Impact of Obesity on Outcomes of Patients With Coronavirus Disease 2019 in the United States: A Multicenter Electronic Health Records Network Study
2020; Elsevier BV; Volume: 159; Issue: 6 Linguagem: Inglês
10.1053/j.gastro.2020.08.028
ISSN1528-0012
AutoresShailendra Singh, Mohammad Bilal, Haig Pakhchanian, Rahul Raiker, Gursimran Kochhar, Christopher C. Thompson,
Tópico(s)COVID-19 and healthcare impacts
ResumoDuring the 2009 H1N1 influenza A virus pandemic, obesity was significantly associated with increased risk for hospitalization and mortality.1Morgan O.W. et al.PLoS One. 2010; 5e9694Crossref PubMed Scopus (354) Google Scholar In 2020, the coronavirus disease 2019 (COVID-19) pandemic has a higher estimated case fatality rate.2Faust J.S. del Rio C. JAMA Intern Med. 2020; 180: 1045-1046Crossref PubMed Scopus (122) Google Scholar It has hit the United States at a time when obesity has also reached epidemic status, with the prevalence of obesity increasing from 30.5% to 42.4% and of severe obesity increasing from 4.7% to 9.2% over the past decade.3Hales C. et al.NCHS Data Brief. 2020; : 1-8Google Scholar Comorbidities associated with obesity are widely recognized risk factors for poor COVID-19 outcomes4Zhou F. et al.Lancet. 2020; 395: 1054-1062Abstract Full Text Full Text PDF PubMed Scopus (18962) Google Scholar; however, larger population-based data evaluating obesity as an independent risk factor continue to be sparse. We performed a retrospective cohort study using TriNetX (Cambridge, MA), a global federated health research network that provided access to electronic medical records of patients from multiple large member health care organizations in the United States. Details of the data source are described in the Supplementary Materials. A search query was performed to identify all adult patients (≥18 years) with a diagnosis of COVID-19 between January 20, 2020, and May 31, 2020. The search criteria to identify potential patients with COVID-19 were based on specific COVID-19 diagnosis codes (Supplementary Materials) or positive laboratory confirmation of COVID-19. Identified patients with COVID-19 were stratified based on a body mass index (BMI) or a diagnosis code for obesity. Patients with a documented BMI of ≥30 kg/m2 or a diagnosis of obesity within 1 year before the diagnosis of COVID-19 were included in the obesity group. Patients with a documented BMI of <30 kg/m2 or with no documented diagnosis of obesity within the last year were included in the control group. We excluded all patients for whom BMI varied between ≥30 kg/m2 and <30 kg/m2 in the preceding year before the diagnosis of COVID-19 or for whom diagnosis of obesity was present but BMI was reported as <30 kg/m2 in the preceding year. Details of patient selection are outlined in Supplementary Figure 1. The obesity group and control groups were compared after 1:1 propensity score matching (PSM). The primary outcome was a composite of intubation or death up to 30 days after diagnosis of COVID-19. Sensitivity analysis and subgroup analysis based on the obesity class were also performed. Details of the statistical analysis, sensitivity analysis, and limitations are also provided in the Supplementary Materials. A total of 41,513 adult patients with COVID-19 from 26 health care organizations in the United States were identified. Out of these patients with COVID-19, 8,641 patients with documented BMI of ≥30 kg/m2 (n = 5,879) or diagnosis of obesity (n = 2,762) were included in the obesity group, and 31,273 patients with BMI of <30 kg/m2 (n = 6437) or without any reported diagnosis of obesity were included in the control group (Supplementary Figure 1). Sex, racial, and ethnic differences were seen between the groups, and patients in the obesity group had a significantly higher proportion of comorbidities compared to the control group (Table 1). In the crude unadjusted