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Background: Learning health systems (LHSs) iteratively generate evidence that can be implemented into practice to improve care and produce generalizable knowledge. Pragmatic clinical trials fit well within LHSs as they combine real-world data and experiences with a degree of methodological rigor which supports generalizability. Objectives: We established a pragmatic clinical trial unit ("RapidEval") to support the development of an LHS. To further advance the field of LHS, we sought to further characterize the role of health information technology (HIT), including innovative solutions and challenges that occur, to improve LHS project delivery. Methods: During the period from December 2021 to February 2023, eight projects were selected out of 51 applications to the RapidEval program, of which five were implemented, one is currently in pilot testing, and two are in planning. We evaluated pre-study planning, implementation, analysis, and study closure approaches across all RapidEval initiatives to summarize approaches across studies and identify key innovations and learnings by gathering data from study investigators, quality staff, and IT staff, as well as RapidEval staff and leadership. Implementation Results: Implementation approaches spanned a range of HIT capabilities including interruptive alerts, clinical decision support integrated into order systems, patient navigators, embedded micro-education, targeted outpatient hand-off documentation, and patient communication. Study approaches include pre-post with time-concordant controls (1), randomized stepped-wedge (1), cluster randomized across providers (1) and location (3), and simple patient level randomization (2). Conclusions: Study selection, design, deployment, data collection, and analysis required close collaboration between data analysts, informaticists, and the RapidEval team.
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OBJECTIVES: To develop an electronic descriptor of clinical deterioration for hospitalized patients that predicts short-term mortality and identifies patient deterioration earlier than current standard definitions. DESIGN: A retrospective study using exploratory record review, quantitative analysis, and regression analyses. SETTING: Twelve-hospital community-academic health system. PATIENTS: All adult patients with an acute hospital encounter between January 1, 2018, and December 31, 2022. INTERVENTIONS: Not applicable. MEASUREMENTS AND MAIN RESULTS: Clinical trigger events were selected and used to create a revised electronic definition of deterioration, encompassing signals of respiratory failure, bleeding, and hypotension occurring in proximity to ICU transfer. Patients meeting the revised definition were 12.5 times more likely to die within 7 days (adjusted odds ratio 12.5; 95% CI, 8.9-17.4) and had a 95.3% longer length of stay (95% CI, 88.6-102.3%) compared with those who were transferred to the ICU or died regardless of meeting the revised definition. Among the 1812 patients who met the revised definition of deterioration before ICU transfer (52.4%), the median detection time was 157.0 min earlier (interquartile range 64.0-363.5 min). CONCLUSIONS: The revised definition of deterioration establishes an electronic descriptor of clinical deterioration that is strongly associated with short-term mortality and length of stay and identifies deterioration over 2.5 hours earlier than ICU transfer. Incorporating the revised definition of deterioration into the training and validation of early warning system algorithms may enhance their timeliness and clinical accuracy.
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Deterioração Clínica , Unidades de Terapia Intensiva , Transferência de Pacientes , Humanos , Masculino , Transferência de Pacientes/estatística & dados numéricos , Estudos Retrospectivos , Feminino , Pessoa de Meia-Idade , Idoso , Mortalidade Hospitalar , Tempo de Internação/estatística & dados numéricos , AdultoRESUMO
Post-acute sequelae of SARS CoV-2 (PASC) are a group of conditions in which patients previously infected with COVID-19 experience symptoms weeks/months post-infection. PASC has substantial societal burden, including increased healthcare costs and disabilities. This study presents a natural language processing (NLP) based pipeline for identification of PASC symptoms and demonstrates its ability to estimate the proportion of suspected PASC cases. A manual case review to obtain this estimate indicated our sample incidence of PASC (13%) was representative of the estimated population proportion (95% CI: 19±6.22%). However, the high number of cases classified as indeterminate demonstrates the challenges in classifying PASC even among experienced clinicians. Lastly, this study developed a dashboard to display views of aggregated PASC symptoms and measured its utility using the System Usability Scale. Overall comments related to the dashboard's potential were positive. This pipeline is crucial for monitoring post-COVID-19 patients with potential for use in clinical settings.
