6-month neurological and psychiatric outcomes in 236 379 survivors of COVID-19: a retrospective cohort study using electronic health records.
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ABSTRACT:
SUBMITTER: Taquet M
PROVIDER: S-EPMC8023694 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Psychiatric health records are highly sensitive data which requires special policy to maintain its privacy, without affecting data accessibility. The current authors reviewed social, ethical and legal underpinnings for psychiatric electronic health records (EHR), and suggests a policy to maintain privacy and confidentiality of the psychiatric data, without affecting data accessibility. The purpose of this policy brief is to discuss and provide alternatives regarding psychiatric electronic health records privacy and information access. The current policy applied in Jordan still immature to ensure high levels of reliability, as the psychiatric data is openly accessed to the non-specialized personnel. Sensitive personal data policy is recommended in this paper with developing overriding mechanisms to counteract obstacles to data accessibility.
| S-EPMC7031133 | biostudies-literature
Project description:electronic Medical Records & Genomics (eMERGE)
| PRJNA158491 | ENA
Project description:BackgroundSecond-line treatment for immune thrombocytopenia (ITP) is not well reported for patients treated in real-world clinical settings.ObjectiveThe purpose of this study was to compare outcomes of four second-line treatments for ITP.Patients/methodsIncluded adult patients had at least two medical records containing ITP diagnoses and second-line eltrombopag, romiplostim, rituximab, or splenectomy. Date of treatment initiation or splenectomy was set as index date, between July 1, 2008, and March 31, 2017. Patients had first-line corticosteroid or intravenous immune globulin treatment and continuous database activity from 6 months before to 12 months after index. Patient characteristics, treatment patterns, platelet counts, bleeding-related episodes (BREs), and thrombotic events (TEs) were compared by second-line treatment cohort.ResultsThe sample included 3332 patients (mean age, 60.5 years; 52.3% female): eltrombopag (5.8%), romiplostim (9.9%), rituximab (73.3%), and splenectomy (11.0%). Patients having splenectomy were younger, more likely female and commercially insured, and less likely to require a third line of treatment than medical regimen cohorts. Proportions of patients having treatment-free (≥180 days with no second-line index or rescue agent) periods varied significantly (P = .01) by regimen: 33% for eltrombopag, 23% for romiplostim, 26% for rituximab, and 17% for splenectomy. All regimens significantly improved platelet counts, while TE and BRE rates differed significantly (P = .03 and P = .01, respectively) when all treatment groups were compared.ConclusionsOver an average 7-year follow-up, all second-line regimens improved platelet counts, but eltrombopag yielded the highest proportion of patients with completely treatment-free periods of at least 180 days.
| S-EPMC7590333 | biostudies-literature
Project description:ObjectiveThough psychiatric illnesses have been associated with increased COVID-19 infection risk, limited information exists about the relationship between ADHD and COVID-19.MethodsUsing the TriNetX COVID-19 Research Network, we examined the impact of ADHD diagnosis and treatment on COVID-19 infection rates and outcomes.ResultsADHD patients had greater risk of COVID-19 (risk ratio (RR) 1.11, 95% CI [1.09, 1.12]). Increased risk was higher in females than males, and highest among Asian and Black patients. Within 60 days after COVID-19 diagnosis, ADHD patients had lower rates of hospitalization (RR 0.91, 95% CI [0.86, 0.96]) and mechanical ventilation (RR 0.69, 95% CI [0.58, 0.83]), and a nonsignificant reduced death rate (RR 0.65, 95% CI [0.42, 1.02]). Patients who recently received ADHD medication had higher rates of COVID-19 (RR 1.13; 95% CI [1.10, 1.15]).ConclusionADHD poses increased risk for COVID-19, but may reduce risk of severe outcomes. ADHD medications modestly impacted COVID-19 risk.
