Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 7 de 7
Filter
1.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-322260

ABSTRACT

Background: The COVID-19 public health emergency (PHE) has caused extensive job loss and loss of employer-sponsored insurance. State Medicaid programs have experienced a related increase in enrollment during the PHE. However, the composition of enrollment and enrollee changes during the pandemic is unknown. Understanding changes in the Medicaid population during the PHE may inform policy development and identify strategies to meet the rising needs for insurance coverage during public health emergencies. This study aims to examine changes in Medicaid enrollment and population characteristics during the PHE.Methods: A retrospective descriptive study documenting changes in Medicaid new enrollment and disenrollment, and enrollee characteristics between March and October 2020 compared to the same time period in 2019 using Full-state Medicaid populations from six states of a wide geographical region. The primary outcomes were medicaid enrollment and disenrollment during the PHE. New enrollment included persons enrolled in Medicaid between March and October 2020 who were not enrolled in January or February of 2020. Disenrollment included persons who were enrolled in March of 2020 but not enrolled in October 2020.Results: The study included 8.50 million Medicaid enrollees in 2020 and 8.46 million in 2019. Overall, enrollment increased by 13.0% during the PHE compared to 2019, relative enrollment growth of 1.19 million. New enrollment accounted for 24.9% of the relative increase, while the remaining 75.1% was due to disenrollment. A larger proportion of new enrollment in 2020 was among adults aged 27-44 (28.3% vs 23.6%), Hispanics (34.3% vs 32.5%) and in the financial needy (44.0% vs 39.0%) category compared to 2019. Disenrollment included a larger proportion of older adults (26.1% vs 8.1%) and non-Hispanics (70.3% vs 66.4%) than in 2019.Conclusions and Relevance: Medicaid enrollment grew considerably during the PHE, and the majority of enrollment growth was attributed to decreases in disenrollment rather than increases in new enrollment. Our results highlight the impact of COVID-19 on state health programs and can guide federal and state budgetary planning.Funding: This study was supported from the Digital Health CRC (Cooperative Research Centre). The DHCRC is established and supported under the Australian Government’s Cooperative Research Centres Program. Funding for Antonia Chan was provided by the Stanford Medical Scholars Fellowship Program.Declaration of Interest: None to declare.

2.
J Med Internet Res ; 23(2): e23026, 2021 02 22.
Article in English | MEDLINE | ID: covidwho-1575588

ABSTRACT

BACKGROUND: For the clinical care of patients with well-established diseases, randomized trials, literature, and research are supplemented with clinical judgment to understand disease prognosis and inform treatment choices. In the void created by a lack of clinical experience with COVID-19, artificial intelligence (AI) may be an important tool to bolster clinical judgment and decision making. However, a lack of clinical data restricts the design and development of such AI tools, particularly in preparation for an impending crisis or pandemic. OBJECTIVE: This study aimed to develop and test the feasibility of a "patients-like-me" framework to predict the deterioration of patients with COVID-19 using a retrospective cohort of patients with similar respiratory diseases. METHODS: Our framework used COVID-19-like cohorts to design and train AI models that were then validated on the COVID-19 population. The COVID-19-like cohorts included patients diagnosed with bacterial pneumonia, viral pneumonia, unspecified pneumonia, influenza, and acute respiratory distress syndrome (ARDS) at an academic medical center from 2008 to 2019. In total, 15 training cohorts were created using different combinations of the COVID-19-like cohorts with the ARDS cohort for exploratory purposes. In this study, two machine learning models were developed: one to predict invasive mechanical ventilation (IMV) within 48 hours for each hospitalized day, and one to predict all-cause mortality at the time of admission. Model performance was assessed using the area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, positive predictive value, and negative predictive value. We established model interpretability by calculating SHapley Additive exPlanations (SHAP) scores to identify important features. RESULTS: Compared to the COVID-19-like cohorts (n=16,509), the patients hospitalized with COVID-19 (n=159) were significantly younger, with a higher proportion of patients of Hispanic ethnicity, a lower proportion of patients with smoking history, and fewer patients with comorbidities (P<.001). Patients with COVID-19 had a lower IMV rate (15.1 versus 23.2, P=.02) and shorter time to IMV (2.9 versus 4.1 days, P<.001) compared to the COVID-19-like patients. In the COVID-19-like training data, the top models achieved excellent performance (AUROC>0.90). Validating in the COVID-19 cohort, the top-performing model for predicting IMV was the XGBoost model (AUROC=0.826) trained on the viral pneumonia cohort. Similarly, the XGBoost model trained on all 4 COVID-19-like cohorts without ARDS achieved the best performance (AUROC=0.928) in predicting mortality. Important predictors included demographic information (age), vital signs (oxygen saturation), and laboratory values (white blood cell count, cardiac troponin, albumin, etc). Our models had class imbalance, which resulted in high negative predictive values and low positive predictive values. CONCLUSIONS: We provided a feasible framework for modeling patient deterioration using existing data and AI technology to address data limitations during the onset of a novel, rapidly changing pandemic.