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COVID-19 , Humanos , COVID-19/epidemiologia , Processamento de Linguagem Natural , SARS-CoV-2 , Progressão da Doença , Custos de Cuidados de SaúdeRESUMO
Background: Trials have shown non-inferiority of non-operative management (NOM) for appendicitis, although critically ill patients have been often excluded. The purpose of this study is to evaluate surgical versus NOM outcomes in critically ill patients with appendicitis by measuring mortality and hospital length of stay (LOS). Patients and Methods: The Healthcare Cost and Utilization Project's (HCUP) Database was utilized to analyze data from 10 states between 2008 and 2015. All patients with acute appendicitis by International Classification of Diseases, Ninth Revision (ICD-9) codes over the age of 18 were included. Negative binomial and logistic regression were used to determine the association of acute renal failure (ARF), cardiovascular failure (CVF), pulmonary failure (PF), and sepsis by treatment strategy (laparoscopic, open, both, or no surgery) on mortality and hospital LOS. Results: Among 464,123 patients, 67.5%, 23.3%, 8.2%, and 0.8% underwent laparoscopic, open, NOM, or both laparoscopic and open surgery, respectively. Patients who underwent surgery had 58% lower odds of mortality and 34% shorter hospital LOS compared with NOM patients. Patients with ARF, CVF, PF, and sepsis had 102%, 383%, 475%, and 666% higher odds of mortality and a 47%, 46%, 71%, and 163% longer hospital LOS, respectively, compared with patients without these diagnoses on admission. Conclusions: Critical illness on admission increases mortality and hospital LOS. Patients who underwent laparoscopic, and to a lesser extent, open appendectomy had improved mortality compared with those who did not undergo surgery regardless of critical illness status.
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Apendicite , Laparoscopia , Sepse , Humanos , Adulto , Pessoa de Meia-Idade , Estado Terminal , Apendicite/cirurgia , Apendicite/diagnóstico , Tempo de Internação , Doença Aguda , Apendicectomia/efeitos adversos , Sepse/etiologia , Estudos Retrospectivos , Resultado do TratamentoRESUMO
Significant progress has been made in preventing severe COVID-19 disease through the development of vaccines. However, we still lack a validated baseline predictive biologic signature for the development of more severe disease in both outpatients and inpatients infected with SARS-CoV-2. The objective of this study was to develop and externally validate, via 5 international outpatient and inpatient trials and/or prospective cohort studies, a novel baseline proteomic signature, which predicts the development of moderate or severe (vs mild) disease in patients with COVID-19 from a proteomic analysis of 7000 + proteins. The secondary objective was exploratory, to identify (1) individual baseline protein levels and/or (2) protein level changes within the first 2 weeks of acute infection that are associated with the development of moderate/severe (vs mild) disease. For model development, samples collected from 2 randomized controlled trials were used. Plasma was isolated and the SomaLogic SomaScan platform was used to characterize protein levels for 7301 proteins of interest for all studies. We dichotomized 113 patients as having mild or moderate/severe COVID-19 disease. An elastic net approach was used to develop a predictive proteomic signature. For validation, we applied our signature to data from three independent prospective biomarker studies. We found 4110 proteins measured at baseline that significantly differed between patients with mild COVID-19 and those with moderate/severe COVID-19 after adjusting for multiple hypothesis testing. Baseline protein expression was associated with predicted disease severity with an error rate of 4.7% (AUC = 0.964). We also found that five proteins (Afamin, I-309, NKG2A, PRS57, LIPK) and patient age serve as a signature that separates patients with mild COVID-19 and patients with moderate/severe COVID-19 with an error rate of 1.77% (AUC = 0.9804). This panel was validated using data from 3 external studies with AUCs of 0.764 (Harvard University), 0.696 (University of Colorado), and 0.893 (Karolinska Institutet). In this study we developed and externally validated a baseline COVID-19 proteomic signature associated with disease severity for potential use in both outpatients and inpatients with COVID-19.
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COVID-19 , Humanos , Estudos Prospectivos , SARS-CoV-2 , Proteômica , BiomarcadoresRESUMO
BACKGROUND Methicillin-resistant Staphylococcus aureus (MRSA) pneumonia is associated with high morbidity and mortality. Recently, MRSA testing by nasal swab has been utilized to "exclude" pneumonia caused by MRSA, given its high negative-predictive value (NPV). We present, however, a case of MRSA pneumonia diagnosed by endotracheal aspirate culture (EAC) in a patient with a negative MRSA nasal swab. CASE REPORT A 58-year-old woman presented with septic shock and respiratory failure. Chest X-ray (CXR) on admission was unrevealing; however, computed tomography (CT) revealed multifocal pneumonia. Intensive Care Unit (ICU)-level care was required for mechanical ventilation and vasopressors. She initially improved with treatment of community-acquired pneumonia (CAP) and was extubated on hospital day 6; however, she then developed a fever, tachycardia, and respiratory distress necessitating re-intubation later that day. Repeat CXR demonstrated a new left lower lobe infiltrate. Blood cultures were drawn and vancomycin and cefepime were started to cover for ventilator-associated pathogens. An EAC and nasal swab were collected to test for MRSA. The next day (day 7), the MRSA nasal swab returned negative, and vancomycin was discontinued. Our patient continued to experience fevers, worsening leukocytosis, and ongoing vasopressor need. On hospital day 9, the EAC results were obtained, and were positive for MRSA. Vancomycin was restarted and our patient recovered. CONCLUSIONS Negative MRSA nasal screening may be considered grounds to de-escalate empiric MRSA antibiotics if MRSA prevalence is low. However, in critically ill patients with high risk and suspicion for MRSA pneumonia, discontinuing empiric MRSA coverage should be done with caution or clinicians should wait until respiratory culture results are obtained before de-escalating antibiotics.
