Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 20 de 3.216
Filtrar
1.
MMWR Morb Mortal Wkly Rep ; 69(44): 1622-1624, 2020 Nov 06.
Artigo em Inglês | MEDLINE | ID: mdl-33151919

RESUMO

Preventive care or follow-up care have the potential to improve health outcomes, reduce disease in the population, and decrease health care costs in the long-term (1). Approximately one half of persons in the United States receive general recommended preventive services (2,3). Missed physician appointments can hinder the receipt of needed health care (4). With electronic health record (EHR) systems able to improve interaction and communication between patients and providers (5), electronic reminders are used to decrease missed care. These reminders can improve various types of preventive and follow-up care, such as immunizations (6) and cancer screening (7); however, computerized capability must exist to make use of these reminders. To examine this capability among U.S. office-based physicians, data from the National Electronic Health Records Survey (NEHRS) for 2017, the most recent data available, were analyzed. An estimated 64.7% of office-based physicians had computerized capability to identify patients who were due for preventive or follow-up care, with 72.9% of primary care physicians and 71.4% of physicians with an EHR system having this capability compared with surgeons (54.8%), nonprimary care physicians (58.5%), and physicians without an EHR system (23.4%). Having an EHR system is associated with the ability to send electronic reminders to increase receipt of preventive or follow-up care, which has been shown to improve patient health outcomes (8).


Assuntos
Assistência ao Convalescente , Registros Eletrônicos de Saúde/estatística & dados numéricos , Necessidades e Demandas de Serviços de Saúde , Consultórios Médicos/estatística & dados numéricos , Médicos/estatística & dados numéricos , Serviços Preventivos de Saúde , Sistemas de Alerta/estatística & dados numéricos , Adulto , Idoso , Idoso de 80 Anos ou mais , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Estados Unidos
2.
Medicine (Baltimore) ; 99(44): e22842, 2020 Oct 30.
Artigo em Inglês | MEDLINE | ID: mdl-33126324

RESUMO

Coronary artery bypass grafting (CABG) is the standard of care for the treatment of complex coronary artery disease. However, the optimal surgical treatment for patients with reduced left ventricular function with low ejection fraction (EF) is inconclusive. In our center, left-sided coronary grafting with bilateral internal thoracic artery (BITA) is generally the preferred method for surgical revascularization, also for patients with low EF. We compared early and long-term outcomes between BITA grafting and single internal thoracic artery (SITA) grafting in patients with low EF.We evaluated short- and long-term outcomes of all patients who underwent surgical revascularization in our center during 1996 to 2011, according to EF ≥30% and <30%. Univariate and multivariate analyses were performed. In addition, patients who underwent BITA and SITA grafting were matched using propensity score matching.In total, 5337 patients with multivessel disease underwent surgical revascularization during the study period. Of them, 394 had low EF. Among these, 188 underwent SITA revascularization and 206 BITA grafting. Those who underwent SITA were more likely to have comorbidities such as chronic obstructive pulmonary disease, diabetes, congestive heart failure, chronic renal failure, and a critical preoperative condition including preoperative intra-aortic balloon pump insertion.Statistically significant differences were not observed between the SITA and BITA groups in 30-day mortality (8.5% vs 6.8%, P = .55), sternal wound infection (2.7% vs 1.0%, P = .27), stroke (3.7% vs 6.3%, P = .24), and perioperative myocardial infarction (5.9% vs 2.9%, P = .15). Long-term survival (median follow up of 14 years, interquartile range, 11.2-18.9) was also similar between the groups. Propensity score matching (129 matched pairs) yielded similar early and long-term outcomes for the groups.This study did not demonstrate any clinical benefit for BITA compared with SITA revascularization in individuals with low EF.


Assuntos
Ponte de Artéria Coronária/métodos , Volume Sistólico/fisiologia , Idoso , Ponte de Artéria Coronária/normas , Ponte de Artéria Coronária/estatística & dados numéricos , Doença da Artéria Coronariana/cirurgia , Registros Eletrônicos de Saúde/estatística & dados numéricos , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Complicações Pós-Operatórias , Estudos Retrospectivos , Resultado do Tratamento , Função Ventricular Esquerda/fisiologia
3.
BMJ Open ; 10(10): e040441, 2020 10 26.
Artigo em Inglês | MEDLINE | ID: mdl-33109676

RESUMO

OBJECTIVE: To assess association of clinical features on COVID-19 patient outcomes. DESIGN: Retrospective observational study using electronic medical record data. SETTING: Five member hospitals from the Mount Sinai Health System in New York City (NYC). PARTICIPANTS: 28 336 patients tested for SARS-CoV-2 from 24 February 2020 to 15 April 2020, including 6158 laboratory-confirmed COVID-19 cases. MAIN OUTCOMES AND MEASURES: Positive test rates and in-hospital mortality were assessed for different racial groups. Among positive cases admitted to the hospital (N=3273), we estimated HR for both discharge and death across various explanatory variables, including patient demographics, hospital site and unit, smoking status, vital signs, lab results and comorbidities. RESULTS: Hispanics (29%) and African Americans (25%) had disproportionately high positive case rates relative to their representation in the overall NYC population (p<0.05); however, no differences in mortality rates were observed in hospitalised patients based on race. Outcomes differed significantly between hospitals (Gray's T=248.9; p<0.05), reflecting differences in average baseline age and underlying comorbidities. Significant risk factors for mortality included age (HR 1.05, 95% CI 1.04 to 1.06; p=1.15e-32), oxygen saturation (HR 0.985, 95% CI 0.982 to 0.988; p=1.57e-17), care in intensive care unit areas (HR 1.58, 95% CI 1.29 to 1.92; p=7.81e-6) and elevated creatinine (HR 1.75, 95% CI 1.47 to 2.10; p=7.48e-10), white cell count (HR 1.02, 95% CI 1.01 to 1.04; p=8.4e-3) and body mass index (BMI) (HR 1.02, 95% CI 1.00 to 1.03; p=1.09e-2). Deceased patients were more likely to have elevated markers of inflammation. CONCLUSIONS: While race was associated with higher risk of infection, we did not find racial disparities in inpatient mortality suggesting that outcomes in a single tertiary care health system are comparable across races. In addition, we identified key clinical features associated with reduced mortality and discharge. These findings could help to identify which COVID-19 patients are at greatest risk of a severe infection response and predict survival.


