
==== Front
Gastro Hep Adv
Gastro Hep Adv
Gastro Hep Advances
2772-5723
Elsevier

S2772-5723(24)00096-7
10.1016/j.gastha.2024.07.001
Original Research—Clinical
Using the Electronic Health Record to Develop a Gastric Cancer Risk Prediction Model
Kim Michelle Kang kimm13@ccf.org
1∗
Rouphael Carol 1
Wehbe Sarah 1
Yoon Ji Yoon 2
Wisnivesky Juan 34
McMichael John 5
Welch Nicole 16
Dasarathy Srinivasan 16
Zabor Emily C. 7
1 Department of Gastroenterology, Hepatology, and Nutrition, Cleveland Clinic, Cleveland, Ohio
2 Division of Gastroenterology, Icahn School of Medicine at Mount Sinai, New York, New York
3 Division of General Internal Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
4 Division of Pulmonary and Critical Care Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
5 Department of Surgery, Digestive Disease and Surgery Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio
6 Department of Inflammation and Immunity, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio
7 Department of Quantitative Health Sciences, Taussig Cancer Institute, Cleveland Clinic, Cleveland, Ohio
∗ Correspondence: Address correspondence to: Michelle Kang Kim, MD, PhD, Cleveland Clinic, 9500 Euclid Avenue, A30, Cleveland, Ohio 44195. kimm13@ccf.org
14 7 2024
2024
14 7 2024
3 7 910916
28 3 2024
3 7 2024
© 2024 The Authors
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Background and Aims

Gastric cancer (GC) is a leading cause of cancer incidence and mortality globally. Population screening is limited by the low incidence and prevalence of GC in the United States. A risk prediction algorithm to identify high-risk patients allows for targeted GC screening. We aimed to determine the feasibility and performance of a logistic regression model based on electronic health records to identify individuals at high risk for noncardia gastric cancer (NCGC).

Methods

We included 614 patients who had a diagnosis of NCGC between ages 40 and 80 years and who were seen at our large tertiary medical center in multiple states between 2010 and 2021. Controls without a diagnosis of NCGC were randomly selected in a 1:10 ratio of cases to controls. Multiple imputation by chained equations for missing data followed by logistic regression on imputed datasets was used to estimate the probability of NCGC. Area under the curve and the 0.632 estimator was used as the estimate for discrimination.

Results

The 0.632 estimator value was 0.731, indicating robust model performance. Probability of NCGC was higher with increasing age (odds ratio [OR] = 1.16, 95% confidence interval [CI]: 1.04–1.3), male sex (OR = 1.97; 95% CI: 1.64–2.36), Black (OR = 3.07; 95% CI: 2.46–3.83) or Asian race (OR = 4.39; 95% CI: 2.60–7.42), tobacco use (OR = 1.61; 95% CI: 1.34–1.94), anemia (OR = 1.35; 95% CI: 1.09–1.68), and pernicious anemia (OR = 6.12, 95% CI: 3.42–10.95).

Conclusion

We demonstrate the feasibility and good performance of an electronic health record–based logistic regression model for estimating the probability of NCGC. Future studies will refine and validate this model, ultimately identifying a high-risk cohort who could be eligible for NCGC screening.

Keywords

Noncardia Gastric Cancer
Electronic Health Record
Logistic Regression Model
Screening
High-Risk Individuals
Cancer Disparity
Abbreviations used in this paper

AUC area under the receiver operator curve

BMI body mass index

CC Cleveland Clinic

EGD esophagogastroduodenoscopy

EHR electronic health record

HP Helicobacter pylori

ICD International Classification of Diseases

IQR interquartile range

MICE multiple imputation with chain equation

NCGC noncardia gastric cancer

PPV positive predictive value
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pmcIntroduction

