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INTRODUCTION: Complications are often under-reported at surgical morbidity and mortality (M&M) conferences due to the sole reliance on voluntary case submission. While most institutions have databases used for targeted initiatives in quality improvement, these are not routinely used for M&M. We aimed to increase case capture for M&M conferences by developing a novel system that augments the existing case submission system with cases representing complications from quality improvement databases and the electronic health record (EHR). METHODS: We developed and implemented a novel system for increasing the capture rate of complications for M&M conferences by developing custom software that combines data from the following sources: an existing voluntary case submission system for M&M, local quality databases-National Surgical Quality Improvement Program and Vizient, and an EHR-based case capture tool. We evaluated this system on a retrospective cohort of all postoperative complications at a single center in a 32-mo period and in a prospective cohort over a 4-mo period after system implementation. RESULTS: In the retrospective cohort, we identified 433 complications among all data sources. Inclusion of the new system introduced 280 new potential cases for M&M review over the 32-mo period. After implementation, the system provided 31% of cases presented at M&M conference that would have otherwise been omitted. CONCLUSIONS: A novel system that includes complications identified in the EHR and quality improvement databases increased the case capture volume for surgical M&M conference, which provides an objective case referral system that can identify complementary quality improvement opportunities.
Subject(s)
Postoperative Complications , Quality Improvement , Humans , Morbidity , Mortality , Postoperative Complications/epidemiology , Postoperative Complications/etiology , Prospective Studies , Retrospective StudiesABSTRACT
BACKGROUND: The extent to which obesity and genetics determine postoperative complications is incompletely understood. METHODS: We performed a retrospective study using two population cohorts with electronic health record (EHR) data. The first included 736,726 adults with body mass index (BMI) recorded between 1990 and 2017 at Vanderbilt University Medical Center. The second cohort consisted of 65,174 individuals from 12 institutions contributing EHR and genome-wide genotyping data to the Electronic Medical Records and Genomics (eMERGE) Network. Pairwise logistic regression analyses were used to measure the association of BMI categories with postoperative complications derived from International Classification of Disease-9 codes, including postoperative infection, incisional hernia, and intestinal obstruction. A genetic risk score was constructed from 97 obesity-risk single-nucleotide polymorphisms for a Mendelian randomization study to determine the association of genetic risk of obesity on postoperative complications. Logistic regression analyses were adjusted for sex, age, site, and race/principal components. RESULTS: Individuals with overweight or obese BMI (≥25 kg/m2) had increased risk of incisional hernia (odds ratio [OR] 1.7-5.5, p < 3.1 × 10-20), and people with obesity (BMI ≥ 30 kg/m2) had increased risk of postoperative infection (OR 1.2-2.3, p < 2.5 × 10-5). In the eMERGE cohort, genetically predicted BMI was associated with incisional hernia (OR 2.1 [95% CI 1.8-2.5], p = 1.4 × 10-6) and postoperative infection (OR 1.6 [95% CI 1.4-1.9], p = 3.1 × 10-6). Association findings were similar after limitation of the cohorts to those who underwent abdominal procedures. CONCLUSIONS: Clinical and Mendelian randomization studies suggest that obesity, as measured by BMI, is associated with the development of postoperative incisional hernia and infection.
Subject(s)
Mendelian Randomization Analysis/methods , Obesity/complications , Postoperative Complications/genetics , Adult , Body Mass Index , Female , Humans , Logistic Models , Male , Middle Aged , Polymorphism, Single Nucleotide , Postoperative Complications/etiology , Retrospective Studies , Risk FactorsABSTRACT
BACKGROUND: Patients listed for solid organ transplants (LSOTP) are at high risk for severe COVID-19 outcomes. Despite national guidelines recommending COVID-19 vaccination for LSOTP, vaccine hesitancy and underuse are reported in this population; however, reasons for this finding have not been examined thoroughly. METHODS: This single-center retrospective survey analysis aimed to characterize reasons for COVID-19 vaccine hesitancy among 110 heart, liver, and kidney patients LSOTP who had not received all the recommended vaccine doses at the time of the study. Survey questions also investigated experiences with influenza vaccination. RESULTS: Fifty-four patients (49.1%) responded to the telephone survey. The most common reasons for vaccine hesitancy were perceived lack of research in vaccine development (31%), fear of vaccine-related side effects (22%), and belief that the vaccine was unnecessary (20%). Of the respondents, 35% reported changing their vaccine perception after being listed for a transplant, most commonly attributing this to a perception that the COVID-19 vaccine is not safe for transplant recipients (32%). Gender differences in hesitancy reasons were observed, with males more likely to delay vaccination until after transplantation, although this difference was not significant (P = .07). Despite these findings, 54% of all respondents reported receiving annual influenza vaccines consistently. CONCLUSION: Despite their risk, patients LSOTP show significant hesitancy toward COVID-19 vaccines owing to perceived safety and necessity issues. The results of this study can inform targeted educational efforts to address and rectify misconceptions and concerns about COVID-19 vaccination among patients LSOTP. Future studies focused on larger, more diverse cohorts are needed to expand our understanding of and address vaccination hesitancy among this vulnerable patient population.
