Effectiveness of BNT162b2 BA.4/5 bivalent mRNA vaccine against a range of COVID-19 outcomes in a large health system in the USA: a test-negative case-control study.

BACKGROUND: XBB-related omicron sublineages have recently replaced BA.4/5 as the predominant omicron sublineages in the USA and other regions globally. Despite preliminary signs of immune evasion of XBB sublineages, few data exist describing the real-world effectiveness of bivalent COVID-19 vaccines, especially against XBB-related illness. We aimed to investigate the effectiveness of the Pfizer--BioNTech BNT162b2 BA.4/5 bivalent vaccine against both BA.4/5-related and XBB-related disease in adults aged 18 years or older. METHODS: In this test-negative case-control study, we estimated the effectiveness of the BNT162b2 BA.4/5 bivalent vaccine using data from electronic health records of Kaiser Permanente Southern California health system members aged 18 years or older who received at least two doses of the wild-type COVID-19 mRNA vaccines. Participants sought care for acute respiratory infection between Aug 31, 2022, and April 15, 2023, and were tested for SARS-CoV-2 via PCR tests. Relative vaccine effectiveness (≥2 doses of wild-type mRNA vaccine plus a BNT162b2 BA.4/5 bivalent booster vs ≥2 doses of a wild-type mRNA vaccine alone) and absolute vaccine effectiveness (vs unvaccinated individuals) was estimated against critical illness related to acute respiratory infection (intensive care unit [ICU] admission, mechanical ventilation, or inpatient death), hospital admission, emergency department or urgent care visits, and in-person outpatient encounters with odds ratios from logistic regression models adjusted for demographic and clinical factors. We stratified vaccine effectiveness estimates for hospital admission, emergency department or urgent care visits, and outpatient encounters by omicron sublineage (ie, likely BA.4/5-related vs likely XBB-related), time since bivalent booster receipt, age group, number of wild-type doses received, and immunocompromised status. This study is registered with ClinicalTrials.gov (NCT04848584). FINDINGS: Analyses were conducted for 123 419 encounters (24 246 COVID-19 cases and 99 173 test-negative controls), including 4131 episode of critical illness (a subset of hospital admissions), 14 529 hospital admissions, 63 566 emergency department or urgent care visits, and 45 324 outpatient visits. 20 555 infections were BA.4/5 related and 3691 were XBB related. In adjusted analyses, relative vaccine effectiveness for those who received the BNT162b2 BA.4/5 bivalent booster compared with those who received at least two doses of a wild-type mRNA vaccine alone was an additional 50% (95% CI 23-68) against critical illness, an additional 39% (28-49) against hospital admission, an additional 35% (30-40) against emergency department or urgent care visits, and an additional 28% (22-33) against outpatient encounters. Waning of the bivalent booster from 0-3 months to 4-7 months after vaccination was evident for outpatient outcomes but was not detected for critical illness, hospital admission, and emergency department or urgent care outcomes. The relative effectiveness of the BNT162b2 BA.4/5 bivalent booster for XBB-related infections compared with BA.4/5-related infections was 56% (95% CI 12-78) versus 40% (27-50) for hospital admission; 34% (21-45) versus 36% (30-41) against emergency department or urgent care visits; and 29% (19-38) versus 27% (20-33) for outpatient encounters. INTERPRETATION: By mid-April, 2023, individuals previously vaccinated only with wild-type vaccines had little protection against COVID-19-including hospital admission. A BNT162b2 BA.4/5 bivalent booster restored protection against a range of COVID-19 outcomes, including against XBB-related sublineages, with the most substantial protection observed against hospital admission and critical illness. FUNDING: Pfizer.

Using natural language processing to identify the status of homelessness and housing instability among serious illness patients from clinical notes in an integrated healthcare system.

Background: Efficiently identifying the social risks of patients with serious illnesses (SIs) is the critical first step in providing patient-centered and value-driven care for this medically vulnerable population. Objective: To apply and further hone an existing natural language process (NLP) algorithm that identifies patients who are homeless/at risk of homeless to a SI population. Methods: Patients diagnosed with SI between 2019 and 2020 were identified using an adapted list of diagnosis codes from the Center for Advance Palliative Care from the Kaiser Permanente Southern California electronic health record. Clinical notes associated with medical encounters within 6 months before and after the diagnosis date were processed by a previously developed NLP algorithm to identify patients who were homeless/at risk of homelessness. To improve the generalizability to the SI population, the algorithm was refined by multiple iterations of chart review and adjudication. The updated algorithm was then applied to the SI population. Results: Among 206 993 patients with a SI diagnosis, 1737 (0.84%) were identified as homeless/at risk of homelessness. These patients were more likely to be male (51.1%), age among 45-64 years (44.7%), and have one or more emergency visit (65.8%) within a year of their diagnosis date. Validation of the updated algorithm yielded a sensitivity of 100.0% and a positive predictive value of 93.8%. Conclusions: The improved NLP algorithm effectively identified patients with SI who were homeless/at risk of homelessness and can be used to target interventions for this vulnerable group.

Risks of Uterine Perforation and Expulsion Associated With Intrauterine Devices.

