COVID-19 Distribution in Pregnancy, Drug Use Patterns and COVID-19 Medication during the Pandemic in Spain: Data from Real-World Electronic Health Records
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ABSTRACT:
SUBMITTER: Mota-Perez M
PROVIDER: S-EPMC10892820 | biostudies-literature | 2024 Feb
REPOSITORIES: biostudies-literature
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Project description:Coronavirus disease 2019 (COVID-19) is a respiratory disease with rapid human-to-human transmission caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Due to the exponential growth of infections, identifying patients with the highest mortality risk early is critical to enable effective intervention and prioritisation of care. Here, we present the COVID-19 early warning system (CovEWS), a risk scoring system for assessing COVID-19 related mortality risk that we developed using data amounting to a total of over 2863 years of observation time from a cohort of 66 430 patients seen at over 69 healthcare institutions. On an external cohort of 5005 patients, CovEWS predicts mortality from 78.8% (95% confidence interval [CI]: 76.0, 84.7%) to 69.4% (95% CI: 57.6, 75.2%) specificity at sensitivities greater than 95% between, respectively, 1 and 192 h prior to mortality events. CovEWS could enable earlier intervention, and may therefore help in preventing or mitigating COVID-19 related mortality.
| S-EPMC7886884 | biostudies-literature
Project description:This study aims to predict death after COVID-19 using only the past medical information routinely collected in electronic health records (EHRs) and to understand the differences in risk factors across age groups. Combining computational methods and clinical expertise, we curated clusters that represent 46 clinical conditions as potential risk factors for death after a COVID-19 infection. We trained age-stratified generalized linear models (GLMs) with component-wise gradient boosting to predict the probability of death based on what we know from the patients before they contracted the virus. Despite only relying on previously documented demographics and comorbidities, our models demonstrated similar performance to other prognostic models that require an assortment of symptoms, laboratory values, and images at the time of diagnosis or during the course of the illness. In general, we found age as the most important predictor of mortality in COVID-19 patients. A history of pneumonia, which is rarely asked in typical epidemiology studies, was one of the most important risk factors for predicting COVID-19 mortality. A history of diabetes with complications and cancer (breast and prostate) were notable risk factors for patients between the ages of 45 and 65 years. In patients aged 65-85 years, diseases that affect the pulmonary system, including interstitial lung disease, chronic obstructive pulmonary disease, lung cancer, and a smoking history, were important for predicting mortality. The ability to compute precise individual-level risk scores exclusively based on the EHR is crucial for effectively allocating and distributing resources, such as prioritizing vaccination among the general population.
| S-EPMC7862405 | biostudies-literature
Project description:ObjectiveDespite advancement in vaccines and treatments for COVID-19 over the past 2 years, many concerns remain about reinfection and waning immunity against COVID-19 and its variants, especially among people with substance use disorder (SUD). The study assessed the risk of COVID-19 reinfection and severe illness among adults with SUD and their vaccination status to inform management in this vulnerable population as the pandemic continues.DesignRetrospective cohort study.SettingNationwide electronic health records (TriNetX database) in the USA among adults with COVID-19 infection from January 2020 to June 2022.ParticipantsAdults (age ≥18 years) who were infected by COVID-19, excluding those who had cancer or lived in nursing homes or palliative care facilities.Outcome measuresCOVID-19 reinfection was defined as a new diagnosis after 45 days of the initial infection. Logistic regression was applied to assess the OR of COVID-19 reinfection and severe outcomes within 30 day of reinfection for adults with alcohol (AUD), opioid (OUD), cocaine (CUD), stimulant (STUD), cannabis (CAUD) and other use disorders, controlled for demographic and comorbid conditions.ResultsThe SUD cohort was 13%-29% more likely to be reinfected by COVID-19 and had significantly higher 30-day mortality. Adults with AUD, STUD and OUD were at greater risks (adjusted ORs, AORs=1.69-1.86) of emergency department, hospital and intensive care admissions after 30 days of reinfection. Individuals with SUD and multiple vaccines doses were associated with decreased risks of worse COVID-19 outcomes. Lower COVID-19 reinfection rates (AORs=0.67-0.84) were only found among individuals with AUD, CUD or CAUD who had COVID-19 vaccination.ConclusionsIndividuals with SUD had greater risks of COVID-19 reinfection and poor outcomes, especially those with OUD, STUD and AUD. Multiple vaccinations are recommended to reduce severe illness after COVID-19 reinfection in the SUD population.