analysis, patients in the obesity group were more likely to have a 30-day composite outcome of death or mechanical ventilation compared to the control group (Risk Ratio [RR] 1.99; 95% confidence interval, 1.84–2.15).Table 1Characteristics and Outcomes of Patients With COVID-19 in the Obesity Group and Control Group Before and After Propensity Score MatchingCharacteristicsBefore Propensity MatchingAfter Propensity MatchingCOVID-19 with obesity (n = 8,641)COVID-19 without obesity (n = 31,273)P valueCOVID-19 with obesity (n = 8,112)COVID-19 without obesity (n = 8,112)P valueAge, y, mean ± SD49.68 ± 15.8449.87 ± 19.27.39549.47 ± 16.0750.68 ± 16.93<.001Age, y, n (%) <402,563 (29.66)11,087 (35.45)<.0012,496 (30.77)2,388 (29.44).065 40–603,596 (41.62)10,409 (33.28)<.0013,260 (40.19)3,237 (39.9).712 60–802,216 (25.65)6,986 (22.34)<.0012,090 (25.76)2,217 (27.33).024 >80266 (3.08)2,791 (8.93)<.001266 (3.28)270 (3.33).861Female, n (%)5,374 (62.19)16,469 (52.66)<.0014,963 (61.18)4,929 (60.76).584Race, n (%) White3,901 (45.15)14,302 (45.73).3323,719 (45.85)3,698 (45.59).741 Black or African American3,114 (36.04)7,534 (24.09)<.0012,818 (34.74)2,886 (35.58).264 Asian96 (1.11)1,050 (3.36)<.00196 (1.18)110 (1.36).326 Unknown race1,482 (17.15)8,202 (26.23)<.0011,434 (17.68)1,369 (16.88).177Ethnicity: Hispanic or Latino, n (%)1,308 (15.14)4,047 (12.94)<.0011,235 (15.22)1,246 (15.36).81Hypertensive disease, n (%)4,661 (53.94)8,091 (25.87)<.0014,138 (51.01)4,202 (51.80).315Disorders of lipoprotein metabolism and other lipidemias, n (%)3,453 (39.96)5,823 (18.62)<.0012,998 (36.96)3,037 (37.44).526Diabetes mellitus, n (%)2,816 (32.59)3,945 (12.62)<.0012,359 (29.08)2,379 (29.33).73Chronic lower respiratory diseases, n (%)2,606 (30.16)4,355 (13.93)<.0012,224 (27.42)2,279 (28.09).335Ischemic heart diseases, n (%)1,252 (14.49)2,488 (7.96)<.0011,092 (13.46)1,095 (13.5).945Heart failure, n (%)1,016 (11.76)1,618 (5.17)<.001844 (10.4)793 (9.78).184Pulmonary heart diseases, n (%)395 (4.57)496 (1.59)<.001290 (3.58)261 (3.22).209Cerebrovascular diseases, n (%)669 (7.74)1,768 (5.65)<.001603 (7.43)629 (7.75).441Chronic kidney disease, n (%)1,051 (12.16)2,043 (6.53)<.001913 (11.26)933 (11.5).621Fatty liver disease, n (%)599 (6.93)511 (1.63)<.001409 (5.04)386 (4.76).403Cirrhosis of liver, n (%)130 (1.5)270 (0.86)<.001106 (1.31)105 (1.29).945Malignant neoplasm of breast, n (%)143 (1.66)317 (1.01)<.001130 (1.6)138 (1.7).622Malignant neoplasms of lymphoid, hematopoietic and related tissue, n (%)140 (1.62)308 (0.99)<.001115 (1.42)125 (1.54).515Malignant neoplasms of digestive organs, n (%)112 (1.3)278 (0.89)<.00195 (1.17)94 (1.16).942Malignant neoplasm of prostate, n (%)79 (0.91)296 (0.95).783275 (0.93)80 (0.99).687Nicotine dependence, n (%)931 (10.77)1,891 (6.05)<.001817 (10.07)865 (10.66).216Anthropometric parameters (within last 1 year)aNot included in PSM.Body height, inches, mean ± SD66.08 ± 4.54 (nbNumber of patients with available data. = 7,407)66.32 ± 4.28 (nbNumber of patients with available data. = 14,376).000166.13 ± 4.49 (nbNumber of patients with available data. = 6,880)66.12 ± 4.29 (nbNumber of patients with available data. = 4,920).891Body weight, lb, mean ± SD224.72 ± 62.38 (nbNumber of patients with available data. = 6,739)171.59 ± 46.95 (nbNumber of patients with available data. = 14,892)<.001223.64 ± 61.98 (nbNumber of patients with available data. = 6,275)175.88 ± 48.73 (nbNumber of patients with available data. = 4,811)<.001BMI, kg/m2, mean ± SD37.07 ± 6.74 (nbNumber of patients with available data. = 5,879)24.62 ± 3.23 (nbNumber of patients with available data. = 6,437)<.00136.89 ± 6.58 (nbNumber of patients with available data. = 5,508)24.70 ± 3.16 (nbNumber of patients with available data. = 1,995)<.001Body surface area, m22.18 ± 0.71 (nbNumber of patients with available data. = 994)1.83 ± 0.27 (nbNumber of patients with available data. = 