OBJECTIVE: The APEX-IUD (Association of Perforation and Expulsion of Intrauterine Devices) study evaluated the association of postpartum timing of intrauterine device (IUD) insertion, breastfeeding, heavy menstrual bleeding, and IUD type (levonorgestrel-releasing vs copper) with risks of uterine perforation and IUD expulsion in usual clinical practice. We summarize the clinically important findings to inform counseling and shared decision making. METHODS: APEX-IUD was a real-world (using U.S. health care data) retrospective cohort study of individuals aged 50 years and younger with IUD insertions between 2001 and 2018 and with electronic health record data. Cumulative incidences of uterine perforation and IUD expulsion were calculated. Adjusted hazard ratios (aHRs) and 95% CIs were estimated from proportional hazards models with control of confounding. RESULTS: Among the study population of 326,658, absolute risk of uterine perforation was low overall (cumulative incidence, 0.21% [95% CI 0.19-0.23%] at 1 year and 0.61% [95% CI 0.56-0.66% at 5 years]) but was elevated for IUDs inserted during time intervals within 1 year postpartum, particularly among those between 4 days and 6 weeks postpartum (aHR 6.71, 95% CI 4.80-9.38), relative to nonpostpartum insertions. Among postpartum insertions, IUD expulsion risk was greatest for insertions in the immediate postpartum period (0-3 days after delivery) compared with nonpostpartum (aHR 5.34, 95% CI 4.47-6.39). Postpartum individuals who were breastfeeding had a slightly elevated risk of perforation and lowered risk of expulsion than those not breastfeeding. Among nonpostpartum individuals, those with a heavy menstrual bleeding diagnosis were at greater risk of expulsion than those without (aHR 2.84, 95% CI 2.66-3.03); heavy menstrual bleeding also was associated with a slightly elevated perforation risk. There was a slightly elevated perforation risk and slightly lower expulsion risk associated with levonorgestrel-releasing IUDs compared with copper IUDs. CONCLUSION: Absolute risk of adverse outcomes with IUD insertion is low. Clinicians should be aware of the differences in risks of uterine perforation and expulsion associated with IUD insertion during specific postpartum time periods and with a heavy menstrual bleeding diagnosis. This information should be incorporated into counseling and decision making for patients considering IUD insertion. FUNDING SOURCE: Bayer AG. CLINICAL TRIAL REGISTRATION: EU PAS register, EUPAS33461.

Research data warehouse: using electronic health records to conduct population-based observational studies.

Background: Electronic health records and many legacy systems contain rich longitudinal data that can be used for research; however, they typically are not readily available. Materials and methods: At Kaiser Permanente Southern California (KPSC), a research data warehouse (RDW) has been developed and maintained since the late 1990s and widely extended in 2006, aggregating and standardizing data collected from internal and a few external sources. This article provides a high-level overview of the RDW and discusses challenges common to data warehouses or repositories for research use. To demonstrate the application of the data, we report the volume, patient characteristics, and age-adjusted prevalence of selected medical conditions and utilization rates of selected medical procedures. Results: A total of 105 million person-years of health plan enrollment was recorded in the RDW between 1981 and 2018, with most healthcare utilization data available since early or middle 1990s. Among active enrollees on December 31, 2018, 15% were ≥65 years of age, 33.9% were non-Hispanic white, 43.3% Hispanic, 11.0% Asian, and 8.4% African American, and 34.4% of children (2-17 years old) and 72.1% of adults (≥18 years old) were overweight or obese. The age-adjusted prevalence of asthma, atrial fibrillation, diabetes mellitus, hypercholesteremia, and hypertension increased between 2001 and 2018. Hospitalization and Emergency Department (ED) visit rates appeared lower, and office visit rates seemed higher at KPSC compared to the reported US averages. Discussion and conclusion: Although the RDW is unique to KPSC, its methodologies and experience may provide useful insights for researchers of other healthcare systems worldwide in the era of big data analysis.

Effectiveness of a fourth dose of mRNA-1273 against COVID-19 among older adults in the United States: Interim results from an observational cohort study.

We evaluated relative vaccine effectiveness (rVE) of 4- vs. 3-dose mRNA-1273 against SARS-CoV-2 infection, and COVID-19 hospitalization and death in immunocompetent adults aged ≥50 years at Kaiser Permanente Southern California. We included 178,492 individuals who received a fourth dose of mRNA-1273, and 178,492 randomly selected 3-dose recipients who were matched to 4-dose recipients by age, sex, race/ethnicity, and third dose date. Adjusted 4- vs. 3-dose rVE against SARS-CoV-2 infection, COVID-19 hospitalization, and COVID-19 hospitalization death were 25.9 % (23.5 %, 28.2 %), 67.3 % (58.7 %, 74.1 %), and 72.5 % (-35.9 %, 95.2 %), respectively. Adjusted rVE against SARS-CoV-2 infection ranged between 19.8 % and 39.1 % across subgroups. Adjusted rVE against SARS-CoV-2 infection and COVID-19 hospitalization decreased 2-4 months after the fourth dose. Four mRNA-1273 doses provided significant protection against COVID-19 outcomes compared with 3 doses, consistent in various subgroups of demographic and clinical characteristics, although rVE varied and waned over time.

Machine learning versus regression for prediction of sporadic pancreatic cancer.