Gastric cancer (GC) is the fifth most commonly diagnosed cancer and fourth leading cause of cancer-related death in the world, with approximately 770,000 deaths in 2020.1 It is frequently diagnosed at an advanced stage, with an overall 5-year survival rate of 31%.2,3 While GC is aggressive and associated with poor outcomes when diagnosed late, early GC can be cured with minimally invasive endoscopic resection.4,5 In countries where noncardia gastric cancer (NCGC) has a high incidence and prevalence, nationwide screening has been adopted resulting in earlier detection and improved overall outcomes.6 In the United States, a lower incidence country, there are no current screening protocols for NCGC in the average population because of limited feasibility and cost-effectiveness.7

Despite lower incidence in the United States, NCGC remains a persistent source of cancer disparity, disproportionately affecting minority race-ethnic groups and those living in poverty.8 Identifying high-risk populations who may benefit from screening is thus paramount. Known risk factors for NCGC include older age, sex, race, ethnicity, family history, smoking, and chronic Helicobacter pylori (HP) infection, variables readily available in the electronic health record (EHR).9, 10, 11, 12, 13

With adoption in the United States exceeding 81%, the EHR is a large resource of clinically relevant, real-world longitudinal data,14,15 and has emerged as a promising data source for the identification of high-risk individuals in rare diseases and for the development of risk prediction models for common medical conditions.16,17 EHR-based models have also been developed for various cancers including cancer of the breast, esophagus, and pancreatic cancer.18, 19, 20 Only one US-based study developed an EHR-based model to predict GC, but was limited to patients who had undergone esophagogastroduodenoscopy (EGD).21

In this study, we developed and assessed the performance of an EHR-based logistic regression predictive model in accurately identifying individuals at high risk for NCGC.

Methods

Data Sources, Case, and Control Selection

We conducted a retrospective case-control study using the Cleveland Clinic (CC) EHR database. The CC is one of the largest healthcare systems in the world with facilities located in multiple states including Ohio and Florida. The CC EHR includes prospectively collected data for around 12 million patients and has been available since 2010.

We identified individuals receiving care at CC between 2010 and 2021 who had a diagnosis of NCGC between the ages of 40 and 80 years through query of the EHR using International Classification of Diseases (ICD) 9 and 10 codes (C16.1–C16.9). A manual chart review was then performed to confirm accurate identification of intestinal type NCGC (adenocarcinoma); we excluded individuals with gastric neuroendocrine tumors, gastrointestinal stromal tumors, hereditary cancer syndromes, signet ring carcinoma, or linitis plastica (Figure). We also excluded cancers of the cardia as they share similar epidemiological characteristics with esophageal adenocarcinoma in the United States. Only patients with a duration of medical records of 12 months or more prior to the NCGC diagnosis were included. We randomly selected controls in a 1:10 ratio of cases to controls from approximately 4.5 million patients aged 40–80 years without a diagnosis of NCGC, who were evaluated during the same timeframe. For the NCGC cohort, age was defined at time of diagnosis; age for controls was defined at time of last encounter in the EHR.Figure Flow diagram of case inclusion and exclusion.

Model Variables

Sociodemographic and clinical data available in the EHR were included in the model, including age, sex, race, ethnicity, body mass index, and tobacco history. Clinical data as obtained by ICD-9 and ICD-10 codes included clinical characteristics and comorbidities and were obtained at a time prior to the NCGC diagnosis. We included only the variables with a high degree of presence in the EHR. Features available from EGD or pathology records were not included as the objective was to identify high-risk patients from EHR features without prior endoscopy, applicable to a broader population.