2,132)<.0012.17 ± 0.36 (nbNumber of patients with available data. = 922)1.86 ± 0.27 (nbNumber of patients with available data. = 750)<.001Outcomes Before PSMOutcomeCOVID-19 with obesity, % (n/total) (n = 8,641)Control group, % (n/total) (n = 31,273)Risk ratio (95% CI)P valueComposite outcome (intubation or death)10.32 (892/8,641)5.19 (1,623/31,273)1.99 (1.84–2.15)<.001Mortality4.57 (395/8,641)3.32 (1,037/31,273)1.38 (1.23–1.54)<.001Intubation8.46 (731/8,641)3.32 (1,014/31,273)2.61 (2.38–2.86)<.001Hospitalization25.66 (2,217/8,641)14.27 (4,463/31,273)1.80 (1.72–1.88)<.001Outcomes After PSMOutcomeCOVID-19 with obesity, % (n/total) (n = 8,112)Control group, % (n/total) (n = 8,112)Risk ratio (95% CI)P valueComposite outcome (intubation or death)10.15 (823/8,112)6.50 (527/8,112)1.56 (1.41–1.73)<.001Mortality4.56 (368/8,112)3.87 (314/8,112)1.17 (1.01–1.36).035Intubation8.32 (675/8,112)4.55 (369/8,112)1.83 (1.62–2.07)<.001Hospitalization25.33 (2,055/8,112)18.11 (1,469/8,112)1.40 (1.32–1.49)<.001COVID-19 with stage 2 obesity (n = 2,568)Control group (n = 2,568)Risk ratio (95% CI)P valueComposite outcome (intubation or death)10.83 (278/2,568)7.20 (185/2,568)1.50 (1.26–1.80)<.001Mortality5.45 (140/2,568)4.17 (107/2,568)1.31 (1.02–1.67).031Intubation8.18 (210/2,568)5.30 (136/2,568)1.54 (1.25–1.90)<.001Hospitalization25.94 (666/2,568)19.08 (490/2,568)1.36 (1.23–1.51)<.001OutcomeCOVID-19 with stage 3 obesity (n = 2,538)Control group (n = 2,538)Risk ratio (95% CI)P valueComposite outcome (intubation or death)13.6 (345/2,538)7.39 (187/2,538)1.85 (1.56–2.19)<.001Mortality6.22 (158/2,538)4.61 (117/2,538)1.35 (1.07–1.76).011Intubation10.95 (278/2,538)4.89 (124/2,538)2.24 (1.83–2.75)<.001Hospitalization29.55 (750/2,538)20.57 (522/2,538)1.44 (1.30–1.58)<.001a Not included in PSM.b Number of patients with available data. Open table in a new tab After PSM, a relatively balanced cohort of obese and nonobese patients were obtained (n = 8,112 patients in each group) (Table 1). The risk of composite outcome was higher in the obesity group compared to the control group (RR, 1.56; 95% confidence interval, 1.41–1.73). Kaplan-Meier survival analysis showed that the cumulative probability of being composite event-free up to 30 days remained significantly lower in the obesity group than the control group (87.7% vs 90.5%; P log rank < .0001) (Supplementary Figure 2). The risk of mortality, intubation, and hospitalization was higher in the obesity group compared to the control group in the matched cohort (Table 1). In a propensity-matched subgroup analysis based on obesity class, the risk of composite outcome and other poor outcomes was highest in patients with obesity class 3 (Table 1). The results of the sensitivity analysis confirmed the robustness of our main findings (Supplementary Materials). Our study using a large nationally representative database showed that patients with COVID-19 with any degree of obesity had a significantly higher risk of hospitalization and intubation or death compared to patients without obesity. A substantial incremental risk of intubation or death in the obesity cohort persisted even after meticulous PSM to adjust for confounding comorbidities. Patients with severe obesity were at highest risk of these poor outcomes. The COVID-19 pandemic has exposed the delivery of health care in the United States and has provoked a reckoning regarding our health care model moving forward. The US obesity epidemic has continued to grow for decades without any signs of abating. Obesity and its associated comorbidities are now a significant determinant of COVID-19 outcomes5Hajifathalian K. Kumar S. Newberry C. et al.Obesity (Silver Spring). 