BACKGROUND/OBJECTIVES: There is currently no widely accepted approach to identify patients at increased risk for sporadic pancreatic cancer (PC). We aimed to compare the performance of two machine-learning models with a regression-based model in predicting pancreatic ductal adenocarcinoma (PDAC), the most common form of PC. METHODS: This retrospective cohort study consisted of patients 50-84 years of age enrolled in either Kaiser Permanente Southern California (KPSC, model training, internal validation) or the Veterans Affairs (VA, external testing) between 2008 and 2017. The performance of random survival forests (RSF) and eXtreme gradient boosting (XGB) models were compared to that of COX proportional hazards regression (COX). Heterogeneity of the three models were assessed. RESULTS: The KPSC and the VA cohorts consisted of 1.8 and 2.7 million patients with 1792 and 4582 incident PDAC cases within 18 months, respectively. Predictors selected into all three models included age, abdominal pain, weight change, and glycated hemoglobin (A1c). Additionally, RSF selected change in alanine transaminase (ALT), whereas the XGB and COX selected the rate of change in ALT. The COX model appeared to have lower AUC (KPSC: 0.737, 95% CI 0.710-0.764; VA: 0.706, 0.699-0.714), compared to those of RSF (KPSC: 0.767, 0.744-0.791; VA: 0.731, 0.724-0.739) and XGB (KPSC: 0.779, 0.755-0.802; VA: 0.742, 0.735-0.750). Among patients with top 5% predicted risk from all three models (N = 29,663), 117 developed PDAC, of which RSF, XGB and COX captured 84 (9 unique), 87 (4 unique), 87 (19 unique) cases, respectively. CONCLUSIONS: The three models complement each other, but each has unique contributions.

Prior SARS-CoV-2 infection and COVID-19 vaccine effectiveness against outpatient illness during widespread circulation of SARS-CoV-2 Omicron variant, US Flu VE network.

BACKGROUND: We estimated combined protection conferred by prior SARS-CoV-2 infection and COVID-19 vaccination against COVID-19-associated acute respiratory illness (ARI). METHODS: During SARS-CoV-2 Delta (B.1.617.2) and Omicron (B.1.1.529) variant circulation between October 2021 and April 2022, prospectively enrolled adult patients with outpatient ARI had respiratory and filter paper blood specimens collected for SARS-CoV-2 molecular testing and serology. Dried blood spots were tested for immunoglobulin-G antibodies against SARS-CoV-2 nucleocapsid (NP) and spike protein receptor binding domain antigen using a validated multiplex bead assay. Evidence of prior SARS-CoV-2 infection also included documented or self-reported laboratory-confirmed COVID-19. We used documented COVID-19 vaccination status to estimate vaccine effectiveness (VE) by multivariable logistic regression by prior infection status. RESULTS: Four hundred fifty-five (29%) of 1577 participants tested positive for SARS-CoV-2 infection at enrollment; 209 (46%) case-patients and 637 (57%) test-negative patients were NP seropositive, had documented previous laboratory-confirmed COVID-19, or self-reported prior infection. Among previously uninfected patients, three-dose VE was 97% (95% confidence interval [CI], 60%-99%) against Delta, but not statistically significant against Omicron. Among previously infected patients, three-dose VE was 57% (CI, 20%-76%) against Omicron; VE against Delta could not be estimated. CONCLUSIONS: Three mRNA COVID-19 vaccine doses provided additional protection against SARS-CoV-2 Omicron variant-associated illness among previously infected participants.

Prediction of persistent chronic cough in patients with chronic cough using machine learning.

Introduction: The aim of this study was to develop and validate prediction models for risk of persistent chronic cough (PCC) in patients with chronic cough (CC). This was a retrospective cohort study. Methods: Two retrospective cohorts of patients 18-85 years of age were identified for years 2011-2016: a specialist cohort which included CC patients diagnosed by specialists, and an event cohort which comprised CC patients identified by at least three cough events. A cough event could be a cough diagnosis, dispensing of cough medication or any indication of cough in clinical notes. Model training and validation were conducted using two machine-learning approaches and 400+ features. Sensitivity analyses were also conducted. PCC was defined as a CC diagnosis or any two (specialist cohort) or three (event cohort) cough events in year 2 and again in year 3 after the index date. Results: 8581 and 52 010 patients met the eligibility criteria for the specialist and event cohorts (mean age 60.0 and 55.5 years), respectively. 38.2% and 12.4% of patients in the specialist and event cohorts, respectively, developed PCC. The utilisation-based models were mainly based on baseline healthcare utilisations associated with CC or respiratory diseases, while the diagnosis-based models incorporated traditional parameters including age, asthma, pulmonary fibrosis, obstructive pulmonary disease, gastro-oesophageal reflux, hypertension and bronchiectasis. All final models were parsimonious (five to seven predictors) and moderately accurate (area under the curve: 0.74-0.76 for utilisation-based models and 0.71 for diagnosis-based models). Conclusions: The application of our risk prediction models may be used to identify high-risk PCC patients at any stage of the clinical testing/evaluation to facilitate decision making.

Automated placental abruption identification using semantic segmentation, quantitative features, SVM, ensemble and multi-path CNN.