Assuntos
Betacoronavirus/isolamento & purificação , Infecções por Coronavirus , Hospitalização/estatística & dados numéricos , Unidades de Terapia Intensiva/estatística & dados numéricos , Pandemias , Pneumonia Viral , Fatores Etários , Técnicas de Laboratório Clínico/estatística & dados numéricos , Comorbidade , Infecções por Coronavirus/diagnóstico , Infecções por Coronavirus/epidemiologia , Infecções por Coronavirus/terapia , Registros Eletrônicos de Saúde/estatística & dados numéricos , Grupos Étnicos , Feminino , Mortalidade Hospitalar , Humanos , Masculino , Pessoa de Meia-Idade , Mortalidade , Cidade de Nova Iorque/epidemiologia , Pneumonia Viral/epidemiologia , Pneumonia Viral/terapia , Estudos Retrospectivos , Fatores de Risco
4.
J Med Internet Res ; 22(10): e21801, 2020 10 28.
Artigo em Inglês | MEDLINE | ID: mdl-33090964

RESUMO

BACKGROUND: Many factors involved in the onset and clinical course of the ongoing COVID-19 pandemic are still unknown. Although big data analytics and artificial intelligence are widely used in the realms of health and medicine, researchers are only beginning to use these tools to explore the clinical characteristics and predictive factors of patients with COVID-19. OBJECTIVE: Our primary objectives are to describe the clinical characteristics and determine the factors that predict intensive care unit (ICU) admission of patients with COVID-19. Determining these factors using a well-defined population can increase our understanding of the real-world epidemiology of the disease. METHODS: We used a combination of classic epidemiological methods, natural language processing (NLP), and machine learning (for predictive modeling) to analyze the electronic health records (EHRs) of patients with COVID-19. We explored the unstructured free text in the EHRs within the Servicio de Salud de Castilla-La Mancha (SESCAM) Health Care Network (Castilla-La Mancha, Spain) from the entire population with available EHRs (1,364,924 patients) from January 1 to March 29, 2020. We extracted related clinical information regarding diagnosis, progression, and outcome for all COVID-19 cases. RESULTS: A total of 10,504 patients with a clinical or polymerase chain reaction-confirmed diagnosis of COVID-19 were identified; 5519 (52.5%) were male, with a mean age of 58.2 years (SD 19.7). Upon admission, the most common symptoms were cough, fever, and dyspnea; however, all three symptoms occurred in fewer than half of the cases. Overall, 6.1% (83/1353) of hospitalized patients required ICU admission. Using a machine-learning, data-driven algorithm, we identified that a combination of age, fever, and tachypnea was the most parsimonious predictor of ICU admission; patients younger than 56 years, without tachypnea, and temperature <39 degrees Celsius (or >39 ºC without respiratory crackles) were not admitted to the ICU. In contrast, patients with COVID-19 aged 40 to 79 years were likely to be admitted to the ICU if they had tachypnea and delayed their visit to the emergency department after being seen in primary care. CONCLUSIONS: Our results show that a combination of easily obtainable clinical variables (age, fever, and tachypnea with or without respiratory crackles) predicts whether patients with COVID-19 will require ICU admission.


Assuntos
Infecções por Coronavirus/diagnóstico , Registros Eletrônicos de Saúde/estatística & dados numéricos , Hospitalização/estatística & dados numéricos , Unidades de Terapia Intensiva/estatística & dados numéricos , Aprendizado de Máquina , Processamento de Linguagem Natural , Pneumonia Viral/diagnóstico , Adulto , Idoso , Betacoronavirus , Infecções por Coronavirus/epidemiologia , Infecções por Coronavirus/terapia , Serviço Hospitalar de Emergência , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Pandemias , Pneumonia Viral/epidemiologia , Pneumonia Viral/terapia , Prognóstico , Estudos Retrospectivos , Espanha/epidemiologia , Resultado do Tratamento
5.
Transl Psychiatry ; 10(1): 337, 2020 10 02.
Artigo em Inglês | MEDLINE | ID: mdl-33009366

RESUMO

Data are scarce regarding the comorbid mental disorders and their management among COVID-19 patients. This study described the clinical characteristics and management of COVID-19 patients treated in psychiatric inpatient settings due to comorbid first-onset mental disorders in Wuhan, China. This electronic medical records-based study included 25 COVID-19 patients with first-onset mental disorders and 55 patients with first-onset mental disorders without COVID-19 (control group). Data collected included ICD-10 diagnoses of mental disorders, psychiatric and respiratory symptoms, treatments, and outcomes. Adjustment disorder (n = 11, 44.0%) and acute and transient psychotic disorders, with associated acute stress (n = 6, 24.0%) were main clinical diagnoses in the COVID-19 group while serious mental illnesses (i.e., schizophrenia, 24.5%) and alcohol use disorders (10.9%) were overrepresented in the control group. On admission, the most common psychiatric symptom in COVID-19 patients was insomnia symptoms (n = 18, 72.0%), followed by aggressive behaviors (n = 16, 64.0%), delusion (n = 10, 40.0%), and severe anxiety (n = 9, 36.0%). In addition to respiratory treatments, 76.0% COVID-19 patients received antipsychotics, 40.0% sedative-hypnotics, and 24.0% mood stabilizers. At the end of inpatient treatment, 4 (16.0%) COVID-19 patients were transferred to other hospitals to continue respiratory treatment after their psychiatric symptoms were controlled while the remaining 21 (84.0%) all recovered. Compared to the control group, COVID-19 group had significantly shorter length of hospital stay (21.2 vs. 37.4 days, P < 0.001). Adjustment disorder and acute and transient psychotic disorders are the main clinical diagnoses of COVID-19 patients managed in psychiatric inpatient settings. The short-term prognosis of these patients is good after conventional psychotropic treatment.


