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1.
BMC Med Inform Decis Mak ; 21(1): 111, 2021 03 31.
Artículo en Inglés | MEDLINE | ID: mdl-33789660

RESUMEN

BACKGROUND: Diabetes is a medical and economic burden in the United States. In this study, a machine learning predictive model was developed to predict unplanned medical visits among patients with diabetes, and findings were used to design a clinical intervention in the sponsoring healthcare organization. This study presents a case study of how predictive analytics can inform clinical actions, and describes practical factors that must be incorporated in order to translate research into clinical practice. METHODS: Data were drawn from electronic medical records (EMRs) from a large healthcare organization in the Northern Plains region of the US, from adult (≥ 18 years old) patients with type 1 or type 2 diabetes who received care at least once during the 3-year period. A variety of machine-learning classification models were run using standard EMR variables as predictors (age, body mass index (BMI), systolic blood pressure (BP), diastolic BP, low-density lipoprotein, high-density lipoprotein (HDL), glycohemoglobin (A1C), smoking status, number of diagnoses and number of prescriptions). The best-performing model after cross-validation testing was analyzed to identify strongest predictors. RESULTS: The best-performing model was a linear-basis support vector machine, which achieved a balanced accuracy (average of sensitivity and specificity) of 65.7%. This model outperformed a conventional logistic regression by 0.4 percentage points. A sensitivity analysis identified BP and HDL as the strongest predictors, such that disrupting these variables with random noise decreased the model's overall balanced accuracy by 1.3 and 1.4 percentage points, respectively. These recommendations, along with stakeholder engagement, behavioral economics strategies, and implementation science principles helped to inform the design of a clinical intervention targeting behavioral changes. CONCLUSION: Our machine-learning predictive model more accurately predicted unplanned medical visits among patients with diabetes, relative to conventional models. Post-hoc analysis of the model was used for hypothesis generation, namely that HDL and BP are the strongest contributors to unplanned medical visits among patients with diabetes. These findings were translated into a clinical intervention now being piloted at the sponsoring healthcare organization. In this way, this predictive model can be used in moving from prediction to implementation and improved diabetes care management in clinical settings.


Asunto(s)
Diabetes Mellitus Tipo 2 , Adolescente , Adulto , Registros Electrónicos de Salud , Humanos , Modelos Logísticos , Aprendizaje Automático , Máquina de Vectores de Soporte
2.
Artículo en Inglés | MEDLINE | ID: mdl-33799968

RESUMEN

The most accurate prognostic approach for follicular lymphoma (FL), progression of disease at 24 months (POD24), requires two years' observation after initiating first-line therapy (L1) to predict outcomes. We applied machine learning to structured electronic health record (EHR) data to predict individual survival at L1 initiation. We grouped 523 observations and 1933 variables from a nationwide cohort of FL patients diagnosed 2006-2014 in the Veterans Health Administration into traditionally used prognostic variables ("curated"), commonly measured labs ("labs"), and International Classification of Diseases diagnostic codes ("ICD") sets. We compared performance of random survival forests (RSF) vs. traditional Cox model using four datasets: curated, curated + labs, curated + ICD, and curated + ICD + labs, also using Cox on curated + POD24. We evaluated variable importance and partial dependence plots with area under the receiver operating characteristic curve (AUC). RSF with curated + labs performed best, with mean AUC 0.73 (95% CI: 0.71-0.75). It approximated, but did not surpass, Cox with POD24 (mean AUC 0.74 [95% CI: 0.71-0.77]). RSF using EHR data achieved better performance than traditional prognostic variables, setting the foundation for the incorporation of our algorithm into the EHR. It also provides for possible future scenarios in which clinicians could be provided an EHR-based tool which approximates the predictive ability of the most accurate known indicator, using information available 24 months earlier.


Asunto(s)
Linfoma Folicular , Veteranos , Registros Electrónicos de Salud , Humanos , Clasificación Internacional de Enfermedades , Linfoma Folicular/diagnóstico , Aprendizaje Automático
3.
Artículo en Inglés | MEDLINE | ID: mdl-33800307

RESUMEN

Implementation of an electronic medical record (EMR) is a significant workplace event for nurses in hospitals. Understanding nurses' key concerns can inform EMR implementation and ongoing optimisation strategies to increase the likelihood of nurses remaining in the nursing workforce. This concurrent mixed-methods study included surveys from 540 nurses (response rate 15.5%), and interviews with 63 nurses to examine their perceptions of using a new EMR prior to implementation at a single healthcare organisation. Survey findings revealed 32.2% (n = 174) of nurses reported low well-being scores and 28.7% (n = 155) were experiencing burnout symptoms. In contrast, 40.3% (n = 216) of nurses reported high work satisfaction, 62.3% (n = 334) had high intentions of staying in their role, and 34.3% (n = 185) were engaged in their work. Nearly half (n = 250, 46.3%) reported intrinsic motivation towards EMR use. Thematic analysis of focus group interviews revealed two themes, each with three subthemes: (1) Us and Them, detailed the juxtaposition between nurses' professional role and anticipated changes imposed on them and their work with the EMR implementation; and (2) Stuck in the middle, revealed nurses' expectations and anticipations about how the EMR may affect the quality of nurse-patient relationships. In conclusion, anticipation of the EMR implementation emerged as a stressor for nursing staff, with some groups of nurses particularly vulnerable to negative consequences to their well-being.