Subject(s)
COVID-19/diagnosis , COVID-19/mortality , Machine Learning , Pneumonia, Viral/diagnosis , Aged , Area Under Curve , Cohort Studies , Comorbidity , Female , Hospitalization/statistics & numerical data , Humans , Male , Middle Aged , Pandemics , Pneumonia, Viral/mortality , Predictive Value of Tests , Prognosis , ROC Curve , Respiration, Artificial/statistics & numerical data , Retrospective Studies , SARS-CoV-2 , Treatment Outcome
3.
Medicine (Baltimore) ; 100(29): e26677, 2021 Jul 23.
Article in English | MEDLINE | ID: covidwho-1494089

ABSTRACT

BACKGROUND: In December 2019, the first case of Corona Virus Disease 2019 (COVID-19) associated with severe acute respiratory syndrome coronavirus-2 viral infection was described in Wuhan. Similar to SARS in 2003, COVID-19 also had a lasting impact. Approximately 76% of patients discharged after hospitalization for COVID-19 had neurological manifestations which could persist for 6 months, and some long-term consequences such as the gradual loss of lung function due to pulmonary interstitial fibrosis could have comprehensive effects on daily quality of life for people who were initially believed to have recovered from COVID-19. METHODS AND ANALYSIS: Our comprehensive search strategy developed in consultation with a research librarian. We will search these following electronic databases: PubMed, Cochrane Library, Web of Science, ScienceDirect, Scopus, Google Scholar, Embase, ProQuest, China Science and Technology Journal Database (VIP), China National Knowledge Infrastructure, WANFANG DATA, WHO covid-19 website, and Centers for Disease Control and the Prevention COVID-19 websites of the United States and China. The bias of publication will be confirmed via the P value of Egger test. The quality of studies will be evaluated by the Newcastle-Ottawa Scale. ETHICS AND DISSEMINATION: There are no ethical considerations associated with this study protocol for this systematic review which mainly focuses on the examination of secondary data. On completion of this analysis, we will prepare a manuscript for publication in a peer-reviewed medical journal. PROSPERO REGISTRATION NUMBER: CRD42021258711.


Subject(s)
COVID-19/drug therapy , Drugs, Chinese Herbal/therapeutic use , Humans , Medicine, Chinese Traditional/methods , Treatment Outcome
4.
J Am Med Inform Assoc ; 28(11): 2536-2540, 2021 10 12.
Article in English | MEDLINE | ID: covidwho-1377974

ABSTRACT

At the onset of the COVID-19 (coronavirus disease 2019) pandemic, telemedicine was rapidly implemented to protect patients and healthcare providers from infection. It is unlikely that care delivery will fully return to the pre-COVID form. Telemedicine offers many opportunities to improve care efficiency, accessibility, and patient outcomes, but many challenges exist related to technology interoperability, the digital divide, and usability. We propose that telemedicine evolve to support continuity of care throughout the patient journey, including multidisciplinary care teams and the seamless integration of data into the clinical workflow to support a learning healthcare system. Importantly, evidence is needed to support this paradigm shift in care delivery to ensure the quality and efficacy of care delivered via telemedicine. Here, we highlight gaps and opportunities that need to be addressed by the biomedical informatics community to move forward with safe and effective healthcare delivery via telemedicine.


