used for 10year atherosclerotic cardiovascular disease (ASCVD) risk prediction with acceptable discrimination. In recent years, neural network models have gained increasing popularity with their success in image recognition and text classification. Various survival neural network models have been proposed by combining survival analysis and neural network architecture to take advantage of the strengths from both. However, the performance of these survival neural network models compared to each other and to PCEs in ASCVD prediction is unknown.
Methods
In this study, we used 6 cohorts from the Lifetime Risk Pooling Project (with 5 cohorts as training/internal validation and one cohort as external validation) and compared the performance of the PCEs in 10year ASCVD risk prediction with an all twoway interactions Cox PH model (Cox PHTWI) and three stateoftheart neural network survival models including Nnetsurvival, Deepsurv, and Coxnnet. For all the models, we used the same 7 covariates as used in the PCEs. We fitted each of the aforementioned models in white females, white males, black females, and black males, respectively. We evaluated models’ internal and external discrimination power and calibration.
Results
The training/internal validation sample comprised 23216 individuals. The average age at baseline was 57.8 years old (SD = 9.6); 16% developed ASCVD during average followup of 10.50 (SD = 3.02) years. Based on 10 × 10 crossvalidation, the method that had the highest Cstatistics was Deepsurv (0.7371) for white males, Deepsurv and Cox PHTWI (0.7972) for white females, PCE (0.6981) for black males, and Deepsurv (0.7886) for black females. In the external validation dataset, Deepsurv (0.7032), Coxnnet (0.7282), PCE (0.6811), and Deepsurv (0.7316) had the highest Cstatistics for white male, white female, black male, and black female population, respectively. Calibration plots showed that in 10 × 10 validation, all models had good calibration in all race and sex groups. In external validation, all models overestimated the risk for 10year ASCVD.
Conclusions
We demonstrated the use of the stateoftheart neural network survival models in ASCVD risk prediction. Neural network survival models had similar if not superior discrimination and calibration compared to PCEs.
Background
Cox Proportional Hazards (Cox PH) model is widely used to quantify the effect of covariates in relation to timetoevent outcomes or to predict the survival time for a new individual [1]. Cox PH is a semiparametric model, which consists of two main components: baseline hazard and multiplicative covariate effect in hazard ratio. The estimates of its regression coefficients are obtained through optimization of the partial likelihood function, which depends on both censored and uncensored individuals.
With the availability of large datasets and highspeed computational power, neural network algorithms have become increasingly popular. Neural networks have been successful when applied to unstructured data such as image recognition and text classification [2,3,4,5,6,7]. Compared to Cox PH, standard neural network architectures focus on predicting outcomes as a binary classification problem at a specific followup point. However, it is common in medical studies that individuals are lost to followup (censored data) before the failure or event time. Standard neural network models cannot train or test on these individuals. In 1995, FaraggiSimon first combined neural network architectures with the Cox PH model to make use of censored information as well as to model nonlinear featuresoutcome relations [8]. Since then, there has been increasing interest in incorporating neural network architectures in survival analysis. In current literature, there are two main ways of modeling timetoevent using neural networks: (i) adapting Cox PH model and using partial likelihood loss, e.g., Coxnnet [9] and Deepsurv [10]; or (ii) discretizing survival time and using a heuristic loss function, e.g., Nnetsurvival [11].
Atherosclerotic cardiovascular disease (ASCVD) is the leading cause of death globally [12]. Currently, some commonly used prediction models for ASCVD are based on Cox PH, such as the Framingham CHD risk score and its derivatives [13]. In recent years, the American College of Cardiology (ACC)/American Heart Association (AHA) guidelines developed new equations, i.e., the Pooled Cohort Equations (PCEs), to estimate 10year ASCVD risk in nonHispanic whites and African Americans [14]. The equations are developed based on datasets from several communitybased epidemiology cohort studies. The PCEs are four race, sexspecific and Cox PH based models. It is unclear whether neural network survival models can outperform PCEs for 10year ASCVD risk prediction. In addition, it is unclear how different architectures of neural network survival models perform compared to each other. In this study, we compared the four race and sexspecific PCEs with race and sexspecific stateoftheart neural network survival models: Nnetsurvival, Deepsurv, and Coxnnet [10, 11, 15] in primary ASCVD risk prediction. For fair comparison, we also included Cox PH models with all significant twoway interactions since this enables Cox PH to capture more complex relationships. For all models, we used the same seven predictors as in the PCEs. Our study is the first study to compare the stateoftheart neural network survival models with PCEs in incident ASCVD prediction.