Asunto(s)
Motivación , Personal de Enfermería en Hospital , Registros Electrónicos de Salud , Humanos , Satisfacción en el Trabajo , Encuestas y Cuestionarios , Lugar de Trabajo
4.
BMC Med Inform Decis Mak ; 21(1): 112, 2021 04 03.
Artículo en Inglés | MEDLINE | ID: mdl-33812369

RESUMEN

BACKGROUND: Many patients with atrial fibrillation (AF) remain undiagnosed despite availability of interventions to reduce stroke risk. Predictive models to date are limited by data requirements and theoretical usage. We aimed to develop a model for predicting the 2-year probability of AF diagnosis and implement it as proof-of-concept (POC) in a production electronic health record (EHR). METHODS: We used a nested case-control design using data from the Indiana Network for Patient Care. The development cohort came from 2016 to 2017 (outcome period) and 2014 to 2015 (baseline). A separate validation cohort used outcome and baseline periods shifted 2 years before respective development cohort times. Machine learning approaches were used to build predictive model. Patients ≥ 18 years, later restricted to age ≥ 40 years, with at least two encounters and no AF during baseline, were included. In the 6-week EHR prospective pilot, the model was silently implemented in the production system at a large safety-net urban hospital. Three new and two previous logistic regression models were evaluated using receiver-operating characteristics. Number, characteristics, and CHA2DS2-VASc scores of patients identified by the model in the pilot are presented. RESULTS: After restricting age to ≥ 40 years, 31,474 AF cases (mean age, 71.5 years; female 49%) and 22,078 controls (mean age, 59.5 years; female 61%) comprised the development cohort. A 10-variable model using age, acute heart disease, albumin, body mass index, chronic obstructive pulmonary disease, gender, heart failure, insurance, kidney disease, and shock yielded the best performance (C-statistic, 0.80 [95% CI 0.79-0.80]). The model performed well in the validation cohort (C-statistic, 0.81 [95% CI 0.8-0.81]). In the EHR pilot, 7916/22,272 (35.5%; mean age, 66 years; female 50%) were identified as higher risk for AF; 5582 (70%) had CHA2DS2-VASc score ≥ 2. CONCLUSIONS: Using variables commonly available in the EHR, we created a predictive model to identify 2-year risk of developing AF in those previously without diagnosed AF. Successful POC implementation of the model in an EHR provided a practical strategy to identify patients who may benefit from interventions to reduce their stroke risk.


Asunto(s)
Fibrilación Atrial , Accidente Cerebrovascular , Adulto , Anciano , Fibrilación Atrial/diagnóstico , Fibrilación Atrial/epidemiología , Registros Electrónicos de Salud , Femenino , Humanos , Indiana , Persona de Mediana Edad , Valor Predictivo de las Pruebas , Estudios Prospectivos , Medición de Riesgo , Factores de Riesgo , Accidente Cerebrovascular/diagnóstico , Accidente Cerebrovascular/epidemiología
5.
BMC Med Inform Decis Mak ; 21(1): 113, 2021 04 03.
Artículo en Inglés | MEDLINE | ID: mdl-33812388

RESUMEN

BACKGROUND: Ensuring data is of appropriate quality is essential for the secondary use of electronic health records (EHRs) in research and clinical decision support. An effective method of data quality assessment (DQA) is automating data quality rules (DQRs) to replace the time-consuming, labor-intensive manual process of creating DQRs, which is difficult to guarantee standard and comparable DQA results. This paper presents a case study of automatically creating DQRs based on openEHR archetypes in a Chinese hospital to investigate the feasibility and challenges of automating DQA for EHR data. METHODS: The clinical data repository (CDR) of the Shanxi Dayi Hospital is an archetype-based relational database. Four steps are undertaken to automatically create DQRs in this CDR database. First, the keywords and features relevant to DQA of archetypes were identified via mapping them to a well-established DQA framework, Kahn's DQA framework. Second, the templates of DQRs in correspondence with these identified keywords and features were created in the structured query language (SQL). Third, the quality constraints were retrieved from archetypes. Fourth, these quality constraints were automatically converted to DQRs according to the pre-designed templates and mapping relationships of archetypes and data tables. We utilized the archetypes of the CDR to automatically create DQRs to meet quality requirements of the Chinese Application-Level Ranking Standard for EHR Systems (CARSES) and evaluated their coverage by comparing with expert-created DQRs. RESULTS: We used 27 archetypes to automatically create 359 DQRs. 319 of them are in agreement with the expert-created DQRs, covering 84.97% (311/366) requirements of the CARSES. The auto-created DQRs had varying levels of coverage of the four quality domains mandated by the CARSES: 100% (45/45) of consistency, 98.11% (208/212) of completeness, 54.02% (57/87) of conformity, and 50% (11/22) of timeliness. CONCLUSION: It's feasible to create DQRs automatically based on openEHR archetypes. This study evaluated the coverage of the auto-created DQRs to a typical DQA task of Chinese hospitals, the CARSES. The challenges of automating DQR creation were identified, such as quality requirements based on semantic, and complex constraints of multiple elements. This research can enlighten the exploration of DQR auto-creation and contribute to the automatic DQA.