2020; 28: 1606-1612Crossref PubMed Scopus (141) Google Scholar in a population where more than 90 million adults have obesity and are highly susceptible. The disproportionate prevalence of obesity and associated comorbidities probably also have played a significant role in the racial and ethnic disparities seen during the COVID-19 pandemic. The obesity cohort derived from our data source showed a higher proportion of African Americans and Hispanics in the obesity group. Obesity increases the risk of poor outcomes in this vulnerable population with limited access to health care. Advanced age and male sex are major risk factors for worse prognosis and higher mortality in patients with COVID-19.6Huang C. et al.Lancet. 2020; 395: 497-506Abstract Full Text Full Text PDF PubMed Scopus (33292) Google Scholar However, a larger proportion of patients with obesity in our cohort were female, and the impact of this can be dramatic enough to shift severe COVID-19 outcomes toward female patients. Similarly, a large number of younger patients with obesity are also affected by severe COVID-19 with poor outcomes. In the United States where obesity is an epidemic, its impact is not only limited to clinical outcomes. Along with the psychosocial impacts of social distancing and quarantining that are applicable to the entire society, persons with obesity must contend with "weight stigma." Derogation of persons with obesity is not uncommon and, unfortunately, more socially acceptable than other marginalized groups.7Pearl R.L. Obesity (Silver Spring). 2020; 28: 1180-1181Crossref PubMed Scopus (99) Google Scholar These biases and behaviors are not limited to the general public, and studies have shown that many health care workers can also have negative attitudes and stereotypes about persons with obesity.8Phelan S.M. et al.Obes Rev. 2015; 16: 319-326Crossref PubMed Scopus (754) Google Scholar Our findings highlight the need for a vast improvement in the care of patients with obesity during this pandemic and moving forward. Physicians should manage patients with COVID-19 with obesity aggressively because outcomes can be significantly worse than in the general population. In the long term, to prepare for future pandemics or if COVID-19 becomes seasonal, there is also a serious need to develop and implement weight-loss strategies. There is a necessity for more health care professionals, including gastroenterologists, to play a central role in caring for patients with obesity. The authors acknowledge West Virginia Clinical and Translational Science Institute for providing us with access to and training on the TriNETX global health care network. The authors also acknowledge the TriNETX (Cambridge, MA) health care network for design assistance to complete this project. The authors acknowledge these additional contributors: Ahmad Khan, Monica Chowdhry, Sergio A. Sánchez-Luna, Arka Chatterjee, and Diogo Turiani Hourneaux de Moura. Shailendra Singh, MD (Conceptualization, investigation, methodology, manuscript writing – original draft, review & editing.); Mohammad Bilal, MD (Conceptualization, investigation, methodology, manuscript writing – original draft, review & editing); Haig Pakhchanian, BS (Conceptualization, investigation, methodology, manuscript writing – original draft, review & editing); Rahul Raiker, BS (Conceptualization, investigation, methodology, manuscript writing – original draft, review & editing); Gursimran Kochhar, MD (Conceptualization, investigation, methodology, manuscript writing – original draft, review & editing); Christopher C. Thompson, MSc, MD, FASGE, FACG, AGAF (Conceptualization, investigation, methodology, manuscript writing – original draft, review & editing). TriNetX (Cambridge, MA) is a global federated health research network providing access to the electronic health records (EHRs) of patients from 34 large member health care organizations (HCOs) in United States. COVID-19 data were incorporated in TriNetX by using specific diagnosis and terminology following the World Health Organization and Centers for Disease Control