Statistical Methods

Patient characteristics were summarized using the number and percentage for binary or categorical variables and median and first and third quartiles for continuous variables, according to case or control status. Missing values in covariates included in the model were assumed to be missing at random and were imputed using multiple imputation by chained equations.22 Ten datasets were imputed based on convergence to an absolute difference in predicted risk of < 3% from 2 random seeds, and only results from the first seed were retained for analysis. The imputation model conditioned on all predictors as well as case vs control status. The logistic regression model was fit to each of the imputed datasets. Final estimates of the regression coefficients and their standard errors are obtained using Rubin’s approach23 and estimates of model performance using the guidance of Marshall et al.24

Discrimination, which is a measure of how accurately patients are classified as having NCGC or not, was assessed using the area under the receiver operator curve (AUC). The apparent AUC was obtained for each multiple imputed dataset. Next, 500 bootstrap samples were taken of each dataset to obtain the bootstrap cross-validation AUC. The apparent and bootstrap cross-validation AUCs were combined into a 0.632 estimator of discrimination. The 0.632 estimator is considered the best estimate of model performance, with the bootstrap cross-validated estimator taken as a minimum and the apparent estimator taken as a maximum, to give a range of model performance.25 Calibration was assessed visually using plots of the predicted risk against the observed proportion, as calculated based on subjects in a nearest neighborhood of the given predicted risk.

To identify an optimal threshold to be used to select patients for screening, we maximized the sensitivity for a fixed value of specificity based on the average predicted probability of GC across the multiple imputed datasets. Positive predictive value (PPV) was also calculated for different threshold points. All statistical analyses were conducted using R software version 4.3, using packages such as multiple imputation by chained equations, riskRegression, and cutpointr.

Results

Cohort Characteristics

Six hundred fourteen patients with NCGC and 6331 controls were identified based on prespecified inclusion criteria (Figure). Baseline characteristics of both NCGC cases and normal controls are listed in Table 1. The median age was 68 years (interquartile range [IQR] 59–74) for cases and 66 years (IQR 60–73) for controls. Male sex constituted a larger proportion in NCGC cases (58%) and was less common in the controls (42%). The majority of patients included in both cases and control groups were of White race (65% and 83%, respectively); however, there were more Black and Asian NCGC cases (28% and 3.6%, respectively) as compared to controls (14% and 1.3%, respectively). On average, approximately 6% of both NCGC cases and controls were Hispanic.Table 1 Clinical Characteristics of Cases and Controls

Characteristic	Case, N = 614a	Control, N = 6331a	
Age (median, IQR)	68 (59, 74)	66 (60, 73)	
Sex			
 Female	256 (42%)	3653 (58%)	
 Male	358 (58%)	2678 (42%)	
Race			
 White	383 (65%)	5048 (83%)	
 Black	165 (28%)	844 (14%)	
 Asian	21 (3.6%)	80 (1.3%)	
 Other	22 (3.7%)	123 (2.0%)	
 Unknown	23	236	
Ethnicity			
 Not Hispanic	519 (93%)	5512 (95%)	
 Hispanic	38 (6.8%)	295 (5.1%)	
 Unknown	57	524	
Average body mass index (BMI) (median, IQR)	28 (24, 32)	29 (25, 33)	
 Unknown	166	275	
Smoking status			
 Never	235 (39%)	3183 (52%)	
 Ever	360 (61%)	2925 (48%)	
 Unknown	19	223	
Dyspepsia	20 (3.3%)	322 (5.1%)	
Heartburn	9 (1.5%)	191 (3.0%)	
Reflux	107 (17%)	2108 (33%)	
Bloating	34 (5.5%)	559 (8.8%)	
Anemia	156 (25%)	1617 (26%)	
Pernicious anemia	19 (3.1%)	55 (0.9%)	
Combined variable immunodeficiency	1 (0.2%)	11 (0.2%)	
Gastroesophageal reflux disease	113 (18%)	2210 (35%)	
Diabetes	127 (21%)	1631 (26%)	
Hypertension	269 (44%)	3709 (59%)	
Hypercholesterolemia	237 (39%)	4078 (64%)	
Coronary artery disease	99 (16%)	1147 (18%)	
Viral hepatitis	12 (2.0%)	146 (2.3%)	
Liver disease	21 (3.4%)	482 (7.6%)	
IQR, interquartile range.

a n (%).