The placenta is a fundamental organ throughout the pregnancy and the fetus' health is closely related to its proper function. Because of the importance of the placenta, any suspicious placental conditions require ultrasound image investigation. We propose an automated method for processing fetal ultrasonography images to identify placental abruption using machine learning methods in this paper. The placental imaging characteristics are used as the semantic identifiers of the region of the placenta compared with the amniotic fluid and hard organs. The quantitative feature extraction is applied to the automatically identified placental regions to assign a vector of optical features to each ultrasonographic image. In the first classification step, two methods of kernel-based Support Vector Machine (SVM) and decision tree Ensemble classifier are elaborated and compared for identification of the abruption cases and controls. The Recursive Feature Elimination (RFE) is applied for optimizing the feature vector elements for the best performance of each classifier. In the second step, the deep learning classifiers of multi-path ResNet-50 and Inception-V3 are used in combination with RFE. The resulting performances of the algorithms are compared together to reveal the best classification method for the identification of the abruption status. The best results were achieved for optimized ResNet-50 with an accuracy of 82.88% ± SD 1.42% in the identification of placental abruption on the testing dataset. These results show it is possible to construct an automated analysis method with affordable performance for the detection of placental abruption based on ultrasound images.

Analysis of mRNA COVID-19 Vaccine Uptake Among Immunocompromised Individuals in a Large US Health System.

Importance: Immunocompromised individuals are at increased risk for severe outcomes due to SARS-CoV-2 infection. Given the varying and complex nature of COVID-19 vaccination recommendations, it is important to understand COVID-19 vaccine uptake in this vulnerable population. Objective: To assess mRNA COVID-19 vaccine uptake and factors associated with uptake among immunocompromised individuals from December 14, 2020, through August 6, 2022. Design, Setting, and Participants: This cohort study was conducted with patients of Kaiser Permanente Southern California (KPSC), an integrated health care system in the US. The study included patients aged 18 years or older who were immunocompromised (individuals with an immunocompromising condition or patients who received immunosuppressive medications in the year prior to December 14, 2020) and still met criteria for being immunocompromised 1 year later. Exposures: Age, sex, self-identified race and ethnicity, prior positive COVID-19 test result, immunocompromising condition, immunomodulating medication, comorbidities, health care utilization, and neighborhood median income. Main Outcomes and Measures: Outcomes were the number of doses of mRNA COVID-19 vaccine received and the factors associated with receipt of at least 4 doses, estimated by hazard ratios (HRs) and 95% Wald CIs via Cox proportional hazards regression. Statistical analyses were conducted between August 9 and 23, 2022. Results: Overall, 42 697 immunocompromised individuals met the study eligibility criteria. Among these, 18 789 (44.0%) were aged 65 years or older; 20 061 (47.0%) were women and 22 635 (53.0%) were men. With regard to race and ethnicity, 4295 participants (10.1%) identified as Asian or Pacific Islander, 5174 (12.1%) as Black, 14 289 (33.5%) as Hispanic, and 17 902 (41.9%) as White. As of the end of the study period and after accounting for participant censoring due to death or disenrollment from the KPSC health plan, 78.0% of immunocompromised individuals had received a third dose of mRNA COVID-19 vaccine. Only 41.0% had received a fourth dose, which corresponds to a primary series and a monovalent booster dose for immunocompromised individuals. Uptake of a fifth dose was only 0.9% following the US Centers for Disease Control and Prevention (CDC) recommendation to receive a second monovalent booster (ie, fifth dose). Adults aged 65 years or older (HR, 3.95 [95% CI, 3.70-4.22]) were more likely to receive at least 4 doses compared with those aged 18 to 44 years or 45 to 64 years (2.52 [2.36-2.69]). Hispanic and non-Hispanic Black adults (HR, 0.77 [95% CI, 0.74-0.80] and 0.82 [0.78-0.87], respectively, compared with non-Hispanic White adults), individuals with prior documented SARS-CoV-2 infection (0.71 [0.62-0.81] compared with those without), and individuals receiving high-dose corticosteroids (0.88 [0.81-0.95] compared with those who were not) were less likely to receive at least 4 doses. Conclusions and Relevance: These findings suggest that adherence to CDC mRNA monovalent COVID-19 booster dose recommendations among immunocompromised individuals was low. Given the increased risk for severe COVID-19 in this vulnerable population and the well-established additional protection afforded by booster doses, targeted and tailored efforts to ensure that immunocompromised individuals remain up to date with COVID-19 booster dose recommendations are warranted.

Chronic and Sustained High-Dose Opioid Use in an Integrated Health System.