Assuntos
Betacoronavirus/isolamento & purificação , Infecções por Coronavirus , Hospitalização/estatística & dados numéricos , Transtornos Mentais , Pandemias , Pneumonia Viral , Psicotrópicos , China/epidemiologia , Comorbidade , Infecções por Coronavirus/epidemiologia , Infecções por Coronavirus/psicologia , Infecções por Coronavirus/terapia , Registros Eletrônicos de Saúde/estatística & dados numéricos , Feminino , Humanos , Masculino , Transtornos Mentais/epidemiologia , Transtornos Mentais/fisiopatologia , Transtornos Mentais/psicologia , Transtornos Mentais/terapia , Pessoa de Meia-Idade , Administração dos Cuidados ao Paciente/métodos , Pneumonia Viral/epidemiologia , Pneumonia Viral/psicologia , Pneumonia Viral/terapia , Prognóstico , Escalas de Graduação Psiquiátrica , Psicotrópicos/classificação , Psicotrópicos/uso terapêutico , Avaliação de Sintomas/métodos , Avaliação de Sintomas/estatística & dados numéricos
6.
Saudi Med J ; 41(10): 1090-1097, 2020 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-33026050

RESUMO

OBJECTIVES: To elucidate the risk factors for hospital admission among COVID-19 patients with type 2 diabetes mellitus (T2DM). METHODS: This retrospective study was conducted at the Prince Sultan Military Medical City, Riyadh, Saudi Arabia between May 2020 and July 2020. Out of 7,260 COVID-19 patients, 920 were identified as T2DM. After the exclusion process, 806 patients with T2DM were included in this analysis. Patients' data were extracted from electronic medical records. A logistic regression model was performed to estimate the risk factors of hospital admission. Results: Of the total of 806 COVID-19 patients with T2DM, 48% were admitted in the hospital, 52% were placed under home isolation. Older age between 70-79 years (OR [odd ratio] 2.56; p=0.017), ≥80 years (OR 6.48; p=0.001) were significantly more likely to be hospitalized compared to less than 40 years. Similarly, patients with higher HbA1c level of ≥9% compared to less than 7%; (OR 1.58; p=0.047); patients with comorbidities such as, hypertension (OR 1.43; p=0.048), cardiovascular disease (OR 1.56; p=0.033), cerebrovascular disease (OR 2.38; p=0.016), chronic pulmonary disease (OR 1.51; p=0.018), malignancy (OR 2.45; p=0.025), chronic kidney disease (CKD) IIIa, IIIb, IV (OR 2.37; p=0.008), CKD V (OR 5.07; p=0.007) were significantly more likely to be hospitalized. Likewise, insulin-treated (OR 1.46; p=0.03) were more likely to require hospital admission compared to non-insulin treated patients. CONCLUSION: Among COVID-19 patients with diabetes, higher age, high HbA1c level, and presence of other comorbidities were found to be significant risk factors for the hospital admission.


Assuntos
Fatores Etários , Doença Crônica/epidemiologia , Infecções por Coronavirus , Diabetes Mellitus Tipo 2 , Hemoglobina A Glicada/análise , Hospitalização/estatística & dados numéricos , Pandemias , Pneumonia Viral , Adulto , Idoso , Betacoronavirus/isolamento & purificação , Comorbidade , Infecções por Coronavirus/epidemiologia , Infecções por Coronavirus/terapia , Diabetes Mellitus Tipo 2/sangue , Diabetes Mellitus Tipo 2/epidemiologia , Registros Eletrônicos de Saúde/estatística & dados numéricos , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Pneumonia Viral/epidemiologia , Pneumonia Viral/terapia , Estudos Retrospectivos , Medição de Risco/métodos , Fatores de Risco , Arábia Saudita/epidemiologia
7.
BMJ ; 371: m3377, 2020 10 01.
Artigo em Inglês | MEDLINE | ID: mdl-33004347

RESUMO

OBJECTIVES: To determine the rate of complicated birth at term in women classified at low risk according to the National Institute for Health and Care Excellence guideline for intrapartum care (no pre-existing medical conditions, important obstetric history, or complications during pregnancy) and to assess if the risk classification can be improved by considering parity and the number of risk factors. DESIGN: Cohort study using linked electronic maternity records. PARTICIPANTS: 276 766 women with a singleton birth at term after a trial of labour in 87 NHS hospital trusts in England between April 2015 and March 2016. MAIN OUTCOME MEASURE: A composite outcome of complicated birth, defined as a birth with use of an instrument, caesarean delivery, anal sphincter injury, postpartum haemorrhage, or Apgar score of 7 or less at five minutes. RESULTS: Multiparous women without a history of caesarean section had the lowest rates of complicated birth, varying from 8.8% (4879 of 55 426 women, 95% confidence interval 8.6% to 9.0%) in those without specific risk factors to 21.8% (613 of 2811 women, 20.2% to 23.4%) in those with three or more. The rate of complicated birth was higher in nulliparous women, with corresponding rates varying from 43.4% (25 805 of 59 413 women, 43.0% to 43.8%) to 64.3% (364 of 566 women, 60.3% to 68.3%); and highest in multiparous women with previous caesarean section, with corresponding rates varying from 42.9% (3426 of 7993 women, 41.8% to 44.0%) to 66.3% (554 of 836 women, 63.0% to 69.5%). CONCLUSIONS: Nulliparous women without risk factors have substantially higher rates of complicated birth than multiparous women without a previous caesarean section even if the latter have multiple risk factors. Grouping women first according to parity and previous mode of birth, and then within these groups according to presence of specific risk factors would provide greater and more informed choice to women, better targeting of interventions, and fewer transfers during labour than according to the presence of risk factors alone.