Subject(s)
COVID-19 , Telemedicine , Delivery of Health Care , Humans , Pandemics , SARS-CoV-2
5.
J Biomed Inform ; 119: 103802, 2021 07.
Article in English | MEDLINE | ID: covidwho-1219050

ABSTRACT

BACKGROUND: Unlike well-established diseases that base clinical care on randomized trials, past experiences, and training, prognosis in COVID19 relies on a weaker foundation. Knowledge from other respiratory failure diseases may inform clinical decisions in this novel disease. The objective was to predict 48-hour invasive mechanical ventilation (IMV) within 48 h in patients hospitalized with COVID-19 using COVID-like diseases (CLD). METHODS: This retrospective multicenter study trained machine learning (ML) models on patients hospitalized with CLD to predict IMV within 48 h in COVID-19 patients. CLD patients were identified using diagnosis codes for bacterial pneumonia, viral pneumonia, influenza, unspecified pneumonia and acute respiratory distress syndrome (ARDS), 2008-2019. A total of 16 cohorts were constructed, including any combinations of the four diseases plus an exploratory ARDS cohort, to determine the most appropriate cohort to use. Candidate predictors included demographic and clinical parameters that were previously associated with poor COVID-19 outcomes. Model development included the implementation of logistic regression and three ensemble tree-based algorithms: decision tree, AdaBoost, and XGBoost. Models were validated in hospitalized COVID-19 patients at two healthcare systems, March 2020-July 2020. ML models were trained on CLD patients at Stanford Hospital Alliance (SHA). Models were validated on hospitalized COVID-19 patients at both SHA and Intermountain Healthcare. RESULTS: CLD training data were obtained from SHA (n = 14,030), and validation data included 444 adult COVID-19 hospitalized patients from SHA (n = 185) and Intermountain (n = 259). XGBoost was the top-performing ML model, and among the 16 CLD training cohorts, the best model achieved an area under curve (AUC) of 0.883 in the validation set. In COVID-19 patients, the prediction models exhibited moderate discrimination performance, with the best models achieving an AUC of 0.77 at SHA and 0.65 at Intermountain. The model trained on all pneumonia and influenza cohorts had the best overall performance (SHA: positive predictive value (PPV) 0.29, negative predictive value (NPV) 0.97, positive likelihood ratio (PLR) 10.7; Intermountain: PPV, 0.23, NPV 0.97, PLR 10.3). We identified important factors associated with IMV that are not traditionally considered for respiratory diseases. CONCLUSIONS: The performance of prediction models derived from CLD for 48-hour IMV in patients hospitalized with COVID-19 demonstrate high specificity and can be used as a triage tool at point of care. Novel predictors of IMV identified in COVID-19 are often overlooked in clinical practice. Lessons learned from our approach may assist other research institutes seeking to build artificial intelligence technologies for novel or rare diseases with limited data for training and validation.


Subject(s)
COVID-19 , Respiratory Insufficiency , Adult , Artificial Intelligence , Hospitalization , Humans , Respiratory Insufficiency/diagnosis , Respiratory Insufficiency/therapy , Retrospective Studies , SARS-CoV-2 , Triage , Ventilators, Mechanical
6.
Crit Care ; 25(1): 50, 2021 02 06.
Article in English | MEDLINE | ID: covidwho-1068599