Methods
Model I, II: Pooled Cohort Equations, all twoway interaction Cox PH
PCEs are four Cox PH based models, each of which is for a specific race and sex group (white male, white female, black male, black female). Cox PH models the probability an individual experiences the event during a smalltime interval given the individual is free of an event at the beginning of the time interval [1], which is also known as hazard rate. Specifically, the hazard function can be expressed as the follows:
where \(t\) is the survival time, \({\lambda }_{0}\left(t\right)\) is the baseline hazard risk at time \(t\), \({{\varvec{X}}}_{i}={\left[{X}_{i1},\dots ,{X}_{ip}\right]}^{T}\) contains the covariates for individual \(i\), and \({\varvec{\beta}}={\left[{\beta }_{1},\dots ,{\beta }_{p}\right]}^{{\varvec{T}}}\) is the regression coefficient vector. The hazard function consists of two parts: baseline hazard \({\lambda }_{0}\left(t\right)\) and a hazard ratio or risk function \(\mathrm{exp}\left({{\varvec{X}}}_{i}^{T}{\varvec{\beta}}\right)\). Cox PH assumes that the relative risk for each covariate (\({\varvec{\beta}}\) in the equation) is constant over time. The estimate of \({\varvec{\beta}}\) is obtained by optimizing the Cox partial likelihood function as defined below:
where \({\Delta }_{i}\) is the indicator for the occurrence of event and \({Y}_{j}\) is followup time for individual \(j\).
In the PCEs, seven predictors were selected based on demonstrated statistical utility using prespecified criteria [14]. These predictors include age at baseline, systolic blood pressure (SBP), diabetes medical history, treatment for hypertension, current smoker, high density cholesterol and total cholesterol. The interactions between age at baseline and the other predictors were tested based on pvalues. Only interactions that had significant pvalues (< 0.05) were kept in the model. The PCEs demonstrated acceptable performance in derivation samples, with Cstatistics for 10year risk prediction of 0.80 in white females, 0.76 in white males, 0.81 in black females, and 0.70 in black males in 10 × 10 crossvalidation [14].
To capture more complex relationships between predictors and ASCVD outcome, in the Cox PHTWI model, we included all the twoway interactions of the 7 predictors in the model for each race and sex. We then retained only the interaction terms that had significant pvalues for each race and sex.
Models III and IV: Deepsurv and Coxnnet
Deepsurv and Coxnnet are both adaptations of the standard Cox PH [10]. Instead of assuming the linear relationship between covariates and loghazard, the Deepsurv and Coxnnet models can automatically learn the nonlinear relationship between risk factors and an individual’s risk of failure by its linear (i.e., multilayer perceptron) and nonlinear (activation functions) transformation. Specifically, the logrisk function \({{\varvec{X}}}_{i}^{T}{\varvec{\beta}}\) in the Cox equation as shown in Eq. (1) is replaced by the output from neural network \({h}_{w,{{\varvec{\beta}}}^{\boldsymbol{*}}}\left({{\varvec{X}}}_{i}\right)\), where \({{\varvec{\beta}}}^{\boldsymbol{*}}\) is the weight for the last hidden layer and \(w\) is the weight for other hidden layers for neural network (see Fig. 1A):
The neural network optimizes the logpartial likelihood function similar to the standard Cox model by tuning parameters \({\varvec{W}},{{\varvec{\beta}}}^{\boldsymbol{*}}\):
Coxnnet was proposed to deal with high dimensional features especially in genomic studies. To avoid overfitting, Coxnnet introduces a ridge regularization term and subsequently, the partial log likelihood in Eq. (2) is extended as the following:
In addition to L_{2}regularizer, Coxnnet also allows dropout for regularization to avoid overfitting. The model is based on Theano framework, therefore, Coxnnet can be run on a Graphics Processing Unit or multiple threads.