Assuntos
Parto Obstétrico , Complicações do Trabalho de Parto , Paridade , Nascimento a Termo , Adulto , Parto Obstétrico/métodos , Parto Obstétrico/estatística & dados numéricos , Registros Eletrônicos de Saúde/estatística & dados numéricos , Inglaterra/epidemiologia , Feminino , Humanos , Complicações do Trabalho de Parto/diagnóstico , Complicações do Trabalho de Parto/epidemiologia , Complicações do Trabalho de Parto/etiologia , Assistência Perinatal/métodos , Assistência Perinatal/normas , Gravidez , Resultado da Gravidez/epidemiologia , Melhoria de Qualidade , História Reprodutiva , Medição de Risco , Fatores de Risco
8.
JAMA ; 324(14): 1429-1438, 2020 10 13.
Artigo em Inglês | MEDLINE | ID: mdl-33048153

RESUMO

Importance: The prevalence of leading risk factors for morbidity and mortality in the US significantly varies across regions, states, and neighborhoods, but the extent these differences are associated with a person's place of residence vs the characteristics of the people who live in different places remains unclear. Objective: To estimate the degree to which geographic differences in leading risk factors are associated with a person's place of residence by comparing trends in health outcomes among individuals who moved to different areas or did not move. Design, Setting, and Participants: This retrospective cohort study estimated the association between the differences in the prevalence of uncontrolled chronic conditions across movers' destination and origin zip codes and changes in individuals' likelihood of uncontrolled chronic conditions after moving, adjusting for person-specific fixed effects, the duration of time since the move, and secular trends among movers and those who did not move. Electronic health records from the Veterans Health Administration were analyzed. The primary analysis included 5 342 207 individuals with at least 1 Veterans Health Administration outpatient encounter between 2008 and 2018 who moved zip codes exactly once or never moved. Exposures: The difference in the prevalence of uncontrolled chronic conditions between a person's origin zip code and destination zip code (excluding the individual mover's outcomes). Main Outcomes and Measures: Prevalence of uncontrolled blood pressure (systolic blood pressure level >140 mm Hg or diastolic blood pressure level >90 mm Hg), uncontrolled diabetes (hemoglobin A1c level >8%), obesity (body mass index >30), and depressive symptoms (2-item Patient Health Questionnaire score ≥2) per quarter-year during the 3 years before and the 3 years after individuals moved. Results: The study population included 5 342 207 individuals (mean age, 57.6 [SD, 17.4] years, 93.9% men, 72.5% White individuals, and 12.7% Black individuals), of whom 1 095 608 moved exactly once and 4 246 599 never moved during the study period. Among the movers, the change after moving in the prevalence of uncontrolled blood pressure was 27.5% (95% CI, 23.8%-31.3%) of the between-area difference in the prevalence of uncontrolled blood pressure. Similarly, the change after moving in the prevalence of uncontrolled diabetes was 5.0% (95% CI, 2.7%-7.2%) of the between-area difference in the prevalence of uncontrolled diabetes; the change after moving in the prevalence of obesity was 3.1% (95% CI, 2.0%-4.2%) of the between-area difference in the prevalence of obesity; and the change after moving in the prevalence of depressive symptoms was 15.2% (95% CI, 13.1%-17.2%) of the between-area difference in the prevalence of depressive symptoms. Conclusions and Relevance: In this retrospective cohort study of individuals receiving care at Veterans Health Administration facilities, geographic differences in prevalence were associated with a substantial percentage of the change in individuals' likelihood of poor blood pressure control or depressive symptoms, and a smaller percentage of the change in individuals' likelihood of poor diabetes control and obesity. Further research is needed to understand the source of these associations with a person's place of residence.


Assuntos
Transtorno Depressivo/epidemiologia , Diabetes Mellitus/epidemiologia , Migração Humana/estatística & dados numéricos , Hipertensão/epidemiologia , Obesidade/epidemiologia , Características de Residência/estatística & dados numéricos , Doença Crônica/epidemiologia , Doença Crônica/etnologia , Transtorno Depressivo/etnologia , Diabetes Mellitus/etnologia , Registros Eletrônicos de Saúde/estatística & dados numéricos , Feminino , Geografia Médica , Migração Humana/tendências , Humanos , Hipertensão/etnologia , Masculino , Pessoa de Meia-Idade , Obesidade/etnologia , Prevalência , Estudos Retrospectivos , Fatores de Risco , Fatores de Tempo , Incerteza , Estados Unidos/epidemiologia , Estados Unidos/etnologia , Serviços de Saúde para Veteranos Militares/estatística & dados numéricos
9.
Lancet Glob Health ; 8(10): e1326-e1334, 2020 10.
Artigo em Inglês | MEDLINE | ID: mdl-32971055

