IMPORTANCE Obesity increases the incidence and mortality from some types of cancer, but it remains uncertain whether intentional weight loss can decrease this risk.OBJECTIVE To investigate whether bariatric surgery is associated with lower cancer risk and mortality in patients with obesity. DESIGN, SETTING, AND PARTICIPANTSIn the SPLENDID (Surgical Procedures and Long-term Effectiveness in Neoplastic Disease Incidence and Death) matched cohort study, adult patients with a body mass index of 35 or greater who underwent bariatric surgery at a US health system between 2004 and 2017 were included. Patients who underwent bariatric surgery were matched 1:5 to patients who did not undergo surgery for their obesity, resulting in a total of 30 318 patients. Follow-up ended in February 2021.EXPOSURES Bariatric surgery (n = 5053), including Roux-en-Y gastric bypass and sleeve gastrectomy, vs nonsurgical care (n = 25 265).MAIN OUTCOMES AND MEASURES Multivariable Cox regression analysis estimated time to incident obesity-associated cancer (a composite of 13 cancer types as the primary end point) and cancer-related mortality. RESULTSThe study included 30 318 patients (median age, 46 years; median body mass index, 45; 77% female; and 73% White) with a median follow-up of 6.1 years (IQR, 3.8-8.9 years). The mean between-group difference in body weight at 10 years was 24.8 kg (95% CI, 24.6-25.1 kg) or a 19.2% (95% CI, 19.1%-19.4%) greater weight loss in the bariatric surgery group. During follow-up, 96 patients in the bariatric surgery group and 780 patients in the nonsurgical control group had an incident obesity-associated cancer (incidence rate of 3.0 events vs 4.6 events, respectively, per 1000 person-years). The cumulative incidence of the primary end point at 10 years was 2.9% (95% CI, 2.2%-3.6%) in the bariatric surgery group and 4.9% (95% CI, 4.5%-5.3%) in the nonsurgical control group (absolute risk difference, 2.0% [95% CI, 1.2%-2.7%]; adjusted hazard ratio, 0.68 [95% CI, 0.53-0.87], P = .002). Cancer-related mortality occurred in 21 patients in the bariatric surgery group and 205 patients in the nonsurgical control group (incidence rate of 0.6 events vs 1.2 events, respectively, per 1000 person-years). The cumulative incidence of cancer-related mortality at 10 years was 0.8% (95% CI, 0.4%-1.2%) in the bariatric surgery group and 1.4% (95% CI, 1.1%-1.6%) in the nonsurgical control group (absolute risk difference, 0.6% [95% CI, 0.1%-1.0%]; adjusted hazard ratio, 0.52 [95% CI, 0.31-0.88], P = .01).CONCLUSIONS AND RELEVANCE Among adults with obesity, bariatric surgery compared with no surgery was associated with a significantly lower incidence of obesity-associated cancer and cancer-related mortality.

Many institutions would like to harness their electronic health record (EHR) data for research. However, with many EHR systems, this process is remarkably difficult. We have been using our vast EHR system for research very effectively, with substantial research support and many publications. Herein we share our process and provide recommendations for others wanting to utilize their EHR data for research.

Background: Understanding the impact of the COVID-19 pandemic on healthcare workers (HCW) is crucial.Objective: Utilizing a health system COVID-19 research registry, we assessed HCW risk for COVID-19 infection, hospitalization and intensive care unit (ICU) admission.Design: Retrospective cohort study with overlap propensity score weighting.Participants: Individuals tested for SARS-CoV-2 infection in a large academic healthcare system (N=72,909) from March 8-June 9 2020 stratified by HCW and patient-facing status. Main Measures: SARS-CoV-2 test result, hospitalization, and ICU admission for COVID-19 infection. Key Results: Of 72,909 individuals tested, 9.0% (551) of 6,145 HCW tested positive for SARS-CoV-2 compared to 6.5% (4353) of 66,764 non-HCW. The HCW were younger than non-HCW (median age 39.7 vs. 57.5, p<0.001) with more females (proportion of males 21.5 vs. 44.9%, p<0.001), higher reporting of COVID-19 exposure (72 vs. 17 %, p<0.001) and fewer comorbidities. However, the overlap propensity score weighted proportions were 8.9 vs. 7.7 for HCW vs. non-HCW having a positive test with weighted odds ratio (OR) 1.17, 95% confidence interval (CI) 0.99-1.38. Among those testing positive, weighted proportions for hospitalization were 7.4 vs.15.9 for HCW vs. non-HCW with OR of 0.42 (CI 0.26-0.66) and for ICU admission: 2.2 vs.4.5 for HCW vs. non-HCW with OR of 0.48 (CI 0.20 -1.04). Those HCW identified as patient-facing compared to not had increased odds of a positive SARS-CoV-2 test (OR 1.60, CI 1.08-2.39, proportions 8.6 vs. 5.5), but no statistically significant increase in hospitalization (OR 0.88, CI 0.20-3.66, proportions 10.2 vs. 11.4) and ICU admission (OR 0.34, CI 0.01-3.97, proportions 1.8 vs. 5.2).Conclusions: In a large healthcare system, HCW had similar odds for testing SARS-CoV-2 positive, but lower odds of hospitalization compared to non-HCW. Patient-facing HCW had higher odds of a positive test. These results are key to understanding HCW risk mitigation during the COVID-19 pandemic.