Asunto(s)
Sistemas de Apoyo a Decisiones Clínicas , Registros Electrónicos de Salud , Exactitud de los Datos , Humanos , Lenguaje , Semántica
6.
BMC Med Inform Decis Mak ; 21(1): 115, 2021 04 05.
Artículo en Inglés | MEDLINE | ID: mdl-33820531

RESUMEN

BACKGROUND: Screening carotid B-mode ultrasonography is a frequently used method to detect subjects with carotid atherosclerosis (CAS). Due to the asymptomatic progression of most CAS patients, early identification is challenging for clinicians, and it may trigger ischemic stroke. Recently, machine learning has shown a strong ability to classify data and a potential for prediction in the medical field. The combined use of machine learning and the electronic health records of patients could provide clinicians with a more convenient and precise method to identify asymptomatic CAS. METHODS: Retrospective cohort study using routine clinical data of medical check-up subjects from April 19, 2010 to November 15, 2019. Six machine learning models (logistic regression [LR], random forest [RF], decision tree [DT], eXtreme Gradient Boosting [XGB], Gaussian Naïve Bayes [GNB], and K-Nearest Neighbour [KNN]) were used to predict asymptomatic CAS and compared their predictability in terms of the area under the receiver operating characteristic curve (AUCROC), accuracy (ACC), and F1 score (F1). RESULTS: Of the 18,441 subjects, 6553 were diagnosed with asymptomatic CAS. Compared to DT (AUCROC 0.628, ACC 65.4%, and F1 52.5%), the other five models improved prediction: KNN + 7.6% (0.704, 68.8%, and 50.9%, respectively), GNB + 12.5% (0.753, 67.0%, and 46.8%, respectively), XGB + 16.0% (0.788, 73.4%, and 55.7%, respectively), RF + 16.6% (0.794, 74.5%, and 56.8%, respectively) and LR + 18.1% (0.809, 74.7%, and 59.9%, respectively). The highest achieving model, LR predicted 1045/1966 cases (sensitivity 53.2%) and 3088/3566 non-cases (specificity 86.6%). A tenfold cross-validation scheme further verified the predictive ability of the LR. CONCLUSIONS: Among machine learning models, LR showed optimal performance in predicting asymptomatic CAS. Our findings set the stage for an early automatic alarming system, allowing a more precise allocation of CAS prevention measures to individuals probably to benefit most.


Asunto(s)
Enfermedades de las Arterias Carótidas , Registros Electrónicos de Salud , Teorema de Bayes , Enfermedades de las Arterias Carótidas/diagnóstico por imagen , Enfermedades de las Arterias Carótidas/epidemiología , Humanos , Aprendizaje Automático , Estudios Retrospectivos
7.
BMC Med Inform Decis Mak ; 21(1): 120, 2021 04 07.
Artículo en Inglés | MEDLINE | ID: mdl-33827555

RESUMEN

BACKGROUND: Accurate, coded problem lists are valuable for data reuse, including clinical decision support and research. However, healthcare providers frequently modify coded diagnoses by including or removing common contextual properties in free-text diagnosis descriptions: uncertainty (suspected glaucoma), laterality (left glaucoma) and temporality (glaucoma 2002). These contextual properties could cause a difference in meaning between underlying diagnosis codes and modified descriptions, inhibiting data reuse. We therefore aimed to develop and evaluate an algorithm to identify these contextual properties. METHODS: A rule-based algorithm called UnLaTem (Uncertainty, Laterality, Temporality) was developed using a single-center dataset, including 288,935 diagnosis descriptions, of which 73,280 (25.4%) were modified by healthcare providers. Internal validation of the algorithm was conducted with an independent sample of 980 unique records. A second validation of the algorithm was conducted with 996 records from a Dutch multicenter dataset including 175,210 modified descriptions of five hospitals. Two researchers independently annotated the two validation samples. Performance of the algorithm was determined using means of the recall and precision of the validation samples. The algorithm was applied to the multicenter dataset to determine the actual prevalence of the contextual properties within the modified descriptions per specialty. RESULTS: For the single-center dataset recall (and precision) for removal of uncertainty, uncertainty, laterality and temporality respectively were 100 (60.0), 99.1 (89.9), 100 (97.3) and 97.6 (97.6). For the multicenter dataset for removal of uncertainty, uncertainty, laterality and temporality it was 57.1 (88.9), 86.3 (88.9), 99.7 (93.5) and 96.8 (90.1). Within the modified descriptions of the multicenter dataset, 1.3% contained removal of uncertainty, 9.9% uncertainty, 31.4% laterality and 9.8% temporality. CONCLUSIONS: We successfully developed a rule-based algorithm named UnLaTem to identify contextual properties in Dutch modified diagnosis descriptions. UnLaTem could be extended with more trigger terms, new rules and the recognition of term order to increase the performance even further. The algorithm's rules are available as additional file 2. Implementing UnLaTem in Dutch hospital systems can improve precision of information retrieval and extraction from diagnosis descriptions, which can be used for data reuse purposes such as decision support and research.