RESUMO

BACKGROUND: Mental disorders can adversely affect HIV treatment outcomes and survival. Data are scarce on premature deaths in people with mental disorders in HIV-positive populations, particularly in low-income and middle-income countries. In this study, we quantified excess mortality associated with mental disorders in HIV-positive people in South Africa, adjusting for HIV treatment outcomes. METHODS: For this cohort study, we analysed routinely collected data on HIV-positive adults receiving antiretroviral therapy (ART) in Cape Town, South Africa between Jan 1, 2004, to Dec 31, 2017. Data from three ART programmes were linked with routine medical records on mental health treatment from Jan 1, 2010, to Dec 31, 2017, and mortality surveillance data from the South African National Population Register up to Dec 31, 2017. People living with HIV aged 15 years or older who initiated ART at a programme site were eligible for analysis. We followed up patients from ART initiation or Jan 1, 2010, whichever occurred later, to transfer, death, or Dec 31, 2017. Patients were considered as having a history of mental illness if they had ever received psychiatric medication or been hospitalised for a mental disorder. We calculated adjusted hazard ratios (aHRs) with 95% CIs for associations between history of mental illness, mortality, and HIV treatment outcomes (retention in care with viral load suppression [VLS; viral load <1000 copies per mL], retention in care with non-suppressed viral load [NVL; viral load ≥1000 copies per mL], and loss to follow-up [LTFU; >180 days late for a clinic visit at closure of the database]) using Cox proportional hazard regression and multistate models. RESULTS: 58 664 patients were followed up for a median of 4·3 years (IQR 2·1-6·4), 2927 (5·0%) of whom had a history of mental illness. After adjustment for age, sex, treatment programme, and year of ART initiation, history of mental illness was associated with increased risk of mortality from all causes (aHR 2·98 [95% CI 2·69-3·30]), natural causes (3·00 [2·69-3·36]), and unnatural causes (2·10 [1·27-3·49]), compared with no history of mental illness. Risk of all-cause mortality in people with a history of mental illness remained increased in multivariable analysis adjusted for age, sex, treatment programme, year of ART initiation, CD4 count and WHO clinical stage at ART initiation, retention in HIV care with or without VLS, and LTFU (2·73 [2·46-3·02]). In our multistate model, adjusted for age, sex, year of ART initiation, cumulative time with NVL, and WHO clinical stage and CD4 cell count at ART initiation, rates of excess all-cause mortality in people with history of mental illness were greatest in patients retained in care with VLS (aHR 3·43 [95% CI 2·83-4·15]), followed by patients retained in care with NVL (2·74 [2·32-3·24]), and smallest in those LTFU (2·12 [1·78-2·53]). History of mental illness was also associated with increased risk of HIV viral rebound (transitioning from VLS to NVL; 1·50 [1·32-1·69]) and LTFU in people with VLS (1·19 [1·06-1·34]). INTERPRETATION: Mental illness was associated with substantial excess mortality in HIV-positive adults in Cape Town. Excess mortality among people with a history of mental illness occurred independently of HIV treatment success. Interventions to reduce excess mortality should address the complex physical and mental health-care needs of people living with HIV and mental illness. FUNDING: National Institutes of Health, Swiss National Science Foundation, South African Medical Research Council.


Assuntos
Registros Eletrônicos de Saúde/estatística & dados numéricos , Infecções por HIV/mortalidade , Transtornos Mentais/mortalidade , Adolescente , Adulto , Estudos de Coortes , Comorbidade , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , África do Sul/epidemiologia , Adulto Jovem
11.
PLoS Med ; 17(9): e1003321, 2020 09.
Artigo em Inglês | MEDLINE | ID: mdl-32911500

RESUMO

BACKGROUND: At the beginning of June 2020, there were nearly 7 million reported cases of coronavirus disease 2019 (COVID-19) worldwide and over 400,000 deaths in people with COVID-19. The objective of this study was to determine associations between comorbidities listed in the Charlson comorbidity index and mortality among patients in the United States with COVID-19. METHODS AND FINDINGS: A retrospective cohort study of adults with COVID-19 from 24 healthcare organizations in the US was conducted. The study included adults aged 18-90 years with COVID-19 coded in their electronic medical records between January 20, 2020, and May 26, 2020. Results were also stratified by age groups (<50 years, 50-69 years, or 70-90 years). A total of 31,461 patients were included. Median age was 50 years (interquartile range [IQR], 35-63) and 54.5% (n = 17,155) were female. The most common comorbidities listed in the Charlson comorbidity index were chronic pulmonary disease (17.5%, n = 5,513) and diabetes mellitus (15.0%, n = 4,710). Multivariate logistic regression analyses showed older age (odds ratio [OR] per year 1.06; 95% confidence interval [CI] 1.06-1.07; p < 0.001), male sex (OR 1.75; 95% CI 1.55-1.98; p < 0.001), being black or African American compared to white (OR 1.50; 95% CI 1.31-1.71; p < 0.001), myocardial infarction (OR 1.97; 95% CI 1.64-2.35; p < 0.001), congestive heart failure (OR 1.42; 95% CI 1.21-1.67; p < 0.001), dementia (OR 1.29; 95% CI 1.07-1.56; p = 0.008), chronic pulmonary disease (OR 1.24; 95% CI 1.08-1.43; p = 0.003), mild liver disease (OR 1.26; 95% CI 1.00-1.59; p = 0.046), moderate/severe liver disease (OR 2.62; 95% CI 1.53-4.47; p < 0.001), renal disease (OR 2.13; 95% CI 1.84-2.46; p < 0.001), and metastatic solid tumor (OR 1.70; 95% CI 1.19-2.43; p = 0.004) were associated with higher odds of mortality with COVID-19. Older age, male sex, and being black or African American (compared to being white) remained significantly associated with higher odds of death in age-stratified analyses. There were differences in which comorbidities were significantly associated with mortality between age groups. Limitations include that the data were collected from the healthcare organization electronic medical record databases and some comorbidities may be underreported and ethnicity was unknown for 24% of participants. Deaths during an inpatient or outpatient visit at the participating healthcare organizations were recorded; however, deaths occurring outside of the hospital setting are not well captured. CONCLUSIONS: Identifying patient characteristics and conditions associated with mortality with COVID-19 is important for hypothesis generating for clinical trials and to develop targeted intervention strategies.