Reports indicate that COVID-19 may impact pancreatic function and increase type 2 diabetes (T2D) risk, although real-world COVID-19 impacts on HbA1c and T2D are unknown. We tested whether COVID-19 increased HbA1c, risk of T2D, or diabetic ketoacidosis (DKA). We compared pre- and post-COVID-19 HbA1c, and T2D risk in a large real-world clinical cohort of 8,755 COVID-19(+) patients and 11,998 COVID-19(−) matched controls. We investigated if DKA risk was modified in COVID-19(+) patients with type 1 diabetes (T1D) (N=701) or T2D (N=21,830), or by race and sex. We observed a statistically significant, albeit clinically insignificant, HbA1c increase post-COVID-19 (all patients △HbA1c=0.06%; with T2D △HbA1c=0.1%), and no increase among COVID-19(−) patients. COVID-19(+) patients were 40% more likely to be diagnosed with T2D compared to COVID-19(−) patients and 28% more likely for the same HbA1c change as COVID-19(−) patients, indicating that COVID-19 attributed T2D risk may be due to increased recognition during COVID-19 management. DKA in COVID-19(+) patients with T1D was not increased. COVID-19(+) Black patients with T2D displayed disproportionately increased DKA risk (HR:2.46[1.48-6.09], P=0.004) compared to White patients, suggesting a need for further clinical awareness and investigation.

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Background: Functional mitral regurgitation (MR) is associated with poor outcomes and the prognostic role of cardiac magnetic resonance imaging (CMR) quantification of secondary MR has rarely been studied. We evaluated the associations between CMR-derived MR-fraction and adverse outcomes in non-ischemic cardiomyopathy (NICM) patients. Methods: We retrospectively studied 840 consecutive NICM patients undergoing CMR during 2001/4/1-2019/3/31. Multivariable Cox proportional hazards regression was performed for MR-fraction and two composite endpoints: primary (time to death, heart transplant and/or left ventricular assist device) and secondary (primary endpoint plus heart failure hospitalizations). Results: The cohort had mean age of 53.0±15.9 years, 319 (38.0) were female, and mean MR-fraction of 14±13% by CMR. There were 141 (16.8%) primary and 223 (26.5%) secondary endpoints over a mean follow-up of 3.6±3.0 years. Multivariable analyses results are shown in the Table. MR-fraction was independently associated with the primary and secondary endpoints with hazards ratios of 1.13 (95%CI 1.07-1.20) and 1.13 (95%CI 1.07-1.19) respectively per 5% increase in MR-fraction. MR-fraction provided stronger prognostic power compared to late gadolinium enhancement (LGE) in both models. Older age and diabetes were independently associated with both composite endpoints, while lower LVEF was independently associated with the secondary endpoint only. Conclusion: CMR quantification of functional MR is a powerful predictor of adverse outcomes and demonstrated stronger associations with poor prognosis compared to LGE and left ventricular remodeling in a large cohort of patients with NICM.

Background Driven by quality outcomes and economic incentives, predicting 30-day hospital readmissions remains important for healthcare systems. The Cleveland Clinic Health System (CCHS) implemented an internally validated readmission risk score in the electronic medical record (EMR). Objective We evaluated the predictive accuracy of the readmission risk score across CCHS hospitals, across primary discharge diagnosis categories, between surgical/medical specialties, and by race and ethnicity. Design Retrospective cohort study. Participants Adult patients discharged from a CCHS hospital April 2017–September 2020. Main Measures Data was obtained from the CCHS EMR and billing databases. All patients discharged from a CCHS hospital were included except those from Oncology and Labor/Delivery, patients with hospice orders, or patients who died during admission. Discharges were categorized as surgical if from a surgical department or surgery was performed. Primary discharge diagnoses were classified per Agency for Healthcare Research and Quality Clinical Classifications Software Level 1 categories. Discrimination performance predicting 30-day readmission is reported using the c-statistic. Results The final cohort included 600,872 discharges from 11 Northeast Ohio and Florida CCHS hospitals. The readmission risk score for the cohort had a c-statistic of 0.6875 with consistent yearly performance. The c-statistic for hospital sites ranged from 0.6762, CI [0.6634, 0.6876], to 0.7023, CI [0.6903, 0.7132]. Medical and surgical discharges showed consistent performance with c-statistics of 0.6923, CI [0.6807, 0.7045], and 0.6802, CI [0.6681, 0.6925], respectively. Primary discharge diagnosis showed variation, with lower performance for congenital anomalies and neoplasms. COVID-19 had a c-statistic of 0.6387. Subgroup analyses showed c-statistics of > 0.65 across race and ethnicity categories. Conclusions The CCHS readmission risk score showed good performance across diverse hospitals, across diagnosis categories, between surgical/medical specialties, and by patient race and ethnicity categories for 3 years after implementation, including during COVID-19. Evaluating clinical decision-making tools post-implementation is crucial to determine their continued relevance, identify opportunities to improve performance, and guide their appropriate use. Supplementary Information The online version contains supplementary material available at 10.1007/s11606-021-07277-4.