Body mass index was comparable between the 2 groups with a median of 28 kg/m2 (IQR 24–32) for the NCGC cohort and 29 kg/m2 (IQR 25–33) for the control cohort. The NCGC cohort had a higher proportion of ever smoking (61%) compared to the controls (48%). Dyspepsia, heartburn, and reflux occurred less frequently in the NCGC compared to the control cohort. Similarly, comorbidities including gastroesophageal reflux disease, diabetes, hypertension, hypercholesterolemia, viral hepatitis, and liver disease were all less commonly present in NCGC patients. Anemia was comparable between both groups; pernicious anemia was more common in NCGC cases.

Variable Associations

Results of a multivariable logistic regression analysis on the multiple imputed dataset are shown in Table 2. The strongest predictors of NCGC were age, male sex, Black or Asian race, ever tobacco use, anemia, and pernicious anemia. Age (per 10-year increase) was significantly associated with increased odds of NCGC (odds ratio [OR] = 1.16; 95% confidence interval [CI]: 1.04–1.30). Patients who were male (OR = 1.97; 95% CI: 1.64–2.36), Black (OR = 3.07; 95% CI: 2.46–3.83), or Asian (OR = 4.39; 95% CI: 2.60–7.42) had increased odds of NCGC. Furthermore, patients who had ever used tobacco (OR = 1.61; 95% CI: 1.34–1.94), had anemia (OR = 1.35; 95% CI: 1.09–1.68), or had pernicious anemia (OR = 6.12; 95% CI: 3.42–10.95) had increased odds of NCGC. In contrast, patients with hypertension (OR = 0.7; 95% CI: 0.56–0.87), hypercholesterolemia (OR = 0.38; 95% CI: 0.30–0.47), or liver disease (OR = 0.53; 95% CI: 0.33–0.85) had decreased odds of NCGC.Table 2 Multivariable Model

Feature	OR	LCI	UCI	P value	
Age	1.16	1.04	1.30	.008	
Male sex	1.97	1.64	2.36	<.0001	
Race (Black)	3.07	2.46	3.83	<.0001	
Race (Asian)	4.39	2.60	7.42	<.0001	
Race (other)	2.10	1.23	3.58	.007	
Ethnicity (Hispanic)	1.48	0.99	2.22	.059	
Average body mass index (BMI)	0.93	0.85	1.01	.074	
Ever smoker	1.61	1.34	1.94	<.0001	
Dyspepsia	0.89	0.54	1.46	.653	
Heartburn	0.79	0.39	1.60	.514	
Reflux	1.00	0.41	2.40	.995	
Bloating	0.94	0.63	1.38	.736	
Anemia	1.35	1.09	1.68	.007	
Pernicious anemia	6.12	3.42	10.95	<.0001	
Combined variable immunodeficiency	1.13	0.14	9.25	.909	
Gastroesophageal reflux disease	0.58	0.25	1.38	.220	
Diabetes	1.01	0.80	1.29	.927	
Hypertension	0.70	0.56	0.87	.001	
Hypercholesterolemia	0.38	0.30	0.47	<.0001	
Coronary artery disease	1.17	0.90	1.51	.240	
Viral hepatitis	0.77	0.41	1.47	.433	
Liver disease	0.53	0.33	0.85	.009	
Pooled results from logistic regression on multiply imputed data.

LCI, lower confidence interval; OR, odds ratio; UCI, upper confidence interval.

Model Performance Characteristics

The median apparent AUC across the 10 multiple imputed datasets was 0.74. The median bootstrap cross-validation AUC across the same datasets was 0.725. The median 0.632 estimator across the 10 multiple imputed datasets, which was taken as our best estimate of the AUC, was 0.731. In addition, the apparent calibration used was robust across every multiple imputed dataset (Figure A1).