Assuntos
Betacoronavirus/isolamento & purificação , Infecções por Coronavirus , Diabetes Mellitus/epidemiologia , Pandemias , Pneumonia Viral , Doença Pulmonar Obstrutiva Crônica/epidemiologia , Fatores Etários , Doença Crônica/classificação , Doença Crônica/epidemiologia , Comorbidade , Infecções por Coronavirus/diagnóstico , Infecções por Coronavirus/mortalidade , Registros Eletrônicos de Saúde/estatística & dados numéricos , Grupos Étnicos/estatística & dados numéricos , Feminino , Hospitalização/estatística & dados numéricos , Humanos , Masculino , Pessoa de Meia-Idade , Mortalidade , Pneumonia Viral/diagnóstico , Pneumonia Viral/mortalidade , Estudos Retrospectivos , Medição de Risco/estatística & dados numéricos , Fatores Sexuais , Estados Unidos/epidemiologia
12.
Nat Commun ; 11(1): 3852, 2020 07 31.
Artigo em Inglês | MEDLINE | ID: mdl-32737308

RESUMO

Acute critical illness is often preceded by deterioration of routinely measured clinical parameters, e.g., blood pressure and heart rate. Early clinical prediction is typically based on manually calculated screening metrics that simply weigh these parameters, such as early warning scores (EWS). The predictive performance of EWSs yields a tradeoff between sensitivity and specificity that can lead to negative outcomes for the patient. Previous work on electronic health records (EHR) trained artificial intelligence (AI) systems offers promising results with high levels of predictive performance in relation to the early, real-time prediction of acute critical illness. However, without insight into the complex decisions by such system, clinical translation is hindered. Here, we present an explainable AI early warning score (xAI-EWS) system for early detection of acute critical illness. xAI-EWS potentiates clinical translation by accompanying a prediction with information on the EHR data explaining it.


Assuntos
Lesão Renal Aguda/diagnóstico , Lesão Pulmonar Aguda/diagnóstico , Inteligência Artificial , Registros Eletrônicos de Saúde/estatística & dados numéricos , Sepse/diagnóstico , Doença Aguda , Lesão Renal Aguda/sangue , Lesão Renal Aguda/patologia , Lesão Pulmonar Aguda/sangue , Lesão Pulmonar Aguda/patologia , Área Sob a Curva , Pressão Sanguínea , Estado Terminal , Diagnóstico Precoce , Frequência Cardíaca , Humanos , Prognóstico , Curva ROC , Sepse/sangue , Sepse/patologia
13.
Public Health Rep ; 135(5): 621-630, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32791022

RESUMO

OBJECTIVE: Electronic health records (EHRs) hold promise as a public health surveillance tool, but questions remain about how EHR patients compare with populations in health and demographic surveys. We compared population characteristics from a regional distributed data network (DDN), which securely and confidentially aggregates EHR data from multiple health care organizations in the same geographic region, with population characteristics from health and demographic surveys. METHODS: Ten health care organizations participating in a Colorado DDN contributed data for coverage estimation. We aggregated demographic and geographic data from 2017 for patients aged ≥18 residing in 7 counties. We used a cross-sectional design to compare DDN population size, by county, with the following survey-estimated populations: the county population, estimated by the American Community Survey (ACS); residents seeking any health care, estimated by the Colorado Health Access Survey; and residents seeking routine (eg, primary) health care, estimated by the Behavioral Risk Factor Surveillance System. We also compared data on the DDN and survey populations by sex, age group, race/ethnicity, and poverty level to assess surveillance system representativeness. RESULTS: The DDN population included 609 840 people in 7 counties, corresponding to 25% coverage of the general adult population. Population coverage ranged from 15% to 35% across counties. Demographic distributions generated by DDN and surveys were similar for many groups. Overall, the DDN and surveys assessing care-seeking populations had a higher proportion of women and older adults than the ACS population. The DDN included higher proportions of Hispanic people and people living in high-poverty neighborhoods compared with the surveys. CONCLUSION: The DDN population is not a random sample of the regional adult population; it is influenced by health care use patterns and organizations participating in the DDN. Strengths and limitations of DDNs complement those of survey-based approaches. The regional DDN is a promising public health surveillance tool.


Assuntos
Registros Eletrônicos de Saúde/estatística & dados numéricos , Geografia , Acesso aos Serviços de Saúde/estatística & dados numéricos , Aceitação pelo Paciente de Cuidados de Saúde/estatística & dados numéricos , Vigilância em Saúde Pública/métodos , Fatores Socioeconômicos , Adulto , Fatores Etários , Idoso , Idoso de 80 Anos ou mais , Colorado , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Fatores Sexuais , Inquéritos e Questionários , Adulto Jovem
14.
PLoS One ; 15(8): e0237698, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32842139

RESUMO

With brief psychiatric hospitalizations, the extent to which symptoms change is rarely characterized. We sought to understand symptomatic changes across Research Domain Criteria (RDoC) dimensions, and the extent to which such improvement might be associated with risk for readmission. We identified 3,634 individuals with 4,713 hospital admissions to the psychiatric inpatient unit of a large academic medical center between 2010 and 2015. We applied a natural language processing tool to extract estimates of the five RDoC domains to the admission note and discharge summary and calculated the change in each domain. We examined the extent to which symptom domains changed during admission, and their relationship to baseline clinical and sociodemographic features, using linear regression. Symptomatic worsening was rare in the negative valence (0.4%) and positive valence (5.1%) domains, but more common in cognition (25.8%). Most diagnoses exhibited improvement in negative valence, which was associated with significant reduction in readmission risk. Despite generally brief hospital stays, we detected reduction across multiple symptom domains, with greatest improvement in negative symptoms, and greatest probability of worsening in cognitive symptoms. This approach should facilitate investigations of other features or interventions which may influence pace of clinical improvement.


