Phenome-Wide Association Studies (PheWAS) investigate whether genetic polymorphisms associated with a phenotype are also associated with other diagnoses. In this study, we have developed new methods to perform a PheWAS based on ICD-10 codes and biological test results, and to use a quantitative trait as the selection criterion. We tested our approach on thiopurine S-methyltransferase (TPMT) activity in patients treated by thiopurine drugs. We developed 2 aggregation methods for the ICD-10 codes: an ICD-10 hierarchy and a mapping to existing ICD-9-CM based PheWAS codes. Eleven biological test results were also analyzed using discretization algorithms. We applied these methods in patients having a TPMT activity assessment from the clinical data warehouse of a French academic hospital between January 2000 and July 2013. Data after initiation of thiopurine treatment were analyzed and patient groups were compared according to their TPMT activity level. A total of 442 patient records were analyzed representing 10,252 ICD-10 codes and 72,711 biological test results. The results from the ICD-9-CM based PheWAS codes and ICD-10 hierarchy codes were concordant. Cross-validation with the biological test results allowed us to validate the ICD phenotypes. Iron-deficiency anemia and diabetes mellitus were associated with a very high TPMT activity (p = 0. 0004 and p = 0. 0015, respectively). We describe here an original method to perform PheWAS on a quantitative trait using both ICD-10 diagnosis codes and biological test results to identify associated phenotypes. In the field of pharmacogenomics, PheWAS allow for the identification of new subgroups of patients who require personalized clinical and therapeutic management.
We aimed to develop methods to perform a PheWAS based on the ICD-10 codes and biological test results, while using a quantitative trait as a selection criterion. We then tested our approach on a specific quantitative trait, TPMTa, in order to identify new subgroups of patients with different characteristics.
We performed an in silico retrospective case-control study using data from an academic hospital, Hôpital Européen Georges Pompidou (HEGP) in Paris, France. We extracted data from HEGP CDW, an i2b2 CDW containing more than 606,524 single patients, collected between 2000 and 2012 [20], [39]. This CDW contains routine care data divided into nine categories (208,955,369 items): demographics (age, sex, and hospital vital status), vital signs (e. g. , temperature, blood pressure, weight…), diagnoses (ICD-10), procedures (French CCAM classification), clinical data (structured questionnaires from EHR), free text reports, pathology codes (French ADICAP classification), biological test results, and Computerized Provider Order Entry (CPOE) drug prescriptions. ICD codes could not be directly used for analysis because of their fine granularity. Therefore, we developed two different aggregation methods. Groups of patients were divided according to the quantitative trait studied. (Figure 2) Then, as described by Denny et al. , for each PheWAS code, a case-control comparison was performed: (i) the case group was generated with patients having an ICD code in the range of this PheWAS code; (ii) the control group was composed of patients without any ICD code in this range; and (iii) patients with ICD codes that were too close to those of the current PheWAS code were excluded from this specific comparison. For each PheWAS code, its siblings were used as exclusion ranges. For example: for grouped codes under C15–26 (Malignant neoplasms, digestive organs), the exclusion range was from C00 to D48 (Neoplasms). We successively used the two methods of ICD code aggregation to compare the distribution of cases between the groups. For each biological test, we used thresholds to define low-value cases and high-value cases, according to the normal value range (Table 1). Because patients had more than one occurrence of each biological test, two algorithms of analyses were applied. The first was a “global approach” in which a high-value case (resp. a low-value case) was defined as the presence of at least one test result above the high threshold (resp. below the low threshold). (Figure S2) In addition, for hyperglycemia, we required two occurrences above the high threshold. (Table S1) The proportions of cases among patient groups were then compared, similar to the ICD analysis. The second method was a “frequency based approach” in which a case was defined by one encounter with at least one result either below (low-value cases) or above (high-value cases) the thresholds. (Figure S2) The proportions of encounter cases (“episodes”) per patient for a test were compared, similar to the ICD analysis. The results of the “global approach” and of the “frequency-based approach” were analyzed in view of the ICD-based findings. For biological tests significantly associated to a TPMTa group, we performed an event-free Kaplan-Meier survival analysis (i. e. low-value event or high-value event) after initiation of thiopurine therapy, excluding events occurring within the first week of treatment. Analysis was censored to 360 days after initiation of thiopurine therapy. Fisher exact test and unadjusted logistic regression were used to compare discrete variables. Continuous variables were compared using Student t-test. Log-rank test was used to evaluate survival curves. We calculated the odds ratios (OR) and confidence intervals at 95% (95%CI). q-q plots were realized to evaluate the distribution of p-values. The p-value was fixed at 0. 05. We used the False Discovery Rate (FDR) method to manage multiple testing and used the threshold of 0. 2 [44]. This study was approved by the IRB and ethics committee CPP Ile-de-France II. IRB Committee # 00001072. Study reference # CDW_2013_0002.