| S-EPMC10729165 | biostudies-literature
Project description:PrcisUtilizing an automated pipeline for data extraction from electronic health records provides real-world information on the success of various glaucoma procedures, with tube shunt implantation associated with increased failure rates compared with trabeculectomy.BackgroundWe aimed to evaluate the long-term survival of glaucoma surgeries using an automated pipeline for extraction of outcomes from electronic health records.MethodsA retrospective observational study from a single academic center. Patients undergoing trabeculectomy, Ex-PRESS shunt, Baerveldt, and Ahmed tube shunt insertion from 2009 to 2018 were identified from electronic health record procedure codes. Patient characteristics were identified from structured and unstructured fields using a previously validated natural language processing pipeline.ResultsFive hundred twelve patients underwent 711 glaucoma surgeries: 287 trabeculectomies, 47 Ex-PRESS shunts, 274 Baerveldt and 103 Ahmed tube implantations. The Median follow-up was 359 days. The mean baseline IOP was 24.4 mm Hg (SD 10.9), and 73.1% were on ≥3 medications. Compared with trabeculectomy, tube shunt surgery had a higher risk of failure (Baerveldt: Hazard Ratio (HR) 1.44, 95% CI 1.02 to 2.02; Ahmed: HR 2.01, 95% CI 1.28 to 3.17). Previous glaucoma surgery was associated with increased failure (≥2 previous surgeries: HR 2.74, 95% CI 1.62 to 4.64), as were fewer baseline medications (<3 medications: HR 2.96, 95% CI 2.12 to 4.13) and male sex (HR 1.40, 95% CI 1.03 to 1.90). At 1 year, tube shunt patients had a 2.53 mm Hg ( P =0.002) higher IOP compared with trabeculectomy patients.ConclusionsBaerveldt and Ahmed tube shunt implantation was associated with increased failure compared with trabeculectomy. Fewer baseline medications, previous glaucoma surgeries, and male sex were also risk factors for failure. These results demonstrate the utility of applying an informatics pipeline to electronic health records to investigate key clinical questions using real-world evidence.
| S-EPMC9633387 | biostudies-literature
Project description:Coronavirus disease 2019 (COVID-19) has strained healthcare systems worldwide. Predicting COVID-19 severity could optimize resource allocation, like oxygen devices and intensive care. If machine learning model could forecast the severity of COVID-19 patients, hospital resource allocation would be more comfortable. This study evaluated machine learning models using electronic records from 3,996 COVID-19 patients to forecast mild, moderate, or severe disease up to 2 days in advance. A deep neural network (DNN) model achieved 91.8% accuracy, 0.96 AUROC, and 0.90 AUPRC for 2-day predictions, regardless of disease phase. Tree-based models like random forest achieved slightly better metrics (random forest: 94.1% of accuracy, 0.98 AUROC, 0.95 AUPRC; Gradient boost: 94.1% of accuracy, 0.98 AUROC, 0.94 AUPRC), prioritizing treatment factors like steroid use. However, the DNN relied more on fixed patient factors like demographics and symptoms in aspect to SHAP value importance. Since treatment patterns vary between hospitals, the DNN may be more generalizable than tree-based models (random forest, gradient boost model). The results demonstrate accurate short-term forecasting of COVID-19 severity using routine clinical data. DNN models may balance predictive performance and generalizability better than other methods. Severity predictions by machine learning model could facilitate resource planning, like ICU arrangement and oxygen devices.
| S-EPMC10810421 | biostudies-literature
Project description:ImportanceThe influence of the COVID-19 pandemic on fertility rates has been suggested in the lay press and anticipated based on documented decreases in fertility and pregnancy rates during previous major societal and economic shifts. Anticipatory planning for birth rates is important for health care systems and government agencies to accurately estimate size of economy and model working and/or aging populations.ObjectiveTo use projection modeling based on electronic health care records in a large US university medical center to estimate changes in pregnancy and birth rates prior to and after the COVID-19 pandemic societal lockdowns.Design, setting, and participantsThis cohort study included all pregnancy episodes within a single US academic health care system retrospectively from 2017 and modeled prospectively to 2021. Data were analyzed September 2021.ExposuresPre- and post-COVID-19 pandemic societal shutdown measures.Main outcomes and measuresThe primary outcome was number of new pregnancy episodes initiated within the health care system and use of those episodes to project birth volumes. Interrupted time series analysis was used to assess the degree to which COVID-19 societal changes may have factored into pregnancy episode volume. Potential reasons for the changes in volumes were compared with historical pregnancy volumes, including delays in starting prenatal care, interruptions in reproductive endocrinology and infertility services, and preterm birth rates.ResultsThis cohort study documented a steadily increasing number of pregnancy episodes over the study period, from 4100 pregnancies in 2017 to 4620 in 2020 (28 284 total pregnancies; median maternal [interquartile range] age, 30 [27-34] years; 18 728 [66.2%] White women, 3794 [13.4%] Black women; 2177 [7.7%] Asian women). A 14% reduction in pregnancy episode initiation was observed after the societal shutdown of the COVID-19 pandemic (risk ratio, 0.86; 95% CI, 0.79-0.92; P < .001). This decrease appeared to be due to a decrease in conceptions that followed the March 15 mandated COVID-19 pandemic societal shutdown. Prospective modeling of pregnancies currently suggests that a birth volume surge can be anticipated in summer 2021.Conclusions and relevanceThis cohort study using electronic medical record surveillance found an initial decline in births associated with the COVID-19 pandemic societal changes and an anticipated increase in birth volume. Future studies can further explore how pregnancy episode volume changes can be monitored and birth rates projected in real-time during major societal events.