Asunto(s)
Registros Electrónicos de Salud , Glaucoma , Algoritmos , Humanos , Almacenamiento y Recuperación de la Información , Incertidumbre
8.
Stomatologiia (Mosk) ; 100(2): 18-23, 2021.
Artículo en Ruso | MEDLINE | ID: mdl-33874655

RESUMEN

The implementation of the unified state information system project in the health sector involves the creation of a single digital space in health care with the accession of medical organizations of all forms of ownership until 2024. Digitalization in dentistry will undoubtedly provide the doctor with additional resources and opportunities, but only if the correct implementation of digital technologies and techniques. Creating an electronic map of a dental patient is one of the most urgent and complex tasks since it involves the formation of completely new principles of the dentist's work. The study allowed creating criteria for establishing new e-cards for dental patients basing on assessment of problems associated with conventional paper medical records and taking into account expert assessments of medical documentation in judicial-medical examinations, examinations of quality of medical services and internal control of the quality and safety of medical activity.


Asunto(s)
Documentación , Registros Electrónicos de Salud , Humanos
9.
Zhongguo Zhen Jiu ; 41(3): 355-8, 2021 Mar 12.
Artículo en Chino | MEDLINE | ID: mdl-33798325

RESUMEN

OBJECTIVE: To explore the rule of point selection in treatment of cerebral palsy with acupuncture in preschool children. METHODS: Based on the electronic medical records of Xi'an Encephalopathy Hospital of TCM, through structuring medical record text, acupuncture prescriptions were extracted. Using the data mining tools of the ancient and modern medical record cloud platform V2.2.3 and the clinical effective prescription and molecular mechanism analysis system of traditional Chinese medicine V2.0, the cluster analysis and complex network analysis were conducted on acupuncture prescriptions. RESULTS: Of 1584 acupuncture prescriptions for cerebral palsy in children, there were 84 acupoints and stimulating areas of scalp acupuncture, of which, foot-motor-sensory area, balance area and Sanyinjiao (SP 6) were the top 3 acupoints with the highest use rate. With cluster analysis, 5 groups of common supplementary acupoints and stimulating areas were found, named, Weizhong (BL 40) and Waiguan (TE 5), Shousanli (LI 10), Xingjian (LR 2), Xuanzhong (GB 39) and Chengfu (BL 36), foot-motor-sensory area, balance area and Sanyinjiao (SP 6), Xuehai (SP 10) and Fenglong (ST 40), Pishu (BL 20), motor area and Yanglingquan (GB 34). With complex network analysis on core prescriptions, 13 core acupoints and stimulating areas of scalp acupuncture were obtained, including 3 core main points, i.e. Sanyinjiao (SP 6), balance area and foot-motor-sensory area and 10 sub-core points, i.e. Taichong (LR 3), motor area, Xuehai (SP 10), Ganshu (BL 18), Pishu (BL 20), Yanglingquan (GB 34), Sishencong (EX-HN 1), Baihui (GV 20), Fengchi (GB 20) and Shenshu (BL 23). CONCLUSION: In treatment of acupuncture for cerebral palsy in preschool children, the core prescriptions reveal the simultaneous treatment of exterior and interior, the mutual regulation of yin and yang and the combination of acupoints with stimulating ares of scalp acupuncture for both encephalopathy and paralysis.


Asunto(s)
Terapia por Acupuntura , Parálisis Cerebral , Puntos de Acupuntura , Parálisis Cerebral/terapia , Preescolar , Minería de Datos , Registros Electrónicos de Salud , Humanos
10.
Dermatol Online J ; 27(3)2021 Mar 15.
Artículo en Inglés | MEDLINE | ID: mdl-33865273

RESUMEN

OBJECTIVE: We update and expand our 2010 article in this journal, Patient safety in dermatology: A review of the literature [4][DH1]. METHODS: PubMed at the National Center for Biotechnology Information (NCBI), United States National Library of Medicine (NLM) was searched September 2019 for English language articles published between 2009 and 2019 concerning patient safety and medical error in dermatology. Potentially relevant articles and communications were critically evaluated by the authors with selected references from 2020 added to include specific topics: medication errors, diagnostic errors including telemedicine, office-based surgery, wrong-site procedures, infections including COVID-19, falls, laser safety, scope of practice, and electronic health records. SUMMARY: Hospitals and clinics are adopting the methods of high-reliability organizations to identify and change ineffective practice patterns. Although systems issues are emphasized in patient safety, people are critically important to effective teamwork and leadership. Advancements in procedural and cosmetic dermatology, organizational and clinical guidelines, and the revolution in information technology and electronic health records have introduced new sources of potential error. CONCLUSION: Despite the growing number of dermatologic patient safety studies, our review supports a continuing need for further studies and reports to reduce the number of preventable errors and provide optimal care.