A total of 554 patients (TPMT cohort) underwent a TPMTa assessment. Of these patients, 460 had ICD-10 codes and at least one biological test result, and a total of 442 patients, i. e. study population, had also a notion of thiopurine treatment in their EHR. (Figure 3, Figure S6) These 442 EHRs included 10,252 ICD-10 occurrences and 72,711 results of the selected biological tests (Table 1). Of these patients, 324, representing 6,183 free-text reports, were included in the thiopurine efficacy validation analysis, after exclusion of the patients having less than two reports with a notion of thiopurine therapy and patients with an adverse effect or intolerance to thiopurines. (Figure 3) Known indications for thiopurine therapy, e. g. , Crohn' s disease (OR, 699. 6; 95%CI, 343. 7–1,600, p = 1. 73E-263) or ulcerative colitis (OR, 583. 1; 95%CI, 237. 9–1,843, p = 1. 5E-144) and their consequences were significantly associated with the TPMT cohort versus hospital population (Table S3). No patient with leukemia or an associated pathology were found in the analysis, as there is no hematologic department at HEGP. Event-free survival was evaluated for anemia and hyperglycemia. It showed that patients with vhTPMTa had a significant risk to have earlier anemia episodes than others (p = 0. 04). (Figure 7) Regarding the development of hyperglycemia, there was no difference between the groups. The efficacy analysis, based on free-text reports, showed 30. 6% (15/49) of thiopurine therapy failure in the vhTPMTa group versus 13. 1% (36/275) in the other TPMTa group (OR, 2. 91; 95%CI, 1. 33–6. 17; p = 0. 0045). After adjustment for sex and age in a logistic regression model, we found an adjusted OR of 3. 11 (95%CI, 1. 61–6. 04; p = 0. 0007).
This study demonstrates the feasibility and benefits of performing a PheWAS on a quantitative trait. Two independent approaches, based on (i) ICD codes and (ii) biological test results, were used to discover pathophysiological features potentially associated with this quantitative trait. In this manner, findings can be cross-validated: the phenotypes extracted from diagnosis codes were confirmed by the biological test results. By this way and using a quantitative trait in the context of pharmacogenomics we discovered new potential associations between TPMTa related to thiopurine treatment and clinical data. To our knowledge, this is the first PheWAS performed using data encoded with ICD-10 classification, as previously published PheWAS were based on ICD-9-CM. The consistency in the results found between the two aggregation methods –the ICD-10-based method and the mapping between ICD-9-CM and ICD-10– demonstrates the feasibility of PheWAS using ICD-10. In our study population and using the ICD code distribution described above, ICD-9-CM based PheWAS codes resulted in more informative phenotypes than the ICD-10 based. Thus, it appears that ICD code aggregation level, i. e. the number of code groups, needs to be optimized according to the size of the population. For example, in a larger population, it may be more appropriate to use a fine grained aggregation based on the 3 digit codes of ICD-10, resulting in more accurate phenotypes. The use of a CDW gives the opportunity to combine data from six heterogeneous sources: demographic data from administrative records, diagnosis codes from the billing system, biological test results, drug prescriptions from the CPOE system, free text reports, and clinical data from structured questionnaires. The clinical interpretation of patient condition by the physician, encoded with ICD codes, and the biological test results, extracted from the laboratory result server, were confronted. Drug prescriptions were extracted directly from the structured data issued by CPOE, structured questionnaires and from free-text reports. The close relationship between thiopurine drug prescriptions and TPMTa assays for therapeutic management was taken into account by incorporating temporal data for this study. Therefore, we restricted our analyses to the events following the initiation of thiopurine therapy. In addition to patient selection based on TPMTa, biological test results were employed to validate the phenotypes obtained from ICD codes analysis. Thus, we assessed the feasibility of expanding PheWAS to another type of data from the CDW. In that aim, classification algorithms were developed to transform continuous test results into discrete classes using value and frequency thresholds. Such algorithms could benefit from semantic web technologies [45], because description logic includes reasoning capabilities. First, the patient' s history was considered globally to compare the proportion of patients with an occurrence of abnormal biological test result between groups. In a second step, we analyzed the number of episodes for a specific biological abnormality, allowing us to compare event frequencies between TPMTa groups. From a clinical point of view, the analyses using ICD-9-CM- or ICD-10-based groupings and biological test results are consistent, resulting in more frequent anemia in vhTPMTa patients than in other patients. In IBD, anemia is frequently observed and has a multifactorial etiology such as chronic inflammation or iron-deficiency caused by enteric bleeding [46]. In addition, myelosuppressive drugs such as thiopurines can cause anemia [47]. In our study, the strong association between iron-deficiency anemia – observed by ICD codes and hemoglobin test results – and vhTMPTa could reflect more active disease in these patients. Moreover, evaluation of the anemia-free duration showed earlier episodes of