| S-EPMC8176329 | biostudies-literature
Project description: Not available
| S-EPMC11335612 | biostudies-literature
Project description:Carfilzomib is a promising anticancer drug for relapsed/refractory multiple myeloma (RRMM). However, real-world evidence has only investigated the cardiovascular safety of carfilzomib, and there is a high demand for thorough safety evaluations. We aimed to comprehensively evaluate the risk of adverse events associated with carfilzomib in Korean patients with RRMM. We followed up with 138 matched patients with RRMM (69 KRd (carfilzomib, lenalidomide, and dexamethasone) and 69 Rd (lenalidomide and dexamethasone) users). A total of 12 adverse events were evaluated. More than 75% of adverse events occurred during the early cycle (1-6 cycles), and the incidence rate showed a tendency to decrease in the later cycle (7-12 and 13-18 cycles). Severities of most adverse events were evaluated as grade 1-2. The KRd regimen were related with significantly increased risks of dyspnea (adjusted HR (aHR) 2.27, 95% confidence interval (CI) 1.24-4.16), muscle spasm (aHR 5.12, 95% CI 1.05-24.9) and thrombocytopenia (aHR 1.84, 95% CI 1.10-3.06). Although the severities were low, carfilzomib has many side effects in treating RRMM; hence, findings on the patterns of its adverse events could lead to both effective and safe use of KRd therapy in real-world settings.
| S-EPMC9603615 | biostudies-literature
Project description:Clopidogrel efficacy is influenced by genetic variation of cytochrome P450 (CYP)2C19, however, few studies have considered patients who have a stroke. We used electronic medical records (EMRs) linked to a bioresource to examine real-world implications of clopidogrel pharmacogenetics in stroke. Patients hospitalized for any arterial thrombo-occlusive (ATO) event who subsequently redeemed clopidogrel prescriptions in the community were entered into the study (n = 651). During 24-month follow-up, the primary endpoint of recurrent ATO or death occurred in 299 patients (46%). CYP2C19*2 loss-of-function allele carriers had an increased risk (hazard ratio (HR) = 1.29; 95% confidence interval (CI) = 1.04-1.59; P = 0.019). In the ischemic stroke subgroup (n = 94), the estimate of risk was greater (HR = 2.23; 95% CI = 1.17-4.24; P = 0.015), which was further supported by a meta-analysis of available studies. In conclusion, we have demonstrated the clinical impact of CYP2C19*2 on clopidogrel efficacy using a purely EMR approach. This suggests that the risk in the ischemic stroke population may be particularly high.
| S-EPMC5813097 | biostudies-literature
Project description:PurposePatients with cancer often have compromised immune system which can lead to worse COVID-19 outcomes. The purpose of this study is to assess the association between COVID-19 outcomes and existing cancer-specific characteristics.Patients and methodsPatients aged 18 or older with laboratory-confirmed COVID-19 between June 1, 2020, and December 31, 2020, were identified (n = 314 004) from the Optum® de-identified COVID-19 Electronic Health Record (EHR) derived from more than 700 hospitals and 7000 clinics in the United States. To allow sufficient observational time, patients with less than one year of medical history in the EHR dataset before their COVID-19 tests were excluded (n = 42 365). Assessed COVID-19 outcomes including all-cause 30-day mortality, hospitalization, ICU admission, and ventilator use, which were compared using relative risks (RRs) according to cancer status and treatments.ResultsAmong 271 639 patients with COVID-19, 18 460 had at least one cancer diagnosis: 8034 with a history of cancer and 10 426 with newly diagnosed cancer within one year of COVID-19 infection. Patients with a cancer diagnosis were older and more likely to be male, white, Medicare beneficiaries, and have higher prevalences of chronic conditions. Cancer patients had higher risks for 30-day mortality (RR 1.07, 95% CI 1.01-1.14, P = 0.028) and hospitalization (RR 1.04, 95% CI 1.01-1.07, P = 0.006) but without significant differences in ICU admission and ventilator use compared to non-cancer patients. Recent cancer diagnoses were associated with higher risks for worse COVID-19 outcomes (RR for mortality 1.17, 95% CI 1.08-1.25, P<0.001 and RR for hospitalization 1.10, 95% CI 1.06-1.14, P<0.001), particularly among recent metastatic (stage IV), hematological, liver and lung cancers compared with the non-cancer group. Among COVID-19 patients with recent cancer diagnosis, mortality was associated with chemotherapy or radiation treatments within 3 months before COVID-19. Age, black patients, Medicare recipients, South geographic region, cardiovascular, diabetes, liver, and renal diseases were also associated with increased mortality.Conclusions and relevanceIndividuals with cancer had higher risks for 30-day mortality and hospitalization after SARS-CoV-2 infection compared to patients without cancer. More specifically, patients with a cancer diagnosis within 1 year and those receiving active treatment were more vulnerable to worse COVID-19 outcomes.
| S-EPMC9067885 | biostudies-literature