Asunto(s)
Dermatología/estadística & datos numéricos , Seguridad del Paciente , Accidentes por Caídas/prevención & control , Accidentes por Caídas/estadística & datos numéricos , /transmisión , Infección Hospitalaria/prevención & control , Fármacos Dermatológicos/efectos adversos , Errores Diagnósticos/prevención & control , Errores Diagnósticos/estadística & datos numéricos , Documentación , Registros Electrónicos de Salud , Fuego , Humanos , Control de Infecciones , Rayos Láser/efectos adversos , Errores Médicos/prevención & control , Errores Médicos/estadística & datos numéricos , Errores de Medicación/prevención & control , Errores de Medicación/estadística & datos numéricos , Equipo de Protección Personal , Factores de Riesgo
12.
Medicine (Baltimore) ; 100(16): e25625, 2021 Apr 23.
Artículo en Inglés | MEDLINE | ID: mdl-33879735

RESUMEN

ABSTRACT: Medical data sharing, anti-tampering, and leakage prevention have always been severe problems that plagued the pharmaceutical industry. When a patient is referred, he often cannot provide information about previous visits because the medical information of each hospital cannot be shared in most cases, but only through Medical records, test sheets, and other easily lost paper information are used to share some medical information. At the same time, patient medical information is easily leaked, and the medical information provided in the event of a medical dispute cannot guarantee authenticity and impartiality. This article designs a consortium medical blockchain system based on a Possible Byzantine Fault Tolerance algorithm. This system is a medical system that is maintained and shared by multiple nodes and can prevent medical data from being tampered with or leaked. It can be used to solve these medical problems. Compared with the existing medical blockchain system, this system has certain advantages and better applicability.


Asunto(s)
Cadena de Bloques/normas , Redes de Comunicación de Computadores/normas , Registros Electrónicos de Salud/normas , Difusión de la Información/métodos , Algoritmos , Consenso , Humanos , Mejoramiento de la Calidad
13.
BMC Med Inform Decis Mak ; 21(Suppl 1): 134, 2021 04 22.
Artículo en Inglés | MEDLINE | ID: mdl-33888098

RESUMEN

BACKGROUND: Deep learning algorithms significantly improve the accuracy of pathological image classification, but the accuracy of breast cancer classification using only single-mode pathological images still cannot meet the needs of clinical practice. Inspired by the real scenario of pathologists reading pathological images for diagnosis, we integrate pathological images and structured data extracted from clinical electronic medical record (EMR) to further improve the accuracy of breast cancer classification. METHODS: In this paper, we propose a new richer fusion network for the classification of benign and malignant breast cancer based on multimodal data. To make pathological image can be integrated more sufficient with structured EMR data, we proposed a method to extract richer multilevel feature representation of the pathological image from multiple convolutional layers. Meanwhile, to minimize the information loss for each modality before data fusion, we use the denoising autoencoder as a way to increase the low-dimensional structured EMR data to high-dimensional, instead of reducing the high-dimensional image data to low-dimensional before data fusion. In addition, denoising autoencoder naturally generalizes our method to make the accurate prediction with partially missing structured EMR data. RESULTS: The experimental results show that the proposed method is superior to the most advanced method in terms of the average classification accuracy (92.9%). In addition, we have released a dataset containing structured data from 185 patients that were extracted from EMR and 3764 paired pathological images of breast cancer, which can be publicly downloaded from http://ear.ict.ac.cn/?page_id=1663 . CONCLUSIONS: We utilized a new richer fusion network to integrate highly heterogeneous data to leverage the structured EMR data to improve the accuracy of pathological image classification. Therefore, the application of automatic breast cancer classification algorithms in clinical practice becomes possible. Due to the generality of the proposed fusion method, it can be straightforwardly extended to the fusion of other structured data and unstructured data.


Asunto(s)
Neoplasias de la Mama , Algoritmos , Mama , Neoplasias de la Mama/diagnóstico por imagen , Registros Electrónicos de Salud , Humanos , Redes Neurales de la Computación
14.
JAMA Netw Open ; 4(4): e217498, 2021 04 01.
Artículo en Inglés | MEDLINE | ID: mdl-33885771