The Deepsurv model also allows the abovementioned regularization techniques to avoid overfitting. In addition to that, Deepsurv adapted modern techniques to improve the training of the network such as introducing scaled Exponential Linear Units (SELU) as the activation function [8].
Although both the Coxnnet and Deepsurv can learn the nonlinear relationship between risk factors and the event risk, it is important to note that proportional hazard assumption still stands in the sense that the hazard ratio between any individual \(\mathrm{i}\) and \(j\) is constant over time.
Model V: Nnetsurvival
Nnetsurvival is a fully parametric survival model that discretizes survival time. Nnetsurvival is proposed to improve two main aspects of the neural network model that are adapted from Cox model: computational speed and the violation of the proportional hazard assumption. Neural network survival models that adapt from Cox model (e.g., Deepsurv, Coxnnet) use partial likelihood function as the loss function to optimize. The partial likelihood function is calculated based on not only the current individual but also all the individuals that are at risk at the time point. This makes it difficult to use stochastic gradient descent or minibatch gradient descent, both of which use a small subset of the whole dataset. Therefore, both Deepsurv and Coxnnet may have slow convergence and cannot be applied to large datasets that run out of memory [9]. Nnetsurvival was proposed to discretize time, which transforms the model into a fully parametric model and avoids the use of partial likelihood as the loss function. In Nnetsurvival models, followup time is discretized to \(n\) intervals. Hazard \({h}_{j}\) is defined as the conditional probability of surviving time interval \(j\) given the individual is alive at the beginning of interval \(j\). Survival probability at the end of interval \(j\) can be then calculated as the following:
The loss function is defined as the following:
for individuals who fail at interval \(j\), and
for individuals who are censored at the second half of interval \(j1\) or the first half of interval \(j\).
There are two main architectures of Nnetsurvival: a flexible version and a proportional hazards version. In the flexible version, output layers have \(n\) neurons, where \(n\) is the number of intervals and each output neuron represents the survival probability at the specific time interval given an individual is alive at the beginning of the time interval. In the proportional hazard version, the final layer only has a single neuron representing \({{\varvec{X}}}_{i}^{T}{\varvec{\beta}}\):
In our study, the flexible version is used, with its architecture of the flexible version shown in Fig. 1B.
Statistical analysis
In this study, we used the harmonized, individuallevel data from 6 cohorts in the Lifetime Risk Pooling Project, including Atherosclerosis Risk in Communities (ARIC) study, Cardiovascular Health Study (CHS), Framingham Offsprinig study, Coronary Artery Risk Development in Young Adults (CARDIA) study, the Framingham Original study, and the MultiEthnic Study of Atherosclerosis (MESA). The first 5 cohort data were used for model development and internal validation, and the MESA data was used for external validation. We included individuals that meet the following criteria: (i) age between 40 to 79; and (ii) free of a previous history of myocardial infarction, stroke, congestive heart failure, or atrial fibrillation. ASCVD was defined as nonfatal myocardial infarction or coronary heart disease death, or fatal or nonfatal stroke (see [14] for details of selection criteria). All study individuals were free of ASCVD at the beginning of the study and were followed up until the first ASCVD event, loss to follow up, or death, whichever came first. We fit PCE, Cox PH with all twoway interactions (Cox PHTWI), Nnetsurvival, Deepsurv, and Coxnnet models in white male, white female, black male, and black female participants. For comparison purposes, for all the models, we included the same predictors as used in the PCEs: age at baseline, systolic blood pressure (SBP), diabetes medical history, treatment for hypertension, current smoker, high density cholesterol (HDLC) and total cholesterol. The details of the study start and end dates, study settings, and how the covariates were collected can be found in [14]. Individuals who had missing data at baseline were excluded from the study.