Model performance characteristics including sensitivity, specificity, and PPV at different threshold points are shown in Table 3. Adjusting the threshold affected the balance between sensitivity and specificity. Setting a higher threshold lowered sensitivity while improving specificity and PPV, and vice versa. For a threshold of 0.028, sensitivity was 95.6% with a specificity of 20.1% and a PPV of 0.30%. As threshold increased to 0.172, sensitivity decreased to 39.6%, while specificity increased to 90.2% and PPV to 1.0%, approaching the desired value for screening tests.Table 3 Impact of Threshold Values on Model Sensitivity, Specificity, and Positive Predictive Value

Threshold	Sensitivity (in %)	Specificity (in %)	Positive predictive value (in %)	
0.028	95.6	20.1	0.30	
0.037	92.2	30.0	0.33	
0.060	80.6	50.0	0.40	
0.121	53.4	80.0	0.67	
0.172	39.6	90.2	1.00	

Discussion

Using a real-world, multistate, large clinical dataset, we developed a multivariable logistic regression model to predict NCGC risk. With a relatively simple model including demographic, behavioral, and clinical features, and without the need for a prior endoscopy, we were able to predict NCGC with good accuracy (AUC 0.73). Importantly, using simple and readily available variables extracted from the EHR, at 0.172 threshold, the model reached a specificity of 90% and PPV of 1%, demonstrating the potential for providing individual-level risk prediction for screening programs.

Our results demonstrate the feasibility of an EHR-based logistic regression model for efficient NCGC prediction in general clinical practice and are a proof of concept of a low-cost and efficient methodology to identify patients at highest risk that may benefit from cancer mitigation strategies such as endoscopic screening. Although previous studies have developed risk prediction models for gastric intestinal metaplasia, no previous study has developed an EHR-based model that can be applied to a general US adult population to identify high-risk candidates appropriate for NCGC screening.26,27 The majority of the published risk prediction models are from Asia, with more homogenous populations compared to the United States.28,29,30 In addition, many of these models included biomarker (eg, pepsinogen) and pathology information from endoscopies (eg, intestinal metaplasia), in addition to established risk factors, limiting generalizability to the United States, where biomarkers are not readily available, and mass EGD screening is not performed. Huang et al. developed a GC risk prediction model using United States–based data. However, these models included endoscopic and pathologic variables, limiting its ability to be used in the population at large.21

While the United States is a lower-incidence country for GC, outcomes are especially poor, contrasting sharply high-incidence countries in the East (Japan, Korea), which have implemented screening programs. In the United States, GC encompasses a major disparity in cancer incidence and mortality. GC incidence is approximately double in non-White groups, and certain Asian subgroups demonstrate up to 14-fold risk compared to non-Hispanic White groups.31 Indeed, GC is the foremost and second-leading cancer for disparity in mortality in Hispanic and Black groups, respectively, and these disparities have persisted.8,32 Greater attention to GC mitigation is needed in light of these disparities and projected growth in high-risk, vulnerable populations.33 While endoscopic screening in the United States is unfeasible and of questionable benefit for the general population, targeting of high-risk groups is supported by modeling studies.34,35

Our findings regarding demographic and behavioral characteristics as well as medical diagnoses align with risk factors identified from existing risk prediction models from several countries.28, 29, 30,36 Increasing age and male sex are well-established risk factors for NCGC, as are Black and Asian race.8,37 Between 2010 and 2014 in the United States, Asians/Pacific Islanders males and Black individuals exhibited the highest rates of GC incidence when compared to other racial and gender groups.38 Individuals with a history of tobacco use had increased odds of NCGC, consistent with prior reports of elevated risk for NCGC across various ethnic groups,39 and tend to be higher among those with prolonged usage and higher consumption of tobacco.11 In addition, patients with anemia or pernicious anemia had increased odds of NCGC. Retrospective studies report the presence of anemia and pernicious anemia in NCGC patients,40 and prospective studies41 as well as systematic reviews42 show higher incidence rates of GC in people with pernicious anemia compared to the general population.