RESUMEN

Importance: Acute ischemic stroke (AIS) is a known neurological complication in patients with respiratory symptoms of COVID-19 infection. However, AIS has not been described as a late sequelae in patients without respiratory symptoms of COVID-19. Objective: To assess AIS experienced by adults 50 years or younger in the convalescent phase of asymptomatic COVID-19 infection. Design, Setting, and Participants: This case series prospectively identified consecutive male patients who received care for AIS from public health hospitals in Singapore between May 21, 2020, and October 14, 2020. All of these patients had laboratory-confirmed asymptomatic COVID-19 infection based on a positive SARS-CoV-2 serological (antibodies) test result. These patients were individuals from South Asian countries (India and Bangladesh) who were working in Singapore and living in dormitories. The total number of COVID-19 cases (54 485) in the worker dormitory population was the population at risk. Patients with ongoing respiratory symptoms or positive SARS-CoV-2 serological test results confirmed through reverse transcriptase-polymerase chain reaction nasopharyngeal swabs were excluded. Main Outcomes and Measures: Clinical course, imaging, and laboratory findings were retrieved from the electronic medical records of each participating hospital. The incidence rate of AIS in the case series was compared with that of a historical age-, sex-, and ethnicity-matched national cohort. Results: A total of 18 male patients, with a median (range) age of 41 (35-50) years and South Asian ethnicity, were included. The median (range) time from a positive serological test result to AIS was 54.5 (0-130) days. The median (range) National Institutes of Health Stroke Scale score was 5 (1-25). Ten patients (56%) presented with a large vessel occlusion, of whom 6 patients underwent intravenous thrombolysis and/or endovascular therapy. Only 3 patients (17%) had a possible cardiac source of embolus. The estimated annual incidence rate of AIS was 82.6 cases per 100 000 people in this study compared with 38.2 cases per 100 000 people in the historical age-, sex-, and ethnicity-matched cohort (rate ratio, 2.16; 95% CI, 1.36-3.48; P < .001). Conclusions and Relevance: This case series suggests that the risk for AIS is higher in adults 50 years or younger during the convalescent period of a COVID-19 infection without respiratory symptoms. Acute ischemic stroke could be part of the next wave of complications of COVID-19, and stroke units should be on alert and use serological testing, especially in younger patients or in the absence of traditional risk factors.


Asunto(s)
Infecciones Asintomáticas/epidemiología , Trombectomía/métodos , Terapia Trombolítica/métodos , Adulto , /diagnóstico , /métodos , Convalecencia , Registros Electrónicos de Salud/estadística & datos numéricos , Procedimientos Endovasculares/métodos , Humanos , Incidencia , /etnología , Masculino , Persona de Mediana Edad , Evaluación de Procesos y Resultados en Atención de Salud , Factores de Riesgo , /patogenicidad , Singapur/epidemiología , Migrantes/estadística & datos numéricos
15.
BMJ ; 373: n826, 2021 04 07.
Artículo en Inglés | MEDLINE | ID: mdl-33827854

RESUMEN

OBJECTIVE: To describe a novel England-wide electronic health record (EHR) resource enabling whole population research on covid-19 and cardiovascular disease while ensuring data security and privacy and maintaining public trust. DESIGN: Data resource comprising linked person level records from national healthcare settings for the English population, accessible within NHS Digital's new trusted research environment. SETTING: EHRs from primary care, hospital episodes, death registry, covid-19 laboratory test results, and community dispensing data, with further enrichment planned from specialist intensive care, cardiovascular, and covid-19 vaccination data. PARTICIPANTS: 54.4 million people alive on 1 January 2020 and registered with an NHS general practitioner in England. MAIN MEASURES OF INTEREST: Confirmed and suspected covid-19 diagnoses, exemplar cardiovascular conditions (incident stroke or transient ischaemic attack and incident myocardial infarction) and all cause mortality between 1 January and 31 October 2020. RESULTS: The linked cohort includes more than 96% of the English population. By combining person level data across national healthcare settings, data on age, sex, and ethnicity are complete for around 95% of the population. Among 53.3 million people with no previous diagnosis of stroke or transient ischaemic attack, 98 721 had a first ever incident stroke or transient ischaemic attack between 1 January and 31 October 2020, of which 30% were recorded only in primary care and 4% only in death registry records. Among 53.2 million people with no previous diagnosis of myocardial infarction, 62 966 had an incident myocardial infarction during follow-up, of which 8% were recorded only in primary care and 12% only in death registry records. A total of 959 470 people had a confirmed or suspected covid-19 diagnosis (714 162 in primary care data, 126 349 in hospital admission records, 776 503 in covid-19 laboratory test data, and 50 504 in death registry records). Although 58% of these were recorded in both primary care and covid-19 laboratory test data, 15% and 18%, respectively, were recorded in only one. CONCLUSIONS: This population-wide resource shows the importance of linking person level data across health settings to maximise completeness of key characteristics and to ascertain cardiovascular events and covid-19 diagnoses. Although this resource was initially established to support research on covid-19 and cardiovascular disease to benefit clinical care and public health and to inform healthcare policy, it can broaden further to enable a wide range of research.