INTRODUCTION: The Centers for Disease Control and Prevention Guideline for Prescribing Opioids for Chronic Pain released in 2016 had led to decreases in opioid prescribing. This study sought to examine chronic and sustained high-dose prescription opioid use in an integrated health system. METHODS: A serial cross-sectional study was conducted in 2021 to estimate the annual age-adjusted prevalence and incidence of chronic and high-dose opioid use among demographically diverse noncancer adults in an integrated health system in Southern California during 2013-2020. Interrupted time-series analysis with segmented regression was conducted to estimate changes in the trends in annual rates before (2013-2015) and after (2017-2020) the 2016 guideline, treating 2016 as a wash-out period. RESULTS: Prevalence and incidence of chronic use and sustained high-dose use had started to decrease after a health system intervention program before the 2016 Centers for Disease Control and Prevention guideline release and continued to decline after the guideline. Among those with sustained high-dose use, there was a substantial decrease in persons with an average daily dosage ≥90 morphine milligram equivalent and concurrent benzodiazepine use. An accelerated decrease in prevalent chronic use after the guideline was observed (slope change: -11.1 [95% CI= -20.3, -1.9] users/10,000 person-years, p=0.03). The incidence of chronic use and sustained high-dose use continued to decrease after the guideline release but at a slower pace. CONCLUSIONS: Implementing evidence-based prescribing guidelines was associated with a decrease in chronic and sustained high-dose prescription opioid use.

Prior SARS-CoV-2 Infection and COVID-19 Vaccine Effectiveness against Outpatient Illness during Widespread Circulation of SARS-CoV-2 Omicron Variant, US Flu VE Network.

Background: We estimated combined protection conferred by prior SARS-CoV-2 infection and COVID-19 vaccination against COVID-19-associated acute respiratory illness (ARI). Methods: During SARS-CoV-2 Delta (B.1.617.2) and Omicron (B.1.1.529) variant circulation between October 2021 and April 2022, prospectively enrolled adult patients with outpatient ARI had respiratory and filter paper blood specimens collected for SARS-CoV-2 molecular testing and serology. Dried blood spots were tested for immunoglobulin-G antibodies against SARS-CoV-2 nucleocapsid (NP) and spike protein receptor binding domain antigen using a validated multiplex bead assay. Evidence of prior SARS-CoV-2 infection also included documented or self-reported laboratory-confirmed COVID-19. We used documented COVID-19 vaccination status to estimate vaccine effectiveness (VE) by multivariable logistic regression by prior infection status. Results: 455 (29%) of 1577 participants tested positive for SARS-CoV-2 infection at enrollment; 209 (46%) case-patients and 637 (57%) test-negative patients were NP seropositive, had documented previous laboratory-confirmed COVID-19, or self-reported prior infection. Among previously uninfected patients, three-dose VE was 97% (95% confidence interval [CI], 60%- 99%) against Delta, but not statistically significant against Omicron. Among previously infected patients, three-dose VE was 57% (CI, 20%-76%) against Omicron; VE against Delta could not be estimated. Conclusions: Three mRNA COVID-19 vaccine doses provided additional protection against SARS-CoV-2 Omicron variant-associated illness among previously infected participants.

BNT162b2 Against COVID-19-Associated Emergency Department and Urgent Care Visits Among Children 5-11 Years of Age: A Test Negative Design.

In a 1:1 matched test-negative design among 5- to 11-year-olds in the Kaiser Permanente Southern California health system (n = 3984), BNT162b2 effectiveness against the omicron-related emergency department or urgent care encounters was 60% [95%CI: 47-69] <3 months post-dose-two and 28% [8-43] after ≥3 months. A booster improved protection to 77% [53-88].

Derivation and External Validation of Machine Learning-Based Model for Detection of Pancreatic Cancer.

INTRODUCTION: There is currently no widely accepted approach to screening for pancreatic cancer (PC). We aimed to develop and validate a risk prediction model for pancreatic ductal adenocarcinoma (PDAC), the most common form of PC, across 2 health systems using electronic health records. METHODS: This retrospective cohort study consisted of patients aged 50-84 years having at least 1 clinic-based visit over a 10-year study period at Kaiser Permanente Southern California (model training, internal validation) and the Veterans Affairs (VA, external testing). Random survival forests models were built to identify the most relevant predictors from >500 variables and to predict risk of PDAC within 18 months of cohort entry. RESULTS: The Kaiser Permanente Southern California cohort consisted of 1.8 million patients (mean age 61.6) with 1,792 PDAC cases. The 18-month incidence rate of PDAC was 0.77 (95% confidence interval 0.73-0.80)/1,000 person-years. The final main model contained age, abdominal pain, weight change, HbA1c, and alanine transaminase change (c-index: mean = 0.77, SD = 0.02; calibration test: P value 0.4, SD 0.3). The final early detection model comprised the same features as those selected by the main model except for abdominal pain (c-index: 0.77 and SD 0.4; calibration test: P value 0.3 and SD 0.3). The VA testing cohort consisted of 2.7 million patients (mean age 66.1) with an 18-month incidence rate of 1.27 (1.23-1.30)/1,000 person-years. The recalibrated main and early detection models based on VA testing data sets achieved a mean c-index of 0.71 (SD 0.002) and 0.68 (SD 0.003), respectively. DISCUSSION: Using widely available parameters in electronic health records, we developed and externally validated parsimonious machine learning-based models for detection of PC. These models may be suitable for real-time clinical application.

Effectiveness and durability of BNT162b2 vaccine against hospital and emergency department admissions due to SARS-CoV-2 omicron sub-lineages BA.1 and BA.2 in a large health system in the USA: a test-negative, case-control study.