and Prevention (CDC) COVID-19 criteria. Real-time access to Health Insurance Portability and Accountability Act–compliant, deidentified, longitudinal clinical data to member HCOs is provided on a cloud-based platform. A typical HCO is a large academic health center with data coming from the majority of its affiliates. In addition to EHR data available in a structured fashion (eg, demographics, diagnoses, procedures, medications, laboratory test results, and vital signs), TriNetX can also extract facts of interest from the narrative text of clinical documents using natural language processing. Data are mapped to a standard and controlled set of clinical terminologies and transformed into a proprietary data schema. This transformation process includes an extensive data quality assessment to reject records that do not meet quality standards. TriNetX data have been granted a waiver from the Western institutional review board because TriNetX is a federated network, and only aggregate counts and statistical summaries of the deidentified information without any protected health information were received from participating HCOs. Both the patients and HCOs as data sources remain anonymous. The software checks the basic formatting to ensure, for example, that dates are appropriately represented. It enforces a list of fields that are required (eg, patient identifier) and rejects those records where the required information is missing. Referential integrity checking is done to ensure that data spanning multiple database tables can be successfully joined together. As the data are refreshed, the software monitors changes in volumes of data over time to ensure data validity. TriNetX requires at least 1 nondemographic fact for a patient to be counted in our data set. Patient records with only demographic information are not included in data sets. Demographics are coded to HL7, version 3, administrative standards. Diagnoses are represented by International Classification of Diseases, 10th Revision–Clinical Modification (ICD-10-CM) codes. If an HCO provides data in the International Classification of Diseases, Ninth Revision–Clinical Modification (ICD-9-CM), the data source uses a 9–to–10-CM mapping based on general equivalence mappings plus custom algorithms and curation to transform data from ICD-9-CM to ICD-10-CM. Diagnoses data are enriched with the Chronic Condition Indicator. Depending on the coding system used by an HCO, procedure data are coded in ICD-10 Procedure Coding System or Current Procedural Terminology (CPT). For many procedures, both ICD-10 Procedure Coding System and CPT codes are added to a query to define a cohort. Medications are represented at the level of ingredients, coded to RxNorm, and organized by National Drug File - Reference Terminology therapeutic classes. Laboratory test results, vitals, and findings are coded to Logical Observation Identifiers, Names, and Codes (LOINC). To ease finding and using common laboratory tests, LOINC codes are combined up to clinically significant levels for the most frequent laboratory tests and coded as TNX: LAB. The search was conducted following the CDC's COVID-19 coding guidance. These codes included ICD-9-CM and ICD-10-CM codes U07.1 (COVID-19, virus identified), B34.2 (Coronavirus infection, unspecified), B97.29 (Other coronavirus as the cause of diseases classified elsewhere), and J12.81 (Pneumonia due to SARS [severe acute respiratory syndrome]-associated coronavirus). Patients identified with diagnosis code 079.89 (Other specified viral infection) were excluded. Only patients diagnosed with the codes, as mentioned, between January 20, 2020, (the first confirmed case in the United States) and May 31, 2020, were included. The B97.29 code was specifically included based on the recommendation from the general guidance of the ICD-10-CM Official Coding Guidelines released by the