To obtain high performance neural network survival models, we manually tuned various hyperparameters including learning rate, number of layers, number of neurons, number of epochs, batch size, momentum, optimizer, learning rate decay, batch normalization, L_{2} regularization, and dropout. More specifically, to tune the hyperparameters in the development dataset (the five training/internal validation cohorts), we split the data into 10 folds. We used nine folds of the data for training and the rest onefold for evaluation. We used grid search to search through a range of hyperparameters and select the hyperparameter combination that generates the highest Cindex in the onefold evaluation dataset. After selecting the optimal hyperparameters, we evaluated model performance through internal validation with 10 × 10 cross validation and external validation with the MESA data. To perform 10 × 10 crossvalidation, we randomly partitioned the pooled cohort data into 10 equalsized subsamples. Of the 10 subsamples, 9 subsamples were used as training data and the remaining single subsample was retained as the validation data for testing the model. Each of the subsamples is used in turn as the validation data. We repeated this process 10 times, during which 100 models were built. The average Cstatistics and calibration plot of the 100 models were used as the final 10 × 10 crossvalidation result. For PCE and Cox PHTWI models, we refit the models in each of the crossvalidated training samples for the internal 10 × 10 crossvalidation to avoid overfitting. In each refit, we kept the original structure of the original PCE models and only updated the coefficients of the models. In the calibration plots, the observed and predicted events were shown in deciles [11]. For the external validation, we trained the model in the whole harmonized dataset (not including MESA cohort) and evaluated the model discrimination and calibration in the external MESA cohort. To compare whether the differences among Cstatistics were significant in neural network models vs. PCE models, we performed significant test using method proposed by Uno et al. [16]. MESA is a more contemporary cohort that had lower CVD event rate compared to the earlier cohorts [14]. This difference could cause models to have poor calibration in MESA. To overcome this, we performed recalibration on all models using the method proposed by Pennells et al. [17]. Briefly, we first calculated rescaling factors that were needed to bring predicted risks in line with observed risks using regression model in MESA dataset. We then applied the rescaling factors to the original predicted risk and got recalibrated risk estimates for all participants.
Nnetsurvival, Deepsurv, and Coxnnet were implemented in python, version 3.7.3. Cox PH model was conducted using the “survival” package in R, version 3.6.0. Cstatistics and the significant test in Cstatistics between two competing risk prediction models were calculated using the “survC1” package in R, version 3.6.0 [16]. We chose 0.05 as the statistical significance level. Regression model for recalibration was performed using “scikitlearn” module in python, version 3.7.3 [18].
All data were deidentified, and all study protocols and procedures were approved by the Institutional Review Board at Northwestern University with a waiver for informed consent. All methods were performed in accordance with the relevant guidelines and regulations.
Results
Overall, out of 26406 participants, 3190 (13.7%) had missing data at baseline. After excluding participants with missing data, there were 23216 participants left in total, including 8644 white male, 1354 black male, 10719 white female, 2499 black female individuals. The average age at baseline was 57.8 years old (SD = 9.6). Among these individuals, 16.0% developed ASCVD with average followup of 10.50 (SD = 3.02) years. The mean SBP value was 127.1 mmHg (SD = 21.0), the mean HDLC value was 51.6 mg/dL (SD = 16.4), total cholesterol was 217.8 mg/dL (SD = 43.0). For binary predictors, 4.6% individuals had a history of diabetes, 26.0% individuals were current smokers, 31.6% individuals had treatment for hypertension. SBP, HDLC, TOTCHL, history of diabetes, smoker percentage, age, and history of hypertension are all significantly different among the four race gender groups. More specifically, black males have the highest SBP, HDLCL, percentage of diabetes history, percentage of smokers. The descriptive statistics for each race and sex group were shown in Table 1.