BACKGROUND: The SARS-CoV-2 omicron (B.1.1.529 BA.1) lineage was first detected in November, 2021, and is associated with reduced vaccine effectiveness. By March, 2022, BA.1 had been replaced by sub-lineage BA.2 in the USA. As new variants evolve, vaccine performance must be continually assessed. We aimed to evaluate the effectiveness and durability of BNT162b2 (Pfizer-BioNTech) against hospital and emergency department admissions for BA.1 and BA.2. METHODS: In this test-negative, case-control study, we sourced data from the electronic health records of adult (aged ≥18 years) members of Kaiser Permanente Southern California (KPSC), which is a health-care system in the USA, who were admitted to one of 15 KPSC hospitals or emergency departments (without subsequent hospitalisation) between Dec 27, 2021, and June 4, 2022, with an acute respiratory infection and were tested for SARS-CoV-2 by RT-PCR. Omicron sub-lineage was determined by use of sequencing, spike gene target failure, and the predominance of variants in certain time periods. Our main outcome was the effectiveness of two or three doses of BNT162b2 in preventing emergency department or hospital admission. Variant-specific vaccine effectiveness was evaluated by comparing the odds ratios from logistic regression models of vaccination between test-positive cases and test-negative controls, adjusting for the month of admission, age, sex, race and ethnicity, body-mass index, Charlson Comorbidity Index, previous influenza or pneumococcal vaccines, and previous SARS-CoV-2 infection. We also assessed effectiveness by the time since vaccination. This study is registered at ClinicalTrials.gov, NCT04848584, and is ongoing. FINDINGS: Of 65 813 total admissions during the study period, we included 16 994 in our analyses, of which 7435 were due to BA.1, 1056 were due to BA.2, and 8503 were not due to SARS-CoV-2. In adjusted analyses, two-dose vaccine effectiveness was 40% (95% CI 27 to 50) for hospitalisation and 29% (18 to 38) for emergency department admission against BA.1 and 56% (31 to 72) for hospitalisation and 16% (-5 to 33) for emergency department admission against BA.2. Three-dose vaccine effectiveness was 79% (74 to 83) for hospitalisation and 72% (67 to 77) for emergency department admission against BA.1 and 71% (55 to 81) for hospitalisation and 21% (1 to 37) for emergency department admission against BA.2. Less than 3 months after the third dose, vaccine effectiveness was 80% (74 to 84) for hospitalisation and 74% (69 to 78) for emergency department admission against BA.1. Vaccine effectiveness 3 months or more after the third dose was 76% (69 to 82) against BA.1-related hospitalisation and 65% (56 to 73) against BA.1-related emergency department admission. Against BA.2, vaccine effectiveness was 74% (47 to 87) for hospitalisation and 59% (40 to 72) for emergency department admission at less than 3 months after the third dose and 70% (53 to 81) for hospitalisation and 5% (-21 to 25) for emergency department admission at 3 months or more after the third dose. INTERPRETATION: Two doses of BNT162b2 provided only partial protection against BA.1-related and BA.2-related hospital and emergency department admission, which underscores the need for booster doses against omicron. Although three doses offered high levels of protection (≥70%) against hospitalisation, variant-adapted vaccines are probably needed to improve protection against less severe endpoints, like emergency department admission, especially for BA.2. FUNDING: Pfizer.

Examining recent trends in spontaneous and iatrogenic preterm birth across race and ethnicity in a large managed care population.

BACKGROUND: For the past several decades, epidemiological studies originating from the United States have consistently reported increasing rates of preterm birth (PTB). Despite the implementation of several clinical and public health interventions to reduce PTB rates, it remains the leading cause of infant morbidity and mortality in the United States and around the world. OBJECTIVE: This study aimed to examine recent trends in preterm birth and its clinical subtypes by maternal race and ethnicity among singleton births. STUDY DESIGN: Kaiser Permanente Southern California electronic health records for all singleton births between 2009 and 2020 (n=427,698) were used to examine preterm birth trends and their subtypes (spontaneous and iatrogenic preterm births). Data on preterm labor triage extracted from electronic health records using natural language processing were used to define preterm birth subtypes. Maternal race and ethnicity are categorized as non-Hispanic White, non-Hispanic Black, Hispanic, and non-Hispanic Asian or Pacific Islander. Multiple logistic regression was used to quantify the linear trend for preterm birth and its subtypes. Racial and ethnic trends were further examined by considering statistical interactions and stratifications. RESULTS: From 2009 to 2020, the overall preterm birth rate decreased by 9.12% (from 8.04% to 7.31%; P<.001). The rates decreased by 19.29% among non-Hispanic Whites (from 7.23% to 5.83%; P<.001), 6.15% among Hispanics (from 7.82% to 7.34%; P=.036), and 12.60% among non-Hispanic Asian or Pacific Islanders (from 8.90% to 7.78%; P<.001), whereas a nonsignificantly increased preterm birth rate (8.45%) was observed among non-Hispanic Blacks (from 9.91% to 10.75%; P=.103). Between 2009 and 2020, overall spontaneous preterm birth rates decreased by 28.85% (from 5.75% to 4.09%; P<.001). However, overall iatrogenic preterm birth rates increased by 40.45% (from 2.29% to 3.22%; p<.001). Spontaneous preterm birth rates decreased by 34.73% among non-Hispanic Whites (from 5.44% to 3.55%; P<.001), 19.75% among non-Hispanic Blacks (from 6.82% to 5.47%; P<.001), 22.96% among Hispanics (from 5.55% to 4.28%; P<.001), and 28.19% among non-Hispanic Asian or Pacific Islanders (from 6.50% to 4.67%; P<.001). Iatrogenic preterm birth rates increased by 52.42% among non-Hispanic Whites (from 1.88% to 2.61%; P<.001), 107.89% among non-Hispanic Blacks (from 3.18% to 6.13%; P<.001), 46.88% among Hispanics (from 2.29% to 3.26%; P<.001), and 42.21% among non-Hispanic Asian or Pacific Islanders (from 2.45% to 3.44%; P<.001). CONCLUSION: The overall preterm birth rate decreased over time and was driven by a decrease in the spontaneous preterm birth rate. There is racial and ethnic variability in the rates of spontaneous preterm birth and iatrogenic preterm birth. The observed increase in iatrogenic preterm birth among all racial and ethnic groups, especially non-Hispanic Blacks, is disconcerting and needs further investigation.