| S-EPMC9596686 | biostudies-literature
Project description:Persons living with HIV (PLWH) are significantly more likely to meet criteria for a psychiatric disorder compared to the general population. To our knowledge there are no prior studies that report rates of psychiatric diagnosis documentation in electronic medical records (EMRs) of PLWH. The goal for this study was to report the rates of a variety of psychiatric diagnoses among PLWH in electronic medical records. Participants (n?=?2,336) were enrolled in the Center for AIDS Research (CFAR) Longitudinal Database study at the University of Pennsylvania, Philadelphia, PA. Diagnostic codes were extracted from the EMR for depressive disorders, alcohol and substance use disorders, PTSD, sleep disorders, and adjustment disorders and were compared to rates from national epidemiological studies. Rates of Major Depressive Disorder in the EMR were comparable to prior reports on HIV-infected samples. In contrast, rates of PTSD, substance use disorders, alcohol use disorders, adjustment disorders and insomnia from the EMR were all markedly lower compared to national estimates for HIV-infected samples. While clinicians appropriately documented evidence of Major Depressive Disorder, other psychiatric comorbidities were largely overlooked. These findings suggest a potential bias in how clinicians either detect or document psychiatric disorders in PLWH.
| S-EPMC7354895 | biostudies-literature
Project description:BackgroundHepatorenal syndrome (HRS) is among the leading causes of hospitalization and mortality in patients with chronic liver disease.ObjectiveTo assess the HRS patient journey from preadmission to postdischarge to understand patient characteristics, disease progression, treatment patterns, and outcomes.MethodsWe conducted a retrospective study using real-world data from a nationwide electronic health record database (Cerner Health Facts, Kansas City, Missouri). We used ICD-9/10 diagnosis codes to identify patients hospitalized with HRS between January 1, 2009, and January 31, 2018. We assessed patient characteristics and history, clinical presentation, treatment, and outcomes. Regression analysis was conducted to assess the association between patient characteristics and survival while adjusting for demographic and clinical covariates.ResultsThe study included 3563 patients (62% men). Precipitants of HRS included gastrointestinal bleeding (18%), diuretics and infections (30%), and paracentesis (26%). Although 21% of patients had liver injury exclusively associated with alcohol use, 20% had hepatitis C, 8% had nonalcoholic steatohepatitis, and the etiology of the remainder (51%) was either some combination of conditions or unknown. A total of 42% of patients received vasopressors, including octreotide and midodrine (10%), other combinations of vasopressors (11%), or another single vasopressor (21%). In-hospital mortality was 34%, and 14% of patients were discharged to hospice. Regression analysis showed patients with acute-on-chronic liver failure had higher mortality in acute-on-chronic liver failure grades 1 (odds ratio = 1.59), 2 (odds ratio = 2.49), and 3 (odds ratio = 4.53) versus no acute-on-chronic liver failure. Among survivor patients, 38% were readmitted within 90 days of discharge; 23% of readmissions were HRS-related.ConclusionsThe HRS patient journey presented in this study highlights inconsistencies in, and provides insight into, associated hospital-based treatment strategies. A mortality rate of 34% along with a readmission rate of 23% associated with HRS-related complications warrant more disease awareness and effective treatment. Further research is needed to examine the interactions between the etiology of cirrhosis, precipitants, treatment, and outcomes. (Curr Ther Res Clin Exp. 2022; 82:XXX-XXX).