Asunto(s)
/epidemiología , Enfermedades Cardiovasculares/epidemiología , Registros Electrónicos de Salud , Registro Médico Coordinado , Adolescente , Adulto , Anciano , Enfermedades Cardiovasculares/diagnóstico , Niño , Preescolar , Estudios de Cohortes , Inglaterra/epidemiología , Femenino , Hospitalización/estadística & datos numéricos , Humanos , Lactante , Recién Nacido , Masculino , Persona de Mediana Edad , Atención Primaria de Salud/estadística & datos numéricos , Adulto Joven
16.
J Prim Care Community Health ; 12: 21501327211008050, 2021.
Artículo en Inglés | MEDLINE | ID: mdl-33829916

RESUMEN

BACKGROUND AND OBJECTIVE: Epidemiological data obtained during the ongoing SARS-CoV-2 pandemic suggests that COVID-19 mortality has specific age and gender associations. However, limited epidemiological studies explored specific populational risk factors, including comorbidities, and patient clinical characteristics. The main aim of our retrospective cohort study was to analyze associations between age, gender, and comorbidities in deceased COVID-19 patients. MATERIALS AND METHODS: A retrospective cohort analysis was performed to assess significant risk factors in adult patients deceased from COVID-19 infection by evaluating Electronic Medical Records and post-mortem analysis in COVID-19 patients deceased between April 2020 to October 2020. All patients underwent post-mortem evaluation along with medical history analysis, including data on disease duration, hospitalization, and clinical peculiarities. RESULTS: Medical records of 1487 COVID-19 patients revealed that the prevalence of males was higher (by 23%) than females; the median age for males was 71 years of age whereas for females it was 78. The most prevalent comorbid pathologies were: hypertension, obesity, diabetes, and cancer. Males are at significantly increased risk of lethal outcome, even in younger age groups, with comorbid conditions. CONCLUSION: The study concluded that comorbidities, such as hypertension, obesity, diabetes, cancer are the most important risk factors for comorbid mortality in COVID-19 patients. In addition to lung damage, multiple organ dysfunctions may be a crucial reason for COVID-19 induced death. Special precautions, such as early hospitalization, increased monitoring, and preventative tactics should be taken for at-risk patients.


Asunto(s)
/epidemiología , Diabetes Mellitus/epidemiología , Hipertensión/epidemiología , Neoplasias/epidemiología , Obesidad/epidemiología , Factores de Edad , Anciano , Anciano de 80 o más Años , Autopsia , Estudios de Cohortes , Comorbilidad , Registros Electrónicos de Salud , Femenino , Humanos , Masculino , Persona de Mediana Edad , Estudios Retrospectivos , Federación de Rusia/epidemiología , Factores Sexuales
17.
Lancet Psychiatry ; 8(5): 416-427, 2021 05.
Artículo en Inglés | MEDLINE | ID: mdl-33836148

RESUMEN

BACKGROUND: Neurological and psychiatric sequelae of COVID-19 have been reported, but more data are needed to adequately assess the effects of COVID-19 on brain health. We aimed to provide robust estimates of incidence rates and relative risks of neurological and psychiatric diagnoses in patients in the 6 months following a COVID-19 diagnosis. METHODS: For this retrospective cohort study and time-to-event analysis, we used data obtained from the TriNetX electronic health records network (with over 81 million patients). Our primary cohort comprised patients who had a COVID-19 diagnosis; one matched control cohort included patients diagnosed with influenza, and the other matched control cohort included patients diagnosed with any respiratory tract infection including influenza in the same period. Patients with a diagnosis of COVID-19 or a positive test for SARS-CoV-2 were excluded from the control cohorts. All cohorts included patients older than 10 years who had an index event on or after Jan 20, 2020, and who were still alive on Dec 13, 2020. We estimated the incidence of 14 neurological and psychiatric outcomes in the 6 months after a confirmed diagnosis of COVID-19: intracranial haemorrhage; ischaemic stroke; parkinsonism; Guillain-Barré syndrome; nerve, nerve root, and plexus disorders; myoneural junction and muscle disease; encephalitis; dementia; psychotic, mood, and anxiety disorders (grouped and separately); substance use disorder; and insomnia. Using a Cox model, we compared incidences with those in propensity score-matched cohorts of patients with influenza or other respiratory tract infections. We investigated how these estimates were affected by COVID-19 severity, as proxied by hospitalisation, intensive therapy unit (ITU) admission, and encephalopathy (delirium and related disorders). We assessed the robustness of the differences in outcomes between cohorts by repeating the analysis in different scenarios. To provide benchmarking for the incidence and risk of neurological and psychiatric sequelae, we compared our primary cohort with four cohorts of patients diagnosed in the same period with additional index events: skin infection, urolithiasis, fracture of a large bone, and pulmonary embolism. FINDINGS: Among 236 379 patients diagnosed with COVID-19, the estimated incidence of a neurological or psychiatric diagnosis in the following 6 months was 33·62% (95% CI 33·17-34·07), with 12·84% (12·36-13·33) receiving their first such diagnosis. For patients who had been admitted to an ITU, the estimated incidence of a diagnosis was 46·42% (44·78-48·09) and for a first diagnosis was 25·79% (23·50-28·25). Regarding individual diagnoses of the study outcomes, the whole COVID-19 cohort had estimated incidences of 0·56% (0·50-0·63) for intracranial haemorrhage, 2·10% (1·97-2·23) for ischaemic stroke, 0·11% (0·08-0·14) for parkinsonism, 0·67% (0·59-0·75) for dementia, 17·39% (17·04-17·74) for anxiety disorder, and 1·40% (1·30-1·51) for psychotic disorder, among others. In the group with ITU admission, estimated incidences were 2·66% (2·24-3·16) for intracranial haemorrhage, 6·92% (6·17-7·76) for ischaemic stroke, 0·26% (0·15-0·45) for parkinsonism, 1·74% (1·31-2·30) for dementia, 19·15% (17·90-20·48) for anxiety disorder, and 2·77% (2·31-3·33) for psychotic disorder. Most diagnostic categories were more common in patients who had COVID-19 than in those who had influenza (hazard ratio [HR] 1·44, 95% CI 1·40-1·47, for any diagnosis; 1·78, 1·68-1·89, for any first diagnosis) and those who had other respiratory tract infections (1·16, 1·14-1·17, for any diagnosis; 1·32, 1·27-1·36, for any first diagnosis). As with incidences, HRs were higher in patients who had more severe COVID-19 (eg, those admitted to ITU compared with those who were not: 1·58, 1·50-1·67, for any diagnosis; 2·87, 2·45-3·35, for any first diagnosis). Results were robust to various sensitivity analyses and benchmarking against the four additional index health events. INTERPRETATION: Our study provides evidence for substantial neurological and psychiatric morbidity in the 6 months after COVID-19 infection. Risks were greatest in, but not limited to, patients who had severe COVID-19. This information could help in service planning and identification of research priorities. Complementary study designs, including prospective cohorts, are needed to corroborate and explain these findings. FUNDING: National Institute for Health Research (NIHR) Oxford Health Biomedical Research Centre.