CDC on February 20, 2020. Similarly, U07.1 is the new specific code for a confirmed diagnosis of COVID-19 with a positive COVID-19 test result starting April 1, 2020, as per the new CDC guidelines. The codes B34.2 and J12.81 were used more often before the CDC guidelines. Patients with ICD-9 code 079.89 (mapped to ICD-10 codes B34.2 and B97.2) were excluded to reduce any false positive COVID-19 patients because this ICD-9 code can still be used occasionally as catch-all code for more than 50 viral infections. In addition to the ICD codes, the following LOINC codes with positive laboratory test results were also used to identify patients with COVID-19: 94533-7 SARS coronavirus 2 N gene [Presence] in Respiratory specimen by nucleic acid amplification with probe detection OR 94534-5 SARS coronavirus 2 RdRp gene [Presence] in Respiratory specimen by NAA with probe detection OR 94505-5 SARS coronavirus 2 IgG Ab [Units/volume] in Serum or Plasma by Immunoassay OR 41458-1 SARS coronavirus RNA [Presence] in Unspecified specimen by NAA with probe detection OR 94309-2 SARS coronavirus 2 RNA [Presence] in Unspecified specimen by NAA with probe detection OR 94531-1 SARS Coronavirus 2 RNA panel—Respiratory specimen by NAA with probe detection OR 94506-3 SARS coronavirus 2 IgM Ab [Units/volume] in Serum or Plasma by Immunoassay OR 94500-6 SARS coronavirus 2 RNA [Presence] in Respiratory specimen by NAA with probe detection OR 94315-9 SARS coronavirus 2 E gene [Presence] in Unspecified specimen by NAA with probe detection. TriNetX has the capability of analyzing data based on a temporal relationship to the index event. The index event in our study was defined as the diagnosis of COVID-19. Baseline characteristics were estimated from any time before the index event. Presenting laboratory values and medications were recorded from the time of the index event up to 2 weeks before the index event. Outcomes were assessed from the index event up to 30 days after the index event. The risk for intubation (mechanical ventilation), hospitalization, and mortality after diagnosis of COVID-19 was recorded. The primary outcome was a composite of intubation or death. All statistical analyses were performed in real-time by using TriNetX. The means, standard deviations, and proportions were used to describe and compare patient characteristics. Categorical variables were compared using the Pearson chi-square test and continuous variables using an independent-samples t test. We performed a 1:1 PSM to reduce the effects of confounding. Covariates in the propensity score model included age, race, ethnicity, dyslipidemia, diabetes mellitus, chronic lower respiratory diseases (chronic obstructive pulmonary disease and asthma), ischemic heart diseases, heart failure, pulmonary heart diseases, cerebrovascular diseases, chronic kidney disease, fatty liver, cirrhosis of liver, malignant neoplasm, and nicotine use (Table 1). Logistic regression on these input matrices was used to obtain propensity scores for each patient in both cohorts. Logistic regression was performed in Python 3.6.5 (Python Software Foundation) using standard libraries numpy and sklearn. The same analyses were also performed in R 3.4.4 software (R Foundation for Statistical Computing, Vienna, Austria) to ensure the matching of outputs. After the calculation of propensity scores, matching was performed by using a greedy nearest-neighbor matching algorithm with a caliper of 0.1 pooled standard deviations. The order of the rows in the covariate matrix can affect the nearest neighbor matching; therefore, the order of the rows in the matrix was randomized to eliminate this bias. For each outcome, the risk ratio with a 95% confidence interval was calculated to compare the association of obesity with the outcome. Kaplan-Meier survival analyses were used to estimate the survival probability of composite