Shiying Hao

Background: Early identification and treatment of sexually transmitted infections (STIs) is critical to improve patient outcomes. Barriers to healthcare seeking are potentially exacerbated by COVID-19. This study examined trends in STI testing and positivity from 2019 to 2021 in primary care in the United States. Methods: This is a retrospective study using the PRIME Registry, a national primary care EHR registry, from January 1, 2019-December 31, 2021. We calculated age-standardized monthly and annual testing rates for chlamydia, gonorrhea, syphilis, and human immunodeficiency virus stratified by gender and race/ethnicity. We also generated quarterly and annual rates for test positivity. Chi-square tests and 95% confidence intervals were used for comparison. 753 practices and 4,410,609 patients were included, with 180,558 having STI tests. Results: We observed a substantial decline in testing rates for STIs from March-April 2020 (31% for chlamydia, 30% for gonorrhea, 23% for syphilis, 24% for HIV), followed by a rapid increase in May-June 2020 (64% for chlamydia, 65% for gonorrhea, 32% for syphilis, 48% for HIV). Testing rates per 100,000 decreased from 2019 to 2021 for chlamydia (3,592 vs 2,355 vs 2,181) while increased for gonorrhea in 2020 (2,129 vs 2,207 vs 2,057). STI testing rates from 2019 to 2021 for females and non-Hispanic Black or African American patients were higher than other groups. An increase in test positivity from 2019 to 2021 was observed for gonorrhea (0.4% vs 0.4% vs 0.5%) but no significant change for chlamydia (1.5% vs 1.6% vs 1.5%). Conclusion: Testing rates for STIs substantially dropped during stay-at-home orders early in the pandemic and recovered after these were relaxed. Gender and race/ethnicity STI testing differences may reflect primary care’s prioritization of higher risk populations. This study emphasizes the role of primary care EHR data in monitoring and an opportunity for closer collaboration with public health agencies.

PURPOSE Chlamydia and gonorrhea are the 2 most common bacterial sexually transmitted infections in the United States. Nonadherence to the Centers for Disease Control and Prevention treatment guidelines remains a concern. We examined how well chlamydia and gonorrhea treatment in primary care settings adhered to guidelines. METHODS We used electronic health records from the PRIME registry to identify patients with diagnosis codes or positive test results for chlamydia and/or gonorrhea from 2018 to 2022. Outcomes were the first dates of antibiotic administered within 30 days after a positive test result for the infection. Descriptive statistics were calculated for patient sociodemographic characteristics. We used a multivariate parametric accelerated failure time analysis with shared frailty modeling to assess associations between these characteristics and time to treatment. RESULTS We identified 6,678 cases of chlamydia confirmed by a positive test and 2,206 cases of gonorrhea confirmed by a positive test; 75.3% and 69.6% of these cases, respectively, were treated. Females, individuals aged 10-29 years, suburban dwellers, and patients with chlamydia-gonorrhea coinfection had higher treatment rates than comparator groups. Chlamydia was infrequently treated with the recommended antibiotic, doxycycline (14.0% of cases), and gonorrhea was infrequently treated with the recommended antibiotic, ceftriaxone (38.7% of cases). Time to treatment of chlamydia was longer for patients aged 50-59 years (time ratio relative to those aged 20-29 years = 1.61; 95% CI, 1.12-2.30) and for non-Hispanic Black patients (time ratio relative to White patients = 1.17; 95% CI, 1.04-1.33). CONCLUSIONS Guideline adherence remains suboptimal for chlamydia and gonorrhea treatment across primary care practices. Efforts are needed to develop interventions to improve quality of care for these sexually transmitted infections.

Background: Cardiac dysrhythmia is currently an extremely common disease. Severe arrhythmias often cause a series of complications, including congestive heart failure, fainting or syncope, stroke, and sudden death. Objective: The aim of this study was to predict incident arrhythmia prospectively within a 1-year period to provide early warning of impending arrhythmia. Methods: Retrospective (1,033,856 individuals enrolled between October 1, 2016, and October 1, 2017) and prospective (1,040,767 individuals enrolled between October 1, 2017, and October 1, 2018) cohorts were constructed from integrated electronic health records in Maine, United States. An ensemble learning workflow was built through multiple machine learning algorithms. Differentiating features, including acute and chronic diseases, procedures, health status, laboratory tests, prescriptions, clinical utilization indicators, and socioeconomic determinants, were compiled for incident arrhythmia assessment. The predictive model was retrospectively trained and calibrated using an isotonic regression method and was prospectively validated. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC). Results: The cardiac dysrhythmia case-finding algorithm (retrospective: AUROC 0.854; prospective: AUROC 0.827) stratified the population into 5 risk groups: 53.35% (555,233/1,040,767), 44.83% (466,594/1,040,767), 1.76% (18,290/1,040,767), 0.06% (623/1,040,767), and 0.003% (27/1,040,767) were in the very low-risk, low-risk, medium-risk, high-risk, and very high-risk groups, respectively; 51.85% (14/27) patients in the very high-risk subgroup were confirmed to have incident cardiac dysrhythmia within the subsequent 1 year. Conclusions: Our case-finding algorithm is promising for prospectively predicting 1-year incident cardiac dysrhythmias in a general population, and we believe that our case-finding algorithm can serve as an early warning system to allow statewide population-level screening and surveillance to improve cardiac dysrhythmia care.