In the MESA external validation dataset, there were 4259 individuals in total. The average age at baseline was 61.6 years old (SD = 9.6). Among the 4259 individuals, 331 (7.77%) developed ASCVD with average followup years of 10.97 years old (SD = 2.48). Among these individuals, there were 1194 white males, 799 black males, 1284 white females, and 982 black females. All the baseline characteristics (among the 7 covariates) are significantly different among the four race gender groups, except for age. Similarly, we observed that black males have the highest SBP, HDLC, percentage of diabetes history, percentage of smokers. Baseline characteristics of the study sample were shown in Table 1, stratified by race and sex group.
In 10 × 10 cross validation, in the white male population (see Fig. 2 and supplemental Table 1), Deepsurv achieved the highest Cstatistics (0.7371) among all the models. In the white female population, Deepsurv (0.7972) and Cox PHTWI (0.7972) had the highest Cstatistics. In the black male population, PCE had the highest Cstatistics (0.6981). In the black female population, Deepsurv had the highest Cstatistics (0.7886). The details of Cstatistics for each model and race sex group were shown in Supplemental Table 1. In the external validation dataset, in white male population, Deepsurv had the highest Cstatistics (0.7032). In white female population, Coxnnet had the highest Cstatistics (0.7282). In black male population, PCE had the highest Cstatistics (0.6811). In black female population, Deepsurv (0.7316) had the highest Cstatistics and this difference was statistically significant compared to PCE (p = 0.00, see Supplemental Table 1). Overall, when including results from both internal and external validations, Deepsurv had the highest Cstatistics for five times followed by PCE which had the highest Cstatistics for two times followed by Coxnnet and Cox PHTWI for one time. However, the difference between all neural network models vs. PCE are not significant except for Deepsurv in black females (see Supplemental Table 1).
In terms of calibration, in 10 × 10 crossvalidation (see Fig. 3), the calibration plot showed that all five models had similar calibration compared to PCE in white male and white female population. In black male population, PCE and Cox PHTWI had better calibration compared to neural network models. In black female population, all models have better calibration than Coxnnet. In the MESA external validation dataset, calibration plot showed that all five models overestimated the event rate among all race and sex groups. In white female and black female populations, all five models had similar overestimation with predicted event rate ranging from 0 to approximately 0.43 compared to 0 to approximately 0.18 in the observed event rate (see Fig. 4). In white male and black male populations, all five models had similar overestimation with predicted event rate ranging from 0 to approximately 0.6 compared to 0 to approximately 0.2 in the observed event rate (see Fig. 4). After recalibration by fitting linear regression models, overestimations of event risk were greatly reduced in all models among all race and sex groups (see Fig. 5). The recalibration intercept and slope are summarized in Supplemental table 2.
Discussion
In this study, we implemented stateoftheart neural network survival models in predicting 10year risk for a first ASCVD event. Our results showed that overall, when using the same predictors as in the PCEs, neural network survival models and PCE had comparable discrimination. Neural network survival models outperformed PCE in white male, white female, and black female population by slim margins. However, the difference is not statistically significant expect for Deepsurv in black female population. In terms of calibration, in the internal validation dataset, PCEs, CoxPH TWI had good calibration across all race and sex populations, while the neural network models’ performance is not consistent. In external validation dataset, all models overestimated the event rate in all four racesex groups. Recalibration largely reduced the overestimation. Among different race gender groups, models in black males have relatively worse performance, which is consistent with the results from the PCE paper [1]. The number of African Americans, particularly men, is relatively low, which could potentially cause greater level of uncertainty with respect to the estimates. In the training dataset, the sample size for black males is 1354, which is much smaller than the white male (8644) and white female (8644) population. In addition, black male population has the highest ASCVD rate compared to other race gender groups. Our prior work showed racial differences in risks for first cardiovascular events and nonCVD death and competing risks analyses may yield somewhat different results than traditional Cox models and provide a complementary approach to examining risks for first CVD events [24].