Demographic, Reproductive, and Medical Risk Factors for Intrauterine Device Expulsion.

OBJECTIVE: To explore to what extent intrauterine device (IUD) expulsion is associated with demographic and clinical risk factors. METHODS: The APEX-IUD (Association of Perforation and Expulsion of IntraUterine Devices) study was a U.S. cohort study using electronic health records from three integrated health care systems (Kaiser Permanente Northern California, Southern California, and Washington) and a health care information exchange (Regenstrief Institute). These analyses included individuals aged 50 years or younger with IUD insertions from 2001 to 2018. Intrauterine device expulsion cumulative incidence and incidence rates were estimated. Using Cox regression models, hazard ratios with 95% CIs were estimated before and after adjustment for risk factors of interest (age, race and ethnicity, parity, body mass index [BMI], heavy menstrual bleeding, and dysmenorrhea) and potential confounders. RESULTS: In total, 228,834 individuals with IUD insertion and no delivery in the previous 52 weeks were identified (184,733 [80.7%] with levonorgestrel-releasing intrauterine system). Diagnosis of heavy menstrual bleeding-particularly a diagnosis in both recent and past periods-was the strongest risk factor for IUD expulsion. Categories with the highest risk of IUD expulsion within each risk factor included individuals diagnosed with overweight, obesity, and morbid obesity; those in younger age groups, especially among those aged 24 years or younger; and in those with parity of four or more. Non-Hispanic White individuals had the lowest incidence and risk, and after adjustment, Asian or Pacific Islander individuals had the highest risk. Dysmenorrhea was not independently associated with expulsion risk when adjusting for heavy menstrual bleeding. CONCLUSION: Most risk factors for expulsion identified in this study appear consistent with known physiologic factors that affect uterine anatomy and physiology (age, BMI, heavy menstrual bleeding, parity). The increased risk of IUD expulsion among individuals of color warrants further investigation. Intrauterine devices are an effective long-term contraceptive; expulsion is uncommon, but patients should be counseled accordingly. FUNDING SOURCE: Bayer AG. CLINICAL TRIAL REGISTRATION: EU PAS register, EUPAS33461.

Natural Language Processing for Improved Characterization of COVID-19 Symptoms: Observational Study of 350,000 Patients in a Large Integrated Health Care System.

BACKGROUND: Natural language processing (NLP) of unstructured text from electronic medical records (EMR) can improve the characterization of COVID-19 signs and symptoms, but large-scale studies demonstrating the real-world application and validation of NLP for this purpose are limited. OBJECTIVE: The aim of this paper is to assess the contribution of NLP when identifying COVID-19 signs and symptoms from EMR. METHODS: This study was conducted in Kaiser Permanente Southern California, a large integrated health care system using data from all patients with positive SARS-CoV-2 laboratory tests from March 2020 to May 2021. An NLP algorithm was developed to extract free text from EMR on 12 established signs and symptoms of COVID-19, including fever, cough, headache, fatigue, dyspnea, chills, sore throat, myalgia, anosmia, diarrhea, vomiting or nausea, and abdominal pain. The proportion of patients reporting each symptom and the corresponding onset dates were described before and after supplementing structured EMR data with NLP-extracted signs and symptoms. A random sample of 100 chart-reviewed and adjudicated SARS-CoV-2-positive cases were used to validate the algorithm performance. RESULTS: A total of 359,938 patients (mean age 40.4 [SD 19.2] years; 191,630/359,938, 53% female) with confirmed SARS-CoV-2 infection were identified over the study period. The most common signs and symptoms identified through NLP-supplemented analyses were cough (220,631/359,938, 61%), fever (185,618/359,938, 52%), myalgia (153,042/359,938, 43%), and headache (144,705/359,938, 40%). The NLP algorithm identified an additional 55,568 (15%) symptomatic cases that were previously defined as asymptomatic using structured data alone. The proportion of additional cases with each selected symptom identified in NLP-supplemented analysis varied across the selected symptoms, from 29% (63,742/220,631) of all records for cough to 64% (38,884/60,865) of all records with nausea or vomiting. Of the 295,305 symptomatic patients, the median time from symptom onset to testing was 3 days using structured data alone, whereas the NLP algorithm identified signs or symptoms approximately 1 day earlier. When validated against chart-reviewed cases, the NLP algorithm successfully identified signs and symptoms with consistently high sensitivity (ranging from 87% to 100%) and specificity (94% to 100%). CONCLUSIONS: These findings demonstrate that NLP can identify and characterize a broad set of COVID-19 signs and symptoms from unstructured EMR data with enhanced detail and timeliness compared with structured data alone.