Ceramides and dihydroceramides are sphingolipids that present in abundance at the cellular membrane of eukaryotes. Although their metabolic dysregulation has been implicated in many diseases, our knowledge about circulating ceramide changes during the pregnancy remains limited. In this study, we present the development and validation of a high-throughput liquid chromatography-tandem mass spectrometric (LC/MS/MS) method for simultaneous quantification of 16 ceramides and 10 dihydroceramides in human serum within 5 mins by using stable isotope-labeled ceramides as internal standards (ISs). This method employs a protein precipitation method for high throughput sample preparation, reverse phase isocratic elusion for chromatographic separation, and Multiple Reaction Monitoring (MRM) for mass spectrometric detection. To qualify for clinical applications, our assay was validated against the FDA guidelines: the Lower Limit of Quantitation (LLOQ as low as 1 nM), linearity (R ² >0.99), precision (Coefficient of Variation85%), and carryover (<0.1%). With enhanced sensitivity and specificity from this method, we have, for the first time, determined the serological levels of ceramides and dihydroceramides to reveal unique temporal gestational patterns. Our approach could have value in providing insights into disorders of pregnancy.

244 Background: Implementation of population screening for colorectal cancer (CRC) before colonoscopy can reduce the challenge of the overall capacity of bowel examination and improve survival. Blood based CRC assessment biomarkers, on a triage concept, can lead to improved selection to colonoscopy and cost-effective CRC care. Methods: Innovative multi-omics approaches, with global and targeted LCMS data production (metabolomics, lipidomics, and 2D proteomics) and integrative data analytics, were applied to discover serological biomarkers to assess nonadvanced adenoma and identify stage I/II colorectal bowel lesions. A cohort of 2396 normal, 660 adenoma, 953 stage I, and 101 stage II blood samples, was constructed to discover screening biomarkers to support case finding of patients at high risk for nonadvanced adenoma and stage I/II cancer for subsequent diagnostic colonoscopy. Results: A three-analyte mProbe panel was constructed which outperformed the commercial assays of plasma methylated septin 9 and fecal Cologuard tests. Sensitivity: (1) nonadvanced adenoma–Cologuard 17.2%, mProbe 76.0%; (2) stage I-III-Cologuard 93.3%, stage I-II Septin 9 (ARUP laboratories) 77%, stage I-II mProbe: 92.3%. Specificity–Cologuard 89.8%, Septin 9 (ARUP laboratories) 88%, mProbe 90.7%. Conclusions: mProbe triage concept of a blood-based protein biomarker panel promises the precision to allow future CRC screening, and reduce the low-risk utilization of unnecessary, unpleasant and risk-associated bowel examinations.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: Some hospitalized patients rapidly deteriorate due to either disease progression or imperfect triage and level of care assignment after their admission. An EWS to identify patients at high risk of subsequent intra-hospital death can be an effective tool to benefit patient safety and quality of care, and also reduce avoidable harm and costs. Objective: To prospectively validate a real-time early warning system (EWS) with the capacity to predict patients at high risk of in-hospital mortality during their inpatient episodes. Methods: Data were collected from the system-wide electronic medical record (EMR) of two acute Berkshire Health System hospitals, comprised of 54,246 inpatient admissions from January 01, 2015 to September 30, 2017, of which 3.97% resulted in intra-hospital deaths. After constructing the model using statistical methods and modern machine learning algorithms, we prospectively validated the algorithms as a real-time inpatient early warning system (EWS) for mortality. Results: Our EWS algorithm scored patients’ daily and long-term risk of in-hospital mortality probability after admission and stratified them into distinct risk groups. In the prospective validation, our EWS prospectively attained a c-statistic of 0.884, where 99 encounters were captured in the highest risk group, 68.69% of which died during the episodes. Furthermore, our real-time EWS successfully forecasted the top 13.33% of the expired patients at least 40.8 hours before death. Important clinical utilization features, together with coded diagnoses, vital signs and laboratory test results were recognized as impactful predictors in the final EWS. Conclusions: In this study, we prospectively demonstrated our new EWS’s capability of real-time monitoring and alerting clinicians to patients at high risk of in-hospital death, providing opportunities for timely interventions. This real-time EWS is able to assist clinical decision making and enable more actionable and effective individualized care for better patients’ health outcomes in target medical facilities.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: Lung cancer is the leading cause of cancer death worldwide. Early detection of individuals at risk of lung cancer is critical to reduce the mortality rate. Objective: The aim of this study was to develop and validate a prospective risk prediction model to identify patients at risk of receiving a new diagnosis of lung cancer (incident lung cancer) within the next one year in a general population. Methods: Data from individual patient electronic health records (EHRs) were extracted from the Maine Health Information Exchange network. The study population consisted of patients having at least one EHR between April 1, 2016 and March 31, 2018, who had no history of lung cancer. A retrospective (N=873,598) cohort and a prospective (N=836,659) cohort were formed for model construction and validation. An Extreme Gradient Boosting (XGBoost) algorithm was adopted to build the model. It assigned a score to each individual that measured the probability of receiving a new diagnosis of lung cancer from October 1, 2016 to September 31, 2017, based on the clinical profile in the retrospective cohort from the preceding 6 months. The model was validated using the prospective cohort to predict risk of incident lung cancer from April 1, 2017 to March 31, 2018, using the preceding 6 months’ clinical profile. Results: The model had an area under the curve (AUC) of 0.881(95% CI 0.873-0.889) in the prospective cohort. Two thresholds of 0.0045 and 0.01 were applied to the predictive scores to stratify the population into low-, medium- and high-risk categories. The incidence of lung cancer in the high-risk category (579/53,922, 1.07%) was 7.7 times higher than the incidence in the overall cohort (1,167/836,659, 0.14%). Age, a history of pulmonary diseases and other chronic diseases, medications for mental disorders, and social disparities were found to be associated with incident lung cancer. Conclusions: We developed and prospectively validated an accurate risk prediction model of incident lung cancer in the next one year by statistically learning from the statewide EHR data in the preceding 6 months. Our model was able to identify patients at high risk of incident lung cancer, which will benefit population health through preventive interventions or more intensive surveillance.