Asunto(s)
Gripe Humana , Trastornos Mentales , Enfermedades del Sistema Nervioso , Infecciones del Sistema Respiratorio , /complicaciones , /fisiopatología , Estudios de Cohortes , Registros Electrónicos de Salud/estadística & datos numéricos , Femenino , Salud Global/estadística & datos numéricos , Humanos , Incidencia , Gripe Humana/complicaciones , Gripe Humana/epidemiología , Masculino , Trastornos Mentales/clasificación , Trastornos Mentales/diagnóstico , Trastornos Mentales/epidemiología , Trastornos Mentales/etiología , Persona de Mediana Edad , Enfermedades del Sistema Nervioso/clasificación , Enfermedades del Sistema Nervioso/diagnóstico , Enfermedades del Sistema Nervioso/epidemiología , Enfermedades del Sistema Nervioso/etiología , Evaluación de Procesos y Resultados en Atención de Salud , Modelos de Riesgos Proporcionales , Proyectos de Investigación , Infecciones del Sistema Respiratorio/complicaciones , Infecciones del Sistema Respiratorio/epidemiología , Medición de Riesgo/métodos , Factores de Riesgo , Índice de Severidad de la Enfermedad
20.
PLoS One ; 16(4): e0249920, 2021.
Artículo en Inglés | MEDLINE | ID: mdl-33857224

RESUMEN

OBJECTIVE: To establish whether one can build a mortality prediction model for COVID-19 patients based solely on demographics and comorbidity data that outperforms age alone. Such a model could be a precursor to implementing smart lockdowns and vaccine distribution strategies. METHODS: The training cohort comprised 2337 COVID-19 inpatients from nine hospitals in The Netherlands. The clinical outcome was death within 21 days of being discharged. The features were derived from electronic health records collected during admission. Three feature selection methods were used: LASSO, univariate using a novel metric, and pairwise (age being half of each pair). 478 patients from Belgium were used to test the model. All modeling attempts were compared against an age-only model. RESULTS: In the training cohort, the mortality group's median age was 77 years (interquartile range = 70-83), higher than the non-mortality group (median = 65, IQR = 55-75). The incidence of former/active smokers, male gender, hypertension, diabetes, dementia, cancer, chronic obstructive pulmonary disease, chronic cardiac disease, chronic neurological disease, and chronic kidney disease was higher in the mortality group. All stated differences were statistically significant after Bonferroni correction. LASSO selected eight features, novel univariate chose five, and pairwise chose none. No model was able to surpass an age-only model in the external validation set, where age had an AUC of 0.85 and a balanced accuracy of 0.77. CONCLUSION: When applied to an external validation set, we found that an age-only mortality model outperformed all modeling attempts (curated on www.covid19risk.ai) using three feature selection methods on 22 demographic and comorbid features.


Asunto(s)
/mortalidad , Factores de Edad , Anciano , Anciano de 80 o más Años , Bélgica/epidemiología , /epidemiología , Estudios de Cohortes , Control de Enfermedades Transmisibles , Comorbilidad , Registros Electrónicos de Salud , Femenino , Hospitalización , Humanos , Masculino , Persona de Mediana Edad , Países Bajos/epidemiología , Pronóstico , Medición de Riesgo , Factores de Riesgo , /aislamiento & purificación
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