Postpartum Migraine Headache Coding in Electronic Health Records of a Large Integrated Health Care System: Validation Study.

BACKGROUND: Migraine is a common neurological disorder characterized by repeated headaches of varying intensity. The prevalence and severity of migraine headaches disproportionally affects women, particularly during the postpartum period. Moreover, migraines during pregnancy have been associated with adverse maternal outcomes, including preeclampsia and postpartum stroke. However, due to the lack of a validated instrument for uniform case ascertainment on postpartum migraine headache, there is uncertainty in the reported prevalence in the literature. OBJECTIVE: The aim of this study was to evaluate the completeness and accuracy of reporting postpartum migraine headache coding in a large integrated health care system's electronic health records (EHRs) and to compare the coding quality before and after the implementation of the International Classification of Diseases, 10th revision, Clinical Modification (ICD-10-CM) codes and pharmacy records in EHRs. METHODS: Medical records of 200 deliveries in all 15 Kaiser Permanente Southern California hospitals during 2 time periods, that is, January 1, 2012 through December 31, 2014 (International Classification of Diseases, 9th revision, Clinical Modification [ICD-9-CM] coding period) and January 1, 2017 through December 31, 2019 (ICD-10-CM coding period), were randomly selected from EHRs for chart review. Two trained research associates reviewed the EHRs for all 200 women for postpartum migraine headache cases documented within 1 year after delivery. Women were considered to have postpartum migraine headache if either a mention of migraine headache (yes for diagnosis) or a prescription for treatment of migraine headache (yes for pharmacy records) was noted in the electronic chart. Results from the chart abstraction served as the gold standard and were compared with corresponding diagnosis and pharmacy prescription utilization records for both ICD-9-CM and ICD-10-CM coding periods through comparisons of sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), as well as the summary statistics of F-score and Youden J statistic (J). The kappa statistic (κ) for interrater reliability was calculated. RESULTS: The overall agreement between the identification of migraine headache using diagnosis codes and pharmacy records compared to the medical record review was strong. Diagnosis coding (F-score=87.8%; J=82.5%) did better than pharmacy records (F-score=72.7%; J=57.5%) when identifying cases, but combining both of these sources of data produced much greater accuracy in the identification of postpartum migraine cases (F-score=96.9%; J=99.7%) with sensitivity, specificity, PPV, and NPV of 100%, 99.7%, 93.9%, and 100%, respectively. Results were similar across the ICD-9-CM (F-score=98.7%, J=99.9%) and ICD-10-CM coding periods (F-score=94.9%; J=99.6%). The interrater reliability between the 2 research associates for postpartum migraine headache was 100%. CONCLUSIONS: Neither diagnostic codes nor pharmacy records alone are sufficient for identifying postpartum migraine cases reliably, but when used together, they are quite reliable. The completeness of the data remained similar after the implementation of the ICD-10-CM coding in the EHR system.

Cohort profile: pathways to care among people with disorders of sex development (DSD).

PURPOSE: The 'DSD Pathways' study was initiated to assess health status and patterns of care among people enrolled in large integrated healthcare systems and diagnosed with conditions comprising the broad category of disorders (differences) of sex development (DSD). The objectives of this communication are to describe methods of cohort ascertainment for two specific DSD conditions-classic congenital adrenal hyperplasia with 46,XX karyotype (46,XX CAH) and complete androgen insensitivity syndrome (CAIS). PARTICIPANTS: Using electronic health records we developed an algorithm that combined diagnostic codes, clinical notes, laboratory data and pharmacy records to assign each cohort candidate a 'strength-of-evidence' score supporting the diagnosis of interest. A sample of cohort candidates underwent a review of the full medical record to determine the score cutoffs for final cohort validation. FINDINGS TO DATE: Among 5404 classic 46,XX CAH cohort candidates the strength-of-evidence scores ranged between 0 and 10. Based on sample validation, the eligibility cut-off for full review was set at the strength-of-evidence score of ≥7 among children under the age of 8 years and ≥8 among older cohort candidates. The final validation of all cohort candidates who met the cut-off criteria identified 115 persons with classic 46,XX CAH. The strength-of-evidence scores among 648 CAIS cohort candidates ranged from 2 to 10. There were no confirmed CAIS cases among cohort candidates with scores <6. The in-depth medical record review for candidates with scores ≥6 identified 61 confirmed cases of CAIS. FUTURE PLANS: As the first cohort of this type, the DSD Pathways study is well-positioned to fill existing knowledge gaps related to management and outcomes in this heterogeneous population. Analyses will examine diagnostic and referral patterns, adherence to care recommendations and physical and mental health morbidities examined through comparisons of DSD and reference populations and analyses of health status across DSD categories.