outcome at the end of 30 days after the index event. Patients were censored when the time window ended or on the day after the last fact in their record. Hypothesis testing for Kaplan-Meier survival curves was conducted by using the log-rank test. An a priori defined 2-sided alpha of <.05 was used for statistical significance. Selection bias in the obesity group and the control group was possible. Therefore, we performed a sensitivity analysis by varying the inclusion criteria. We first included all patients with a diagnosis of obesity in their health records at any time before COVID-19 diagnosis and compared them to a cohort of patients with no record of obesity. Second, we compared patients with a diagnosis of obesity in the last 3 months and 1 month to a cohort of patients with no reported obesity. Additional sensitivity analyses included the same set of main analyses but also adjusting for medications (angiotensin-converting enzyme inhibitors or angiotensin receptor blockers) and presenting laboratory values (ferritin, C-reactive protein, and lactic acid dehydrogenase). Finally, given the possibility that poor outcomes in patients with obesity might be higher at presentation or related to late presentation and access to health care, we performed an analysis excluding the composite outcomes in the first 2 days after diagnosis. An analysis of a larger group of selected patients using diagnostic criteria of obesity as any time before the index event (after PSM: n = 9,769) showed a higher risk for composite outcomes in the obesity group (RR, 1.32; 95% confidence interval [CI], 1.20–1.46; P < .0001) compared to control individuals. Similarly, using an obesity diagnosis period of 3 months (after PSM: n = 6,780) also yielded a higher risk of composite outcome in obesity group (RR, 1.81; 95% CI, 1.62–2.02; P < .0001). Likewise, using an obesity diagnosis period of 1 month (after PSM: n = 5,825) also showed a higher risk of composite outcomes in the obesity group (RR, 2.18; 95% CI, 1.93–2.45; P < .0001). Composite outcome after adjustment for medications, angiotensin-converting enzyme inhibitors, and angiotensin receptor blockers (RR, 1.53; 95% CI, 1.38–1.70; P < .0001) or laboratory values (RR, 1.32; 95% CI, 1.19–1.46; P < .001) were similar. After excluding patients with outcomes in the first 2 days after diagnosis, the risk for composite outcomes was still higher in patients with obesity (RR, 1.54; 95% CI, 1.37–1.73; P < .0001). We acknowledge the limitations due to the retrospective nature of the study. The data derived from an EHR-based database is susceptible to errors in coding when patient information is translated into codes. However, extensive data quality assessment that includes data cleaning and quality checks minimizes the risk of data collection errors at the investigator's end. Adjustments for missing data are not currently possible on the TriNetX platform. Cases of COVID-19 could have been misdiagnosed as other cases of pneumonia or viral infections due to diagnosis or coding errors, especially early in the pandemic. We likely missed patients who were asymptomatic or had mild disease and did not seek medical attention; therefore, our cohort may represent the more the severe spectrum of COVID-19. Data on exposure history, incubation time, and dynamic changes in patients' clinical conditions could not be estimated from the EHR database. Socioeconomic and structural determinants, psychological elements, geographical factors, and health care delivery during COVID-19 could have affected the care of patients with obesity but were beyond the scope of our study. Despite these limitations, our study uses a large national database to evaluate the impact of obesity in patients with COVID-19. Given that our study population is representative of multiple centers across the United States, the results are more generalizable than