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BACKGROUND: Adherence to opioid prescribing protocols after operations remains challenging despite published guidelines. Integration of these guidelines with the electronic health record could potentially improve their adoption. We hypothesize that implementing an electronic health record order set containing prepopulated tablet quantities tailored to surgical procedures based on published guidelines will decrease postoperative opioid prescription. METHODS: We conducted a 12-month prepost intervention study on adult patients who underwent appendectomy, cholecystectomy, inguinal or umbilical hernia repair, thyroidectomy, or parathyroidectomy at a single institution. An electronic health record order set was developed with prepopulated opioid tablet quantities reflecting the upper limit of published recommendations. The primary endpoint was change in morphine milligram equivalent prescribed postintervention and was analyzed using linear regression adjusting for age, race, procedure, and prescriber training level. Secondary endpoints were emergency department visits for pain-related issues and opioid refill rates. RESULTS: We identified 524 patients (mean age = 53, 51% male) in our baseline cohort and 433 patients (mean age = 52, 58% male) in our postintervention group. The mean morphine milligram equivalent prescribed was 62.6 and 50.4 for the preintervention and postintervention cohorts, respectively (P = .049). Thyroidectomies and parathyroidectomies achieved the largest decrease after intervention, which decreased to 42.6 morphine milligram equivalent from 79.7 morphine milligram equivalent preintervention (P < .001). Refill rate was 1.6% postintervention compared to 3.1% preintervention (P = .20), and emergency department visit for pain control rate was 0.2% post intervention and 2.5% preintervention (P = .005). CONCLUSION: An electronic health record tailored order set based on prescription guidelines is a safe, effective, and scalable intervention for decreasing opioid prescriptions after operations.
Subject(s)
Analgesics, Opioid , Pain, Postoperative , Adult , Analgesics, Opioid/therapeutic use , Drug Prescriptions , Electronic Health Records , Female , Humans , Male , Middle Aged , Morphine Derivatives/therapeutic use , Pain, Postoperative/drug therapy , Pain, Postoperative/prevention & control , Practice Patterns, Physicians' , Retrospective Studies , Tablets/therapeutic useABSTRACT
INTRODUCTION: Despite adoption of the emergency general surgery (EGS) service by hospitals nationally, quality improvement (QI) and research for this patient population are challenging because of the lack of population-specific registries. Past efforts have been limited by difficulties in identifying EGS patients within institutions and labor-intensive approaches to data capture. Thus, we created an automated electronic health record (EHR)-linked registry for EGS. METHODS: We built a registry within the Epic EHR at University of California San Diego for the EGS service. Existing EHR labels that identified patients seen by the EGS team were used to create our automated inclusion rules. Registry validation was performed using a retrospective cohort of EGS patients in a 30-month period and a 1-month prospective cohort. We created quality metrics that are updated and reported back to clinical teams in real time and obtained aggregate data to identify QI and research opportunities. A key metric tracked is clinic schedule rate, as we care that discontinuity postdischarge for the EGS population remains a challenge. RESULTS: Our registry captured 1,992 patient encounters with 1,717 unique patients in the 30-month period. It had a false-positive EGS detection rate of 1.8%. In our 1-month prospective cohort, it had a false-positive EGS detection rate of 0% and sensitivity of 85%. For quality metrics analysis, we found that EGS patients who were seen as consults had significantly lower clinic schedule rates on discharge compared with those who were admitted to the EGS service (85% vs. 60.7%, p < 0.001). CONCLUSION: An EHR-linked EGS registry can reliably conduct capture data automatically and support QI and research. LEVEL OF EVIDENCE: Prognostic and epidemiological, level III.