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Staphylococcus aureus Resistente à Meticilina , Pneumonia Estafilocócica , Infecções Estafilocócicas , Feminino , Humanos , Pessoa de Meia-Idade , Vancomicina , Estudos Retrospectivos , Pneumonia Estafilocócica/diagnóstico , Pneumonia Estafilocócica/tratamento farmacológico , Pneumonia Estafilocócica/epidemiologia , Antibacterianos/uso terapêutico , Ventiladores Mecânicos , Infecções Estafilocócicas/diagnóstico , Infecções Estafilocócicas/tratamento farmacológicoRESUMO
BACKGROUND: Mortality prediction for intensive care unit (ICU) patients frequently relies on single ICU admission acuity measures without accounting for subsequent clinical changes. OBJECTIVE: Evaluate novel models incorporating modified admission and daily, time-updating Laboratory-based Acute Physiology Score, version 2 (LAPS2) to predict in-hospital mortality among ICU patients. RESEARCH DESIGN: Retrospective cohort study. PATIENTS: ICU patients in 5 hospitals from October 2017 through September 2019. MEASURES: We used logistic regression, penalized logistic regression, and random forest models to predict in-hospital mortality within 30 days of ICU admission using admission LAPS2 alone in patient-level and patient-day-level models, or admission and daily LAPS2 at the patient-day level. Multivariable models included patient and admission characteristics. We performed internal-external validation using 4 hospitals for training and the fifth for validation, repeating analyses for each hospital as the validation set. We assessed performance using scaled Brier scores (SBS), c -statistics, and calibration plots. RESULTS: The cohort included 13,993 patients and 107,699 ICU days. Across validation hospitals, patient-day-level models including daily LAPS2 (SBS: 0.119-0.235; c -statistic: 0.772-0.878) consistently outperformed models with admission LAPS2 alone in patient-level (SBS: 0.109-0.175; c -statistic: 0.768-0.867) and patient-day-level (SBS: 0.064-0.153; c -statistic: 0.714-0.861) models. Across all predicted mortalities, daily models were better calibrated than models with admission LAPS2 alone. CONCLUSIONS: Patient-day-level models incorporating daily, time-updating LAPS2 to predict mortality among an ICU population performs as well or better than models incorporating modified admission LAPS2 alone. The use of daily LAPS2 may offer an improved tool for clinical prognostication and risk adjustment in research in this population.
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Cuidados Críticos , Unidades de Terapia Intensiva , Humanos , Estudos Retrospectivos , Mortalidade Hospitalar , HospitalizaçãoRESUMO
IMPORTANCE: The SARS-CoV-2 pandemic has overwhelmed hospital capacity, prioritizing the need to understand factors associated with type of discharge disposition. OBJECTIVE: Characterization of disposition associated factors following SARS-CoV-2. DESIGN: Retrospective study of SARS-CoV-2 positive patients from March 7th, 2020, to May 4th, 2022, requiring hospitalization. SETTING: Midwest academic health-system. PARTICIPANTS: Patients above the age 18 years admitted with PCR + SARS-CoV-2. INTERVENTION: None. MAIN OUTCOMES: Discharge to home versus PAC (inpatient rehabilitation facility (IRF), skilled-nursing facility (SNF), long-term acute care (LTACH)), or died/hospice while hospitalized (DH). RESULTS: We identified 62,279 SARS-CoV-2 PCR+ patients; 6,248 required hospitalizations, of whom 4611(73.8%) were discharged home, 985 (15.8%) to PAC and 652 (10.4%) died in hospital (DH). Patients discharged to PAC had a higher median age (75.7 years, IQR: 65.6-85.1) compared to those discharged home (57.0 years, IQR: 38.2-69.9), and had longer mean length of stay (LOS) 14.7 days, SD: 14.0) compared to discharge home (5.8 days, SD: 5.9). Older age (RRR:1.04, 95% CI:1.041-1.055), and higher Elixhauser comorbidity index [EI] (RRR:1.19, 95% CI:1.168-1.218) were associated with higher rate of discharge to PAC versus home. Older age (RRR:1.069, 95% CI:1.060-1.077) and higher EI (RRR:1.09, 95% CI:1.071-1.126) were associated with more frequent DH versus home. Blacks, Asians, and Hispanics were less likely to be discharged to PAC (RRR, 0.64 CI 0.47-0.88), (RRR 0.48 CI 0.34-0.67) and (RRR 0.586 CI 0.352-0.975). Having alpha variant was associated with less