single-center or regional experiences. In addition, even though our study was not randomized, we performed a robust statistical analysis using PSM. Supplementary Figure 2Kaplan-Meier survival curve showing the probability of being composite event (intubation or death)–free up at the end of 30 days after COVID-19 diagnosis.View Large Image Figure ViewerDownload Hi-res image Download (PPT)Supplementary Table 1Codes UsedVariableCoding System and CodesCodes Used for Patient Characteristics Included in the PSM RaceHL7 version 3 White2106-3 Black or African American2054-5 Unknown race2131-1 Asian2028-9EthnicityHL7 version 3 Hispanic or Latino2135-2Hypertensive diseasesICD-10 I10-I16Disorders of lipoprotein metabolism and other lipidemiasICD-10 E78Diabetes mellitusICD-10 E08-E13Chronic lower respiratory diseasesICD-10 J40-J47Chronic kidney diseaseICD-10 N18Ischemic heart diseasesICD-10 I20-I25Heart failureICD-10 I50Nicotine dependenceICD-10 F17Cerebrovascular diseasesICD-10 I60-I69Fatty (change of) liverICD-10 K76.0Cirrhosis of liverICD-10 K74.6Pulmonary heart diseasesICD-10 I27Malignant neoplasm of breastICD-10 C50Malignant neoplasms of lymphoid, hematopoietic and related tissueICD-10 C81-C96Malignant neoplasm of prostateICD-10 C61Malignant neoplasms of digestive organsICD-10 C15-C26Codes Used for Anthropometric Parameters and Obesity DiagnosisBody heightTNX 9077 (Included LOINC codes 8307-1 Body height –preoperative, 8306-3 Body height –lying, 8302-2 Height, 8301-4 Body height Estimated, 3138-5 Body height Stated, 8308-9 Body height –standing, 8305-5 Body height –postpartum, 3137-7 Body height Measured)Body weightTNX 9081 (Included LOINC codes 8335-2 Body weight Estimated, 3142-7 Body weight Stated, 3141-9 Weight, 29463-7 Body weight)Body surface areaTNX 9087 (Included LOINC codes 8277-6 Body surface area, 3139-3 Body surface area Measured, 3140-1 Body surface area)BMILOINC code 39156-5 Body mass index ICD-10 Z68ObesityICD-10 E66 (excluding ICD-10 E66.3 overweight)Codes Used to Define Outcomes of the StudyVariableCodes andMortality"Deceased" (Known deceased documented)Mechanical Ventilation"31500" (CPT: Intubation, endotracheal, emergency procedure) OR "1015098" (CPT: Ventilator management) OR "5A1935Z" (ICD-10: Respiratory Ventilation, Less than 24 Consecutive hours) OR "5A1945Z" (ICD-10: Respiratory Ventilation, 24–96 Consecutive hours) OR "5A1955Z" (ICD-10: Respiratory Ventilation, Greater than 96 Consecutive hours) OR "0BH17EZ" (ICD-10: Insertion of Endotracheal Airway into Trachea, Via Natural or Artificial Opening) OR 0BH18EZ (ICD-10: Insertion of Endotracheal Airway into Trachea, Via Natural or Artificial Opening Endoscopic) OR 0BH13EZ (ICD-10: Insertion of Endotracheal Airway into Trachea, Percutaneous Approach) OR 1022227 (CPT: Extracorporeal membrane oxygenation [ECMO]/extracorporeal life support [ECLS] provided by physician) OR 39.65 (ICD9: Extracorporeal membrane oxygenation [ECMO])Hospitalization:"1013659" (CPT: Hospital Inpatient Services) OR "1013609" (CPT: Initial Inpatient Consultation) OR "1013729" (CPT: Critical Care Services) OR "Visit: Inpatient Acute" OR "Visit: Inpatient Encounter" OR "Visit: Inpatient Non-acute" OR "Visit: Short Stay" Open table in a new tab Obesity and Mortality in COVID-19: Cause or Association?GastroenterologyVol. 164Issue 7PreviewWe read with great interest the study by Singh et al,1 in which the authors have shown the impact of obesity on the 30-day composite outcomes, death or intubation, in patients with coronavirus disease 2019 (COVID-19). An appropriate propensity score matching ensured that the 2 groups had similar confounding risk factors affecting mortality. They also demonstrated higher risks of complications with increasing grades of obesity. However, there are a few concerns that merit further exploration. Full-Text PDF
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