Assuntos
Manual Diagnóstico e Estatístico de Transtornos Mentais , Transtornos Mentais/diagnóstico , Readmissão do Paciente/estatística & dados numéricos , Centros Médicos Acadêmicos/estatística & dados numéricos , Adulto , Registros Eletrônicos de Saúde/estatística & dados numéricos , Feminino , Humanos , Tempo de Internação/estatística & dados numéricos , Masculino , Transtornos Mentais/terapia , Pessoa de Meia-Idade , Processamento de Linguagem Natural , Admissão do Paciente/estatística & dados numéricos , Sumários de Alta do Paciente Hospitalar/estatística & dados numéricos , Estudos Retrospectivos , Medição de Risco/métodos , Fatores de Tempo , Resultado do Tratamento
15.
Curr Opin Ophthalmol ; 31(5): 427-434, 2020 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-32740067

RESUMO

PURPOSE OF REVIEW: The COVID-19 pandemic has posed an unprecedented challenge to the healthcare community. To reduce disease transmission, national regulatory agencies temporarily recommended curtailment of all nonurgent office visits and elective surgeries in March 2020, including vitreoretinal outpatient care in the USA. The effect of these guidelines on utilization of vitreoretinal care has not been explored to date. RECENT FINDINGS: Retinal outpatient visits, new patient visits, intravitreal antivascular endothelial growth factor injections and in-office multimodal retinal imaging has seen a significant decline in utilization in the early phase of the pandemic. Intravitreal injections were performed at a comparatively higher rate than office visits. Utilization appeared to steadily increase in April 2020. Telemedicine visits, enabled by new national legislation for all areas of medicine, have been adopted to a modest degree by the retina community. SUMMARY: In-office retinal care declined in response to the COVID-19 pandemic and national regulatory guidelines limiting nonurgent care. These trends in practice patterns and care utilization may be of interest to vitreoretinal providers and all ophthalmologists at large.


Assuntos
Betacoronavirus , Infecções por Coronavirus/epidemiologia , Pandemias/estatística & dados numéricos , Aceitação pelo Paciente de Cuidados de Saúde/estatística & dados numéricos , Pneumonia Viral/epidemiologia , Padrões de Prática Médica/estatística & dados numéricos , Doenças Retinianas , Corpo Vítreo/patologia , Assistência Ambulatorial/estatística & dados numéricos , Bases de Dados Factuais , Assistência à Saúde/estatística & dados numéricos , Registros Eletrônicos de Saúde/estatística & dados numéricos , Pesquisa sobre Serviços de Saúde , Humanos , Doenças Retinianas/diagnóstico , Doenças Retinianas/terapia , Telemedicina/estatística & dados numéricos , Estados Unidos/epidemiologia
16.
Biodemography Soc Biol ; 65(3): 257-267, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32727275

RESUMO

In the United States, obesity has increased in prevalence over time and is strongly associated with subsequent outcomes such as diabetes mellitus (DM) and nonalcoholic fatty liver disease (NAFLD). It is unclear, however, as to how the magnitude of NAFLD risk from obesity and DM is increased in safety-net health system settings. Among the San Francisco Health Network (SFHN) patients (N = 47,211), we examined the association between Body Mass Index (BMI) and elevated liver enzyme levels, including interaction by DM status. Our findings revealed that 32.2 percent of SFHN patients were obese, and Pacific Islanders in the safety-net had the highest rates of obesity compared to other racial groups, even after using higher race-specific BMI cutoffs. In SFHN, obesity was associated with elevated liver enzymes, with the relationship stronger among those without DM. Our findings highlight how obesity is a stronger factor of NAFLD in the absence of DM, suggesting that practitioners consider screening for NAFLD among safety-net patients with obesity even if DM has not developed. These results highlight the importance of directing efforts to reduce obesity in safety-net health systems and encourage researchers to further examine effect modification between health outcomes in such populations.


Assuntos
Obesidade/terapia , Provedores de Redes de Segurança/métodos , Adolescente , Adulto , Idoso , Índice de Massa Corporal , California/epidemiologia , Comorbidade , Diabetes Mellitus/epidemiologia , Diabetes Mellitus/etiologia , Registros Eletrônicos de Saúde/estatística & dados numéricos , Feminino , Insuficiência Hepática/epidemiologia , Insuficiência Hepática/etiologia , Humanos , Modelos Logísticos , Masculino , Pessoa de Meia-Idade , Obesidade/epidemiologia , Prevalência , Fatores de Risco , Provedores de Redes de Segurança/organização & administração , Provedores de Redes de Segurança/estatística & dados numéricos
17.
Ann Emerg Med ; 76(4): 501-514, 2020 10.
Artigo em Inglês | MEDLINE | ID: mdl-32713624

RESUMO

STUDY OBJECTIVE: Acute kidney injury occurs commonly and is a leading cause of prolonged hospitalization, development and progression of chronic kidney disease, and death. Early acute kidney injury treatment can improve outcomes. However, current decision support is not able to detect patients at the highest risk of developing acute kidney injury. We analyzed routinely collected emergency department (ED) data and developed prediction models with capacity for early identification of ED patients at high risk for acute kidney injury. METHODS: A multisite, retrospective, cross-sectional study was performed at 3 EDs between January 2014 and July 2017. All adult ED visits in which patients were hospitalized and serum creatinine level was measured both on arrival and again with 72 hours were included. We built machine-learning-based classifiers that rely on vital signs, chief complaints, medical history and active medical visits, and laboratory results to predict the development of acute kidney injury stage 1 and 2 in the next 24 to 72 hours, according to creatinine-based international consensus criteria. Predictive performance was evaluated out of sample by Monte Carlo cross validation. RESULTS: The final cohort included 91,258 visits by 59,792 unique patients. Seventy-two-hour incidence of acute kidney injury was 7.9% for stages greater than or equal to 1 and 1.0% for stages greater than or equal to 2. The area under the receiver operating characteristic curve for acute kidney injury prediction ranged from 0.81 (95% confidence interval 0.80 to 0.82) to 0.74 (95% confidence interval 0.74 to 0.75), with a median time from ED arrival to prediction of 1.7 hours (interquartile range 1.3 to 2.5 hours). CONCLUSION: Machine learning applied to routinely collected ED data identified ED patients at high risk for acute kidney injury up to 72 hours before they met diagnostic criteria. Further prospective evaluation is necessary.