ABSTRACT

BACKGROUND: Although the immune function of neutrophils in sepsis has been well described, the heterogeneity of neutrophils remains unclear during the process of sepsis. METHODS: In this study, we used a mouse CLP model to simulate the clinical scenario of patients with sepsis, neutrophil infiltration, abnormal distribution and dysfunction was analyzed. LPS was used to stimulate neutrophils in vitro to simulate sepsis; single-cell gene sequencing technology was used to explore the immunological typing. To explore the immunological function of immunosuppressive neutrophils, PD-L1 knockout neutrophils were cocultured with lymphocytes from wild-type mice. RESULTS: We found that neutrophils presented variant dysfunction at the late stage of sepsis, including inhibition of apoptosis, seriously damaged chemotaxis and extensive infiltration into the tissues. Single-cell RNA sequencing revealed that multiple subclusters of neutrophils were differentiated after LPS stimulation. The two-dimensional spatial distribution analysis showed that Foxp3+ T cells were much closer to Ly-6G than the CD4+ and CD8+ cells, indicating that infiltrated neutrophils may play immunomodulatory effect on surrounding T-regs. Further observations showed that LPS mediates PD-L1 over expression through p38α-MSK1/-MK2 pathway in neutrophils. The subsets of highly expressed PD-L1 exert immunosuppressive effect under direct contact mode, including inhibition of T cell activation and induction of T cell apoptosis and trans-differentiation. CONCLUSIONS: Taken together, our data identify a previously unknown immunosuppressive subset of neutrophils as inhibitory neutrophil in order to more accurately describe the phenotype and characteristics of these cells in sepsis.


Subject(s)
Genetic Heterogeneity , Neutrophils/classification , Sepsis/blood , Animals , Disease Models, Animal , Leukocyte Count/methods , Leukocyte Count/statistics & numerical data , Mice , Mice, Inbred C57BL , Neutrophils/physiology , Polymerase Chain Reaction/methods , Sepsis/genetics
7.
Front Med (Lausanne) ; 7: 518, 2020.
Article in English | MEDLINE | ID: covidwho-760866

ABSTRACT

Background: Despite an increase in the familiarity of the medical community with the epidemiological and clinical characteristics of coronavirus disease 2019 (COVID-19), there is presently a lack of rapid and effective risk stratification indicators to predict the poor clinical outcomes of COVID-19 especially in severe patients. Methods: In this retrospective single-center study, we included 117 cases confirmed with COVID-19. The clinical, laboratory, and imaging features were collected and analyzed during admission. The Multi-lobular infiltration, hypo-Lymphocytosis, Bacterial coinfection, Smoking history, hyper-Tension and Age (MuLBSTA) Score and Confusion, Urea, Respiratory rate, Blood pressure, Age 65 (CURB65) score were used to assess the death and intensive care unit (ICU) risks in all patients. Results: Among of all 117 hospitalized patients, 21 (17.9%) patients were admitted to the ICU care, and 5 (4.3%) patients were died. The median hospital stay was 12 (10-15) days. There were 18 patients with MuLBSTA score ≥ 12 points and were all of severe type. In severe type, ICU care and death patients, the proportion with MuLBSTA ≥ 12 points were greater than that of CURB65 score ≥ 3 points (severe type patients, 50 vs. 27.8%; ICU care, 61.9 vs. 19.0%; death, 100 vs. 40%). For the MuLBSTA score, the ROC curve showed good efficiency of diagnosis death (area under the curve [AUC], 0.956; cutoff value, 12; specificity, 89.5%; sensitivity, 100%) and ICU care (AUC, 0.875; cutoff value, 11; specificity, 91.7%; sensitivity, 71.4%). The K-M survival analysis showed that patients with MuLBSTA score ≥ 12 had higher risk of ICU (log-rank, P = 0.001) and high risk of death (log-rank, P = 0.000). Conclusions: The MuLBSTA score is valuable for risk stratification and could effectively screen high-risk patients at admission. The higher score at admission have higher risk of ICU care and death in patients infected with COVID.

SELECTION OF CITATIONS
SEARCH DETAIL