anemia in the vhTPMTa group compared to other patients. Finally, thiopurine efficacy analysis on free-text reports showed a three times more therapy failure occurrences in the vhTPMTa group, in relation with anemia episodes and an active disease. Besides, an over-representation of diabetes mellitus, identified by ICD-9-CM and ICD-10 mapping analyses, has been observed in patients with a vhTPMTa. This result has been confirmed by glycemia test result analyses with more patients having hyperglycemia. Onset of type 2 diabetes or glucose intolerance could result from a sustained steroid therapy secondary to thiopurine resistance and active disease in vhTPMTa patients. This finding is strengthened by the weak association with secondary hypertension also known as a steroid adverse effect. Finally, the higher risk of thiopurine therapy failure in vhTPMTa patients, highlighted by free-text report analysis, is in agreement with sustained steroid therapy, according to IBD therapeutic management. Altogether, these findings suggested that patients with vhTPMTa could have more active disease than the others, leading to more frequent anemia episodes despite thiopurine therapy. These patients may benefit from more intensive thiopurine therapy to maintain remission, spare steroids and lessen common adverse effects. As a limit of our PheWAS study, the study design does not distinguish the effect of vhTPMTa itself from a drug effect. A possible approach to assess this point would be to perform a PheWAS on patients with a TPMTa assessment but without thiopurine therapy. However, according to TPMTa testing indication, i. e. before starting a thiopurine therapy to screen TPMT-deficient patients, the HEGP CDW did not contain data to process such an analysis. Systematic TPMTa determination for inpatients, in a context a large DNA biobanking could be valuable for analyzing the impact of vhTPMTa on clinical phenotypes. The number of patients in our study (n = 442) is relatively small. Previously published PheWAS were mainly based on pooled data or large population based cohorts [7], [13], [14]. However, despite the size of our study, we obtained statistically significant results and supported by a clinical/biological cross-validation. This cross-validation was followed by a manual in-depth analysis of free-text reports to evaluate the validity of our initial conclusions. Regarding multiple testing issues, Denny et al. used a Bonferonni correction but estimated that it might be too restrictive [7], [42], [48]. We decided to use FDR because of its tolerance towards auto-correlated tests [49]. Given the cross-validation process based on the biological test results: (i) we did not exclude PheWAS codes with small numbers of cases from our analysis as in previous studies; (ii) and we considered the patients who had at least one occurrence of the ICD code p as having the phenotype p, whereas previous studies considered patients as cases when the ICD code was present more than once in the patient record (a minimum of two or even four occurrences of the same code) [8], [11], [12], [16]. To be used as a selection criterion, a quantitative trait should be stable over the period of phenotype analysis. As all enzymes, TPMT can be influenced by physiological factors (e. g. , pregnancy) or co-treatments [29], [50]. In our study, TPMTa was stable over the analysis period. To extend this method to other quantitative traits, this stability over time must be checked. Regarding our ICD and biological test result analyses, it could be valuable to extend it to other retrospective cohorts or CDW. Finally, the implementation of a large prospective study, including patients treated by thiopurine according to their TPMTa, could help to confirm our findings regarding vhTPMTa and thiopurine therapy failure associated with steroid side effects, and to develop further research. We described here an original method to perform a PheWAS analysis on a quantitative trait, TPMTa, using both ICD-10 diagnosis codes and biological test results to identify associated phenotypes. This study highlighted a potential association between very high TPMT activity and signs that could be associated with a failure of thiopurine therapy and sustained steroid requirements in IBD patients. In the field of pharmacogenomics, PheWAS may allow the description of new subgroups of patients who need personalized clinical and therapeutic management.
The use of underlying molecular mechanisms and other factors to describe and classify diseases is a major challenge for future treatment strategies. New methods are needed to achieve this goal. The phenome wide association study (PheWAS) methodology was initially developed to unveil unknown associations between a specific genetic status and phenotypic features (e. g. diagnoses from electronic health records). We initially propose to extend this method to assessment of the relationships between the levels of a quantitative trait and diagnosis codes. We also assess the relationships between this quantitative trait and the biological test results. We tested this method using the levels of enzymatic activity of thiopurine S-methyltransferase (TPMT) that is involved in the metabolism of thiopurine drugs used in inflammatory bowel diseases for example. We discovered an association between a very high TPMT activity and nutritional anemia and diabetes. These results could be used to describe a new subgroup of patients in order to optimize drug treatments.
Phenome-Wide Association Studies on a Quantitative Trait: Application to TPMT Enzyme Activity and Thiopurine Therapy in Pharmacogenomics