Assuntos
Lesão Renal Aguda/diagnóstico , Registros Eletrônicos de Saúde/estatística & dados numéricos , Aprendizado de Máquina/normas , Adolescente , Adulto , Idoso , Idoso de 80 Anos ou mais , Regras de Decisão Clínica , Creatinina/análise , Creatinina/sangue , Estudos Transversais , Serviço Hospitalar de Emergência/organização & administração , Serviço Hospitalar de Emergência/estatística & dados numéricos , Feminino , Humanos , Aprendizado de Máquina/estatística & dados numéricos , Masculino , Pessoa de Meia-Idade , Estudos Retrospectivos
19.
BMJ Open ; 10(7): e039369, 2020 07 29.
Artigo em Inglês | MEDLINE | ID: mdl-32727740

RESUMO

OBJECTIVES: There is uncertainty about when the first cases of COVID-19 appeared in Spain. We aimed to determine whether influenza diagnoses masked early COVID-19 cases and estimate numbers of undetected COVID-19 cases. DESIGN: Time-series study of influenza and COVID-19 cases, 2010-2020. SETTING: Primary care, Catalonia, Spain. PARTICIPANTS: People registered in primary-care practices, covering >6 million people and >85% of the population. MAIN OUTCOME MEASURES: Weekly new cases of influenza and COVID-19 clinically diagnosed in primary care. ANALYSES: Daily counts of both cases were computed using the total cases recorded over the previous 7 days to avoid weekly effects. Epidemic curves were characterised for the 2010-2011 to 2019-2020 influenza seasons. Influenza seasons with a similar epidemic curve and peak case number as the 2019-2020 season were used to model expected case numbers with Auto Regressive Integrated Moving Average models, overall and stratified by age. Daily excess influenza cases were defined as the number of observed minus expected cases. RESULTS: Four influenza season curves (2011-2012, 2012-2013, 2013-2014 and 2016-2017) were used to estimate the number of expected cases of influenza in 2019-2020. Between 4 February 2020 and 20 March 2020, 8017 (95% CI: 1841 to 14 718) excess influenza cases were identified. This excess was highest in the 15-64 age group. CONCLUSIONS: COVID-19 cases may have been present in the Catalan population when the first imported case was reported on 25 February 2020. COVID-19 carriers may have been misclassified as influenza diagnoses in primary care, boosting community transmission before public health measures were taken. The use of clinical codes could misrepresent the true occurrence of the disease. Serological or PCR testing should be used to confirm these findings. In future, this surveillance of excess influenza could help detect new outbreaks of COVID-19 or other influenza-like pathogens, to initiate early public health responses.


Assuntos
Infecções por Coronavirus , Influenza Humana , Pandemias , Pneumonia Viral , Adolescente , Adulto , Betacoronavirus/isolamento & purificação , Técnicas de Laboratório Clínico , Infecções por Coronavirus/diagnóstico , Infecções por Coronavirus/epidemiologia , Diagnóstico Diferencial , Registros Eletrônicos de Saúde/estatística & dados numéricos , Feminino , Humanos , Influenza Humana/diagnóstico , Influenza Humana/epidemiologia , Masculino , Pessoa de Meia-Idade , Determinação de Necessidades de Cuidados de Saúde , Pandemias/prevenção & controle , Pandemias/estatística & dados numéricos , Pneumonia Viral/diagnóstico , Pneumonia Viral/epidemiologia , Atenção Primária à Saúde/estatística & dados numéricos , Saúde Pública/métodos , Saúde Pública/normas , Estações do Ano , Espanha/epidemiologia
20.
PLoS One ; 15(7): e0233004, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32609757

RESUMO

INTRODUCTION: The Electronic Health Record (EHR) has become an integral component of healthcare delivery. Survey based studies have estimated that physicians spend 4-6 hours of their workday devoted to EHR. Our study was designed to use computer software to objectively obtain time spent on EHR. METHODS: We recorded EHR time for 248 physiciansover 2 time intervals. EHR active use was defined as more than 15 keystrokes, or 3 mouse clicks, or 1700 "mouse miles" per minute. We recorded total time and % of work hours spent on EHR, and differences in those based on seniority. Physicians reported duty hours using a standardized toolkit. RESULTS: Physicians spent 3.8 (±2) hours on EHR daily, which accounted for 37% (±17%), 41% (±14%), and 45% (±12%) of their day for all clinicians, residents, and interns, respectively. With the progression of training, there was a reduction in EHR time (all p values <0.01). During the first academic quarter, clinicians spent 38% (± 8%) of time on chart review, 17% (± 7%) on orders, 28% (±11%) on documentation (i.e. writing notes) and 17% (±7%) on other activities (i.e. physician hand-off and medication reconciliation). This pattern remained unchanged during the fourth quarter. CONCLUSIONS: Physicians spend close to 40% of their work day on EHR, with interns spending the most time. There is a significant reduction in time spent on EHR with training and greater experience, although the overall amount of time spent on EHR remained high.


Assuntos
Registros Eletrônicos de Saúde/estatística & dados numéricos , Hospitais Comunitários/estatística & dados numéricos , Atitude Frente aos Computadores , Humanos , Internato e Residência/estatística & dados numéricos , Satisfação do Paciente , Médicos/estatística & dados numéricos , Fatores de Tempo
SELEÇÃO DE REFERÊNCIAS
DETALHE DA PESQUISA