| S-EPMC8987804 | biostudies-literature
Project description:BackgroundHealthcare professionals are required to access, interpret and generate patient data in the digital environment, and use this information to deliver and optimise patient care. Healthcare students are rarely exposed to the technology, or given the opportunity to use this during their training, which can impact on the digital competence of the graduating workforce. In this study we set out to develop and define domains of competence and associated learning outcomes needed by healthcare graduates to commence working in a digital healthcare environment.MethodA National Working Group was established in the UK to integrate Electronic Patient Records (EPRs) into undergraduate education for healthcare students studying medicine, pharmacy, nursing and midwifery. The working group, comprising 12 academic institutions and representatives from NHS England, NHS Digital and EPR system providers, met to discuss and document key learning outcomes required for using EPRs in the healthcare environment. Outcomes were grouped into six key domains and refined by the group prior to external review by experts working in medical education or with EPRs.ResultsSix key domains of competence and associated learning outcomes were identified and defined. External expert review provided iterative refinement and amendment. The agreed domains were: 1) Digital Health: work as a practitioner in the digital healthcare environment; 2) Accessing Data: access and interpret patient data to inform clinical decision-making; 3) Communication: communicate effectively with healthcare professionals and patients in the digital environment; 4) Generating data: generate data for and about patients within the EPR; 5) Multidisciplinary working: work with healthcare professionals with and alongside EPRs; and 6) Monitoring and audit: monitor and improve the quality and safety of healthcare.ConclusionThe six domains of competence and associated learning outcomes can be used by academics to guide the integration of EPRs into undergraduate healthcare programmes. This is key to ensuring that the future healthcare workforce can work with and alongside EPRs.
| S-EPMC6341543 | 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
Project description:Aim: With the introduction of "Electronic Medical Record" (EMR) a wealth of digital data has become available. This provides a unique opportunity for exploring precedents for seclusion. This study explored the feasibility of text mining analysis in the EMR to eventually help reduce the use of seclusion in psychiatry. Methods: The texts in notes and reports of the EMR during 5 years on an acute and non-acute psychiatric ward were analyzed using a text mining application. A period of 14 days was selected before seclusion or for non-secluded patients, before discharge. The resulting concepts were analyzed using chi-square tests to assess which concepts had a significant higher or lower frequency than expected in the "seclusion" and "non-seclusion" categories. Results: Text mining led to an overview of 1,500 meaningful concepts. In the 14 day period prior to the event, 115 of these concepts had a significantly higher frequency in the seclusion category and 49 in the non-seclusion category. Analysis of the concepts from days 14 to 7 resulted in 54 concepts with a significantly higher frequency in the seclusion-category and 14 in the non-seclusion category. Conclusions: The resulting significant concepts are comparable to reasons for seclusion in literature. These results are "proof of concept". Analyzing text of reports in the EMR seems therefore promising as contribution to tools available for the prediction of seclusion. The next step is to build, train and test a model, before text mining can be part of an evidence-based clinical decision making tool.
| S-EPMC6470375 | biostudies-literature
Project description:BackgroundWith the prevalence of cardiovascular diseases increasing worldwide, early prediction and accurate assessment of heart failure (HF) risk are crucial to meet the clinical demand.ObjectiveOur study objective was to develop machine learning (ML) models based on real-world electronic health records to predict 1-year in-hospital mortality, use of positive inotropic agents, and 1-year all-cause readmission rate.MethodsFor this single-center study, we recruited patients with newly diagnosed HF hospitalized between December 2010 and August 2018 at the First Affiliated Hospital of Dalian Medical University (Liaoning Province, China). The models were constructed for a population set (90:10 split of data set into training and test sets) using 79 variables during the first hospitalization. Logistic regression, support vector machine, artificial neural network, random forest, and extreme gradient boosting models were investigated for outcome predictions.ResultsOf the 13,602 patients with HF enrolled in the study, 537 (3.95%) died within 1 year and 2779 patients (20.43%) had a history of use of positive inotropic agents. ML algorithms improved the performance of predictive models for 1-year in-hospital mortality (areas under the curve [AUCs] 0.92-1.00), use of positive inotropic medication (AUCs 0.85-0.96), and 1-year readmission rates (AUCs 0.63-0.96). A decision tree of mortality risk was created and stratified by single variables at levels of high-sensitivity cardiac troponin I (<0.068 μg/L), followed by percentage of lymphocytes (<14.688%) and neutrophil count (4.870×109/L).ConclusionsML techniques based on a large scale of clinical variables can improve outcome predictions for patients with HF. The mortality decision tree may contribute to guiding better clinical risk assessment and decision making.
| S-EPMC8094022 | biostudies-literature