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Electronic Health Records , General Surgery , Aftercare , Emergency Service, Hospital , Humans , Patient Discharge , Prospective Studies , Registries , Retrospective StudiesABSTRACT
INTRODUCTION: Patient outcome prediction models are underused in clinical practice because of lack of integration with real-time patient data. The electronic health record (EHR) has the ability to use machine learning (ML) to develop predictive models. While an EHR ML model has been developed to predict clinical deterioration, it has yet to be validated for use in trauma. We hypothesized that the Epic Deterioration Index (EDI) would predict mortality and unplanned intensive care unit (ICU) admission in trauma patients. METHODS: A retrospective analysis of a trauma registry was used to identify patients admitted to a level 1 trauma center for >24 hours from October 2019 to July 2020. We evaluated the performance of the EDI, which is constructed from 125 objective patient measures within the EHR, in predicting mortality and unplanned ICU admissions. We performed a 5 to 1 match on age because it is a major component of EDI, then examined the area under the receiver operating characteristic curve (AUROC), and benchmarked it against Injury Severity Score (ISS) and new injury severity score (NISS). RESULTS: The study cohort consisted of 1,325 patients admitted with a mean age of 52.5 years and 91% following blunt injury. The in-hospital mortality rate was 2%, and unplanned ICU admission rate was 2.6%. In predicting mortality, the maximum EDI within 24 hours of admission had an AUROC of 0.98 compared with 0.89 of ISS and 0.91 of NISS. For unplanned ICU admission, the EDI slope within 24 hours of ICU admission had a modest performance with an AUROC of 0.66. CONCLUSION: Epic Deterioration Index appears to perform strongly in predicting in-patient mortality similarly to ISS and NISS. In addition, it can be used to predict unplanned ICU admissions. This study helps validate the use of this real-time EHR ML-based tool, suggesting that EDI should be incorporated into the daily care of trauma patients. LEVEL OF EVIDENCE: Prognostic, level III.
Subject(s)
Critical Care , Electronic Health Records/statistics & numerical data , Machine Learning , Wounds and Injuries , Critical Care/methods , Critical Care/statistics & numerical data , Female , Hospital Mortality , Humans , Injury Severity Score , Male , Middle Aged , Predictive Value of Tests , Prognosis , Registries/statistics & numerical data , Risk Assessment/methods , Risk Assessment/statistics & numerical data , United States/epidemiology , Wounds and Injuries/diagnosis , Wounds and Injuries/mortality , Wounds and Injuries/therapyABSTRACT
OBJECTIVE: High BMI is associated with many comorbidities and mortality. This study aimed to elucidate the overall clinical risk of obesity using a genome- and phenome-wide approach. METHODS: This study performed a phenome-wide association study of BMI using a clinical cohort of 736,726 adults. This was followed by genetic association studies using two separate cohorts: one consisting of 65,174 adults in the Electronic Medical Records and Genomics (eMERGE) Network and another with 405,432 participants in the UK Biobank. RESULTS: Class 3 obesity was associated with 433 phenotypes, representing 59.3% of all billing codes in individuals with severe obesity. A genome-wide polygenic risk score for BMI, accounting for 7.5% of variance in BMI, was associated with 296 clinical diseases, including strong associations with type 2 diabetes, sleep apnea, hypertension, and chronic liver disease. In all three cohorts, 199 phenotypes were associated with class 3 obesity and polygenic risk for obesity, including novel associations such as increased risk of renal failure, venous insufficiency, and gastroesophageal reflux. CONCLUSIONS: This combined genomic and phenomic systematic approach demonstrated that obesity has a strong genetic predisposition and is associated with a considerable burden of disease across all disease classes.
Subject(s)
Diabetes Mellitus, Type 2 , Phenomics , Humans , Electronic Health Records , Genome-Wide Association Study , Diabetes Mellitus, Type 2/epidemiology , Diabetes Mellitus, Type 2/genetics , Polymorphism, Single Nucleotide , Genomics , Genetic Predisposition to Disease , Obesity/epidemiology , Obesity/genetics , Phenotype , Cost of IllnessABSTRACT
Brain-derived neurotrophic factor (BDNF) plays a key role in energy balance. In population studies, SNPs of the BDNF locus have been linked to obesity, but the mechanism by which these variants cause weight gain is unknown. Here, we examined human hypothalamic BDNF expression in association with 44 BDNF SNPs. We observed that the minor C allele of rs12291063 is associated with lower human ventromedial hypothalamic BDNF expression (p < 0.001) and greater adiposity in both adult and pediatric cohorts (p values < 0.05). We further demonstrated that the major T allele for rs12291063 possesses a binding capacity for the transcriptional regulator, heterogeneous nuclear ribonucleoprotein D0B, knockdown of which disrupts transactivation by the T allele. Binding and transactivation functions are both disrupted by substituting C for T. These findings provide a rationale for BDNF augmentation as a targeted treatment for obesity in individuals who have the rs12291063 CC genotype.