Theoretically, among the different neural network survival models, Nnetsurvival is faster in training than Deepsurv and Coxnnet models. Nnetsurvival’s loss function only relies on individuals in the current minibatch which allows minibatch gradient descent while both Deepsurv and Coxnnet require the entire dataset for each gradient descent update. On the other hand, the discretization of timetoevent in Nnetsurvival leads to a less smooth predicted survival curve compared to Deepsurv and Coxnnet.
In prior studies, Gensheimer et al. applied Cox PH, Nnetsurvival, Deepsurv, and Coxnnet in life expectancy prediction using the Study to Understand Prognoses and Preferences for Outcomes and Risks of Treatments (SUPPORT) dataset [11]. The dataset consisted of 9105 individuals and 39 predictors. The four neural network survival models generated similar Cstatistics compared to the Cox PH model, which was consistent with our findings in ASCVD prediction. Both the SUPPORT dataset and our dataset had low dimension number of predictors. Several studies explored other machine learning methods for CVD prediction. Joo et al. [19] applied logistic regression, deep neural networks, random forests, and LightGBM to predict CVD as a binary outcome using the Korean National Health Insurance Service–National Health Sample Cohort dataset. The authors found that deep neural network had better performance (Cstatistics = 0.7446) compared to the PCE (Cstatistics = 0.7381) in that cohort. However, the ML models used more predictors (hemoglobin level, diastolic blood pressure, presence of proteinuria, serum aspartate aminotransferase, serum alanine aminotransferase, and total cholesterol) compared to the PCE. In another study, Dimopoulos et al. implemented KNN, random forest, and decision tree to predict CVD compared to the HellenicSCORE, a Cox regression based model [20]. Their results showed that ML models have comparable performance compared to the HellenicSCORE [21] using 5 and 13 same predictors respectively but were not able to outperform the baseline model.
Similar to other machine learning models, neural network models often show advantage in modeling nonlinear complex relationships between predictors and outcome. We used the same predictors as the PCE, which are all wellstudied risk factors of cardiovascular disease. The biologic basis for many of these variables has been well studied, and they are known to be independently and often linearly associated with risk of cardiovascular events. In this situation, simpler models might suffice since they can accurately capture a linear biologic relation without sacrificing interpretability. Similar conclusions were reached in data comparing three machine learning methods to a simpler logistic regression model for predicting death after acute myocardial infarction [22]. In the study, two of the 3 machine learning algorithms improved discrimination by a slim margin [22]. In the follow up editorial by Engelhard et al. [23], they mentioned that machine learning has been most impactful with complex data (e.g., high dimensional and difficult to summarize without substantial loss of information). There have been some explorations on using machine learning/deep learning with high dimensional features and/or longitudinal risk factors to improve CVD risk prediction. However, the findings are somewhat mixed in the literature. For instance, Zhao et al. implemented convolutional neural network and recurrent neural networks with long shortterm memory using longitudinal electronic health records and genetic data and demonstrated significant improvement over the PCEs [24]. Dolezalova et al. trained both Cox PH and Deepsurv models using 608 variables derived from the UK Biobank. The two models achieved almost identical performance in Cindex, although both models were superior to the Framingham risk score [25]. Taking together, with the same set of predictors as in the PCEs, our results show that the neural network survival models do not provide clinically meaningful improvement over the simpler and more interpretable PCEs. With more high dimensional complex data being readily accessible (e.g., repeated measurement data and imaging data in the electronic health records database), further research is needed to establish the clinical utility of neural network survival models and other machine learning/deep learning models for improving CVD risk prediction.
Limitations
Our study has several limitations. First, the cohorts we used from the Lifetime Risk Pooling Project were the same cohorts used in the derivation of the PCEs. This may have led to some optimism in the performance of the PCEs. Second, the participants of our external validation cohort, MESA, were perhaps healthier than the general population. More importantly, they received intensive screening for subclinical CVD, which influenced health behaviors and preventive interventions including use of effective drug therapies; this may result in the lower event rate in MESA participants than what would have been predicted because of the use of effective preventive therapies selectively in higherrisk individuals.