BACKGROUND The rapid deterioration observed in the condition of some hospitalized patients can be attributed to either disease progression or imperfect triage and level of care assignment after their admission. An early warning system (EWS) to identify patients at high risk of subsequent intrahospital death can be an effective tool for ensuring patient safety and quality of care and reducing avoidable harm and costs. OBJECTIVE The aim of this study was to prospectively validate a real-time EWS designed to predict patients at high risk of inpatient mortality during their hospital episodes. METHODS Data were collected from the system-wide electronic medical record (EMR) of two acute Berkshire Health System hospitals, comprising 54,246 inpatient admissions from January 1, 2015, to September 30, 2017, of which 2.30% (1248/54,246) resulted in intrahospital deaths. Multiple machine learning methods (linear and nonlinear) were explored and compared. The tree-based random forest method was selected to develop the predictive application for the intrahospital mortality assessment. After constructing the model, we prospectively validated the algorithms as a real-time inpatient EWS for mortality. RESULTS The EWS algorithm scored patients’ daily and long-term risk of inpatient mortality probability after admission and stratified them into distinct risk groups. In the prospective validation, the EWS prospectively attained a c-statistic of 0.884, where 99 encounters were captured in the highest risk group, 69% (68/99) of whom died during the episodes. It accurately predicted the possibility of death for the top 13.3% (34/255) of the patients at least 40.8 hours before death. Important clinical utilization features, together with coded diagnoses, vital signs, and laboratory test results were recognized as impactful predictors in the final EWS. CONCLUSIONS In this study, we prospectively demonstrated the capability of the newly-designed EWS to monitor and alert clinicians about patients at high risk of in-hospital death in real time, thereby providing opportunities for timely interventions. This real-time EWS is able to assist clinical decision making and enable more actionable and effective individualized care for patients’ better health outcomes in target medical facilities.

BACKGROUND Lung cancer is the leading cause of cancer death worldwide. Early detection of individuals at risk of lung cancer is critical to reduce the mortality rate. OBJECTIVE The aim of this study was to develop and validate a prospective risk prediction model to identify patients at risk of new incident lung cancer within the next 1 year in the general population. METHODS Data from individual patient electronic health records (EHRs) were extracted from the Maine Health Information Exchange network. The study population consisted of patients with at least one EHR between April 1, 2016, and March 31, 2018, who had no history of lung cancer. A retrospective cohort (N=873,598) and a prospective cohort (N=836,659) were formed for model construction and validation. An Extreme Gradient Boosting (XGBoost) algorithm was adopted to build the model. It assigned a score to each individual to quantify the probability of a new incident lung cancer diagnosis from October 1, 2016, to September 31, 2017. The model was trained with the clinical profile in the retrospective cohort from the preceding 6 months and validated with the prospective cohort to predict the risk of incident lung cancer from April 1, 2017, to March 31, 2018. RESULTS The model had an area under the curve (AUC) of 0.881 (95% CI 0.873-0.889) in the prospective cohort. Two thresholds of 0.0045 and 0.01 were applied to the predictive scores to stratify the population into low-, medium-, and high-risk categories. The incidence of lung cancer in the high-risk category (579/53,922, 1.07%) was 7.7 times higher than that in the overall cohort (1167/836,659, 0.14%). Age, a history of pulmonary diseases and other chronic diseases, medications for mental disorders, and social disparities were found to be associated with new incident lung cancer. CONCLUSIONS We retrospectively developed and prospectively validated an accurate risk prediction model of new incident lung cancer occurring in the next 1 year. Through statistical learning from the statewide EHR data in the preceding 6 months, our model was able to identify statewide high-risk patients, which will benefit the population health through establishment of preventive interventions or more intensive surveillance.