Our study has multiple strengths. Our approach of using the CC EHR leverages the immense longitudinal data available in a large EHR encompassing data of diverse populations from diverse geographic locations. We confirmed the accuracy of the diagnosis of NCGC with multiple measures including cross-referencing of EGD reports and manual chart review. We limited tertiary referral center bias by including only those patients with > 12 months of data. In addition, our study included only NCGC, which is significant as cardia cancer is widely acknowledged to be biologically similar to esophageal cancer.43 The comprehensive EHR data used in our analyses have the potential beyond standard clinical characteristics for more detailed risk stratification at point of care. For instance, individuals living in poverty and immigrants from high-incidence countries are additional important subgroups at risk identified by epidemiological studies that would not easily be incorporated into conventional risk stratification algorithms.44,45,32

There are several limitations to our study. In any EHR, missing data are a frequent and important challenge to overcome.46 To overcome this, we included only those variables with a high degree of presence; we also used multiple imputation and sensitivity analyses to confirm the robustness of our results. These techniques were able to account for missing data in multiple variables including smoking. Clinical variables were considered present if their corresponding ICD-9/ICD-10 codes were documented in their patients’ records, and the absence of the ICD-9/ICD-10 codes was interpreted as absence of the respective condition. However, we were unable to assess for 2 important factors: HP status and alcohol use, both established risk factors associated with increased risk of developing NCGC, but poorly reported in the EHR.47, 48, 49, 50 As our aim was to develop an EHR risk prediction model that can be applied to the general population, the solid performance of the model despite the lack of HP data ensures in fact that the model can be more widely applied. Another limitation is that we did not conduct external validation of our novel EHR-based model in NCGC screening.51

Conclusion

In summary, we demonstrate the feasibility of an EHR-based logistic regression model in accurately predicting the probability of NCGC. Our model included easily attainable demographic, behavioral, and medical history using ICD codes. While different thresholds of the model achieved a range of sensitivity and specificity, the most specific model was able to achieve a PPV approaching 1%. Future studies are needed to validate and refine this model.

Supplementary Materials

Figure A1 Apparent Calibration for Each Multiply Imputed Dataset.

Authors' Contributions:

Michelle Kang Kim: Conception and design of the study, interpretation of data, drafting and revision of the manuscript, and approval of final version of the manuscript. Carol Rouphael: Assembly and interpretation of data, drafting and revision of manuscript, and approval of the final version of the manuscript. Sarah Wehbe: Assembly and interpretation of data, drafting and revision of manuscript, and approval of final version of the manuscript. Ji Yoon Yoon: Drafting and revision of manuscript. Juan Wisnivesky: Drafting and revision of manuscript. John McMichael: Generation, collection, and assembly of the data. Nicole Welch: Drafting and revision of manuscript. Srinivasan Dasarathy: Conception and design of the study, drafting and revision of manuscript, and approval of the final version of the manuscript. Emily C. Zabor: Conception and design of the study, analysis and interpretation of data, and drafting and revision of the manuscript.

Conflicts of Interest: These authors disclose the following: Juan Wisnivesky consults for PPD, Banook, and Sanofi and received grants from Sanofi, Regeneron, and Axella. Nicole Welch is partially supported by NIH K08 AA028794. Srinivasan Dasarathy is partially supported by R01 GM119174; R01 DK113196; P50 AA024333; R01 AA021890; 3U01AA026976 – 03S1; U01 AA 026976; R56HL141744; U01 DK061732. The remaining authors disclose no conflicts.

Funding: The authors report no funding.

Ethical Statement: This research received Institutional Review Board approval #23–151 at the Cleveland Clinic.

Data Transparency Statement: Data and study materials can be made available upon specific request.

Reporting Guidelines: STROBE and SAGER guidelines.

Material associated with this article can be found, in the online version, at https://doi.org/10.1016/j.gastha.2024.07.001.
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