Conclusion
Neural network survival models can achieve comparable discrimination if not superior performance compared to the PCEs in 10year timetoASCVD prediction in the white female, white male, black female, and black male population in our dataset. In future studies, high dimensional features and/or longitudinal risk factors should be considered to fully explore the benefits of neural network survival models for ASCVD risk prediction.
Availability of data and materials
The Lifetime Risk Pooling Project data used in this study are not publicly available due to data use agreement with the National Heart, Lung, and Blood Institute (NHLBI). These data can be requested from the Biologic Specimen and Data Repositories Information Coordinating Center of NHLBI. Readers interested in the code used for this study may contact the corresponding author.
Abbreviations
 PCEs:

Pooled Cohort Equations
 ASCVD:

Atherosclerotic cardiovascular disease
 Cox PH:

Cox Proportional Hazards
 ACC:

American College of Cardiology
 AHA:

American Heart Association
 ARIC:

Atherosclerosis Risk in Communities study
 CHS:

Cardiovascular Health Study
 CARDIA:

Coronary Artery Risk Development in Young Adults
 SBP:

Systolic blood pressure
References
Cox DR, Oakes D. Analysis of survival data. London; New York: Chapman and Hall; 1984.
Krizhevsky A, Sutskever I, Hinton GE. ImageNet Classification with Deep Convolutional Neural Networks. Communications ACM. 2017;60(6):84–90.
Zeng Z, Deng Y, Li X, Naumann T, Luo Y. Natural Language Processing for EHRBased Computational Phenotyping. IEEE/ACM Trans Comput Biol Bioinform. 2019;16(1):139–53.
Vaswani A, Shazeer N, Parmar N. Attention is all you need. Adv Neural Inf Process Syst. 2017;30:1.
Zhao Y, Hong Q, Zhang X, Deng Y, Wang Y, Petzold L. Bertsurv: Bertbased survival models for predicting outcomes of trauma patients. arXiv preprint arXiv:2103.10928. 2021.
Deng Y, Pacheco J, Chung A, Mao C, Smith J, Zhao J, et al. Natural Language Processing to Identify Lupus Nephritis Phenotype in Electronic Health Records. Arthritis Rheumatol. 2021;73 (suppl 9).
Adekkanattu P, Jiang G, Luo Y, Kingsbury PR, Xu Z, Rasmussen LV, et al. Evaluating the Portability of an NLP System for Processing Echocardiograms: A Retrospective, Multisite Observational Study. AMIA Annu Symp Proc. 2020;2019:190–9.
Faraggi D, Simon R. A neural network model for survival data. Stat Med. 1995;14:73–82.
Ahmed Z, Mohamed K, Zeeshan S, Dong X. Artificial intelligence with multifunctional machine learning platform development for better healthcare and precision medicine. Database (Oxford). 2020;2020:baaa010.
Katzman JL, Shaham U, Cloninger A, Bates J, Jiang T, Kluger Y. DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network. BMC Med Res Methodol. 2018;18(1):24.
Gensheimer MF, Narasimhan B. A scalable discretetime survival model for neural networks. PeerJ. 2019;7:e6257. https://doi.org/10.7717/peerj.6257.
Tsao CW, Aday AW, Almarzooq ZI, Beaton AZ, Bittencourt MS, Boehme AK. Heart Disease and Stroke Statistics—2022 Update: a Report From the American Heart Association. Circulation. 2022;145(8):e153–639.
D’Agostino RB Sr, Grundy S, Sullivan LM, Wilson P. Validation of the Framingham Coronary Heart Disease Prediction Scores: Results of a Multiple Ethnic Groups Investigation. JAMA. 2001;286(2):180–7.
Goff DC Jr, LloydJones DM, Bennett G, Coady S, D’Agostino RB, Gibbons R, Greenland P, Lackland DT, Levy D, O’Donnell CJ, et al. 2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. Circulation. 2014;129(25 Suppl 2):S4973.
Ching TZX, Garmire LX. Coxnnet An artificial neural network method for prognosis prediction of highthroughput omics data. PLoS Comput Biol. 2018;14(4):e1006076.
Uno H, Cai T, Pencina MJ, D'Agostino RB, Wei LJ. On the Cstatistics for evaluating overall adequacy of risk prediction procedures with censored survival data. Stat Med. 2011;30(10):1105–17.
Pennells L, Kaptoge S, Wood A, Sweeting M, Zhao X, White I, Burgess S, Willeit P, Bolton T, Moons KGM, et al. Equalization of four cardiovascular risk algorithms after systematic recalibration: individualparticipant metaanalysis of 86 prospective studies. Eur Heart J. 2019;40(7):621–31.
Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, et al. scikitlearn: machine learning in Python — scikitlearn 1.0.2 documentation. JMLR. 2011;12:2825–30.
Joo G, Song Y, Im H, Park J. Clinical implication of machine learning in predicting the occurrence of cardiovascular disease using big data (Nationwide Cohort Data in Korea). IEEE Access. 2020;8:157643–53.
Dimopoulos AC, Nikolaidou M, Caballero FF, Engchuan W, SanchezNiubo A, Arndt H, et al. Machine learning methodologies versus cardiovascular risk scores, in predicting disease risk. BMC Med Res Methodol. 2018;18(1):179.
Panagiotakos DB, Fitzgerald AP, Pitsavos C, Pipilis A, Graham I, Stefanadis C. Statistical modelling of 10year fatal cardiovascular disease risk in Greece: the HellenicSCORE (a calibration of the ESC SCORE project). Hellenic J Cardiol. 2007;48(2):55–63.
Khera R, Haimovich J, Hurley NC, McNamara R, Spertus JA, Desai N. Use of Machine Learning Models to Predict Death After Acute Myocardial Infarction. JAMA Cardiol. 2021;6:633–41.
Mm E, Am N. MJ P: Incremental Benefits of Machine Learning—When Do We Need a Better Mousetrap? JAMA Cardiology. 2021;6:621–3.
Zhao J, Feng Q, Wu P, Lupu RA, Wilke RA, Wells QS, Denny JC, Wei WQ. Learning from Longitudinal Data in Electronic Health Record and Genetic Data to Improve Cardiovascular Event Prediction. Sci Rep. 2019;9(1):717.
Dolezalova N, Reed AB, Despotovic A, Obika BD, Morelli D, Aral M, Plans D. Development of an accessible 10year Digital CArdioVAscular (DiCAVA) risk assessment: a UK Biobank study. European Heart JournalDigital Health. 2021;2(3):528–38.
Acknowledgements
We thank the editors and reviewers for their constructive comments, which have significantly improved the presentation of this manuscript.
Funding
This work was supported by NIH R01: R01HL136942 and the NIH Intramural Research Program, National Library of Medicine.
Author information
Authors and Affiliations
Contributions
YD led the study, performed all data analyses, and wrote the manuscript. LZ designed and supervised the project, and revised the manuscript. LL and HJ provided statistical expertise on data cleaning and model evaluation. YP provided deep learning expertise on tuning the deep learning models. HN made substantial contribution to the data acquisition. All the other authors read, edited, and approved the final manuscript.
Corresponding author
Ethics declarations
Ethics approval and consent to participate
All data were deidentified, and all study protocols and procedures were approved by the Institutional Review Board at Northwestern University with a waiver for informed consent. All methods were conducted in accordance with the ethical standards of the declaration of Helsinki.
All methods were carried out in accordance with relevant guidelines and regulations.
Consent for publication
N/A
Competing interests
The authors declare that they have no competing interests.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
About this article
Cite this article
Deng, Y., Liu, L., Jiang, H. et al. Comparison of StateoftheArt Neural Network Survival Models with the Pooled Cohort Equations for Cardiovascular Disease Risk Prediction. BMC Med Res Methodol 23, 22 (2023). https://doi.org/10.1186/s1287402201829w
Received:
Accepted:
Published:
DOI: https://doi.org/10.1186/s1287402201829w