frequent PAC discharge versus home (RRR 0.589 CI 0.444-780). The relative risks for DH were lower with a higher platelet count 0.998 (CI 0.99-0.99) and albumin levels 0.342 (CI 0.26-0.45), and higher with increased CRP (RRR 1.006 CI 1.004-1.007) and D-Dimer (RRR 1.070 CI 1.039-1.101). Increased albumin had lower risk to PAC discharge (RRR 0.630 CI 0.497-0.798. An increase in D-Dimer (RRR1.033 CI 1.002-1.064) and CRP (RRR1.002 CI1.001-1.004) was associated with higher risk of PAC discharge. A breakthrough (BT) infection was associated with lower likelihood of DH and PAC. CONCLUSION: Older age, higher EI, CRP and D-Dimer are associated with PAC and DH discharges following hospitalization with COVID-19 infection. BT infection reduces the likelihood of being discharged to PAC and DH.
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COVID-19 , Hospitais para Doentes Terminais , Humanos , Idoso , Idoso de 80 Anos ou mais , Adolescente , Alta do Paciente , Estudos Retrospectivos , COVID-19/epidemiologia , SARS-CoV-2/genética , Hospitalização , AlbuminasRESUMO
Background: Mortality prediction for intensive care unit (ICU) patients frequently relies on single acuity measures based on ICU admission physiology without accounting for subsequent clinical changes. Objectives: Evaluate novel models incorporating modified admission and daily, time-updating Laboratory-based Acute Physiology Scores, version 2 (LAPS2) to predict in-hospital mortality among ICU patients. Research design: Retrospective cohort study. Subjects: All ICU patients in five hospitals from October 2017 through September 2019. Measures: We used logistic regression, penalized logistic regression, and random forest models to predict in-hospital mortality within 30 days of ICU admission using admission LAPS2 alone in patient-level and patient-day-level models, or admission and daily LAPS2 at the patient-day level. Multivariable models included patient and admission characteristics. We performed internal-external validation using four hospitals for training and the fifth for validation, repeating analyses for each hospital as the validation set. We assessed performance using scaled Brier scores (SBS), c-statistics, and calibration plots. Results: The cohort included 13,993 patients and 120,101 ICU days. The patient-level model including the modified admission LAPS2 without daily LAPS2 had an SBS of 0.175 (95% CI 0.148-0.201) and c-statistic of 0.824 (95% CI 0.808-0.840). Patient-day-level models including daily LAPS2 consistently outperformed models with modified admission LAPS2 alone. Among patients with <50% predicted mortality, daily models were better calibrated than models with modified admission LAPS2 alone. Conclusions: Models incorporating daily, time-updating LAPS2 to predict mortality among an ICU population perform as well or better than models incorporating modified admission LAPS2 alone.
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OBJECTIVE: We critically evaluated the surgical literature to explore the prevalence and describe how equity assessments occur when using clinical decision support systems. BACKGROUND: Clinical decision support (CDS) systems are increasingly used to facilitate surgical care delivery. Despite formal recommendations to do so, equity evaluations are not routinely performed on CDS systems and underrepresented populations are at risk of harm and further health disparities. We explored surgical literature to determine frequency and rigor of CDS equity assessments and offer recommendations to improve CDS equity by appending existing frameworks. METHODS: We performed a scoping review up to Augus 25, 2021 using PubMed and Google Scholar for the following search terms: clinical decision support, implementation, RE-AIM, Proctor, Proctor's framework, equity, trauma, surgery, surgical. We identified 1415 citations and 229 abstracts met criteria for review. A total of 84 underwent full review after 145 were excluded if they did not assess outcomes of an electronic CDS tool or have a surgical use case. RESULTS: Only 6% (5/84) of surgical CDS systems reported equity analyses, suggesting that current methods for optimizing equity in surgical CDS are inadequate. We propose revising the RE-AIM framework to include an Equity element (RE 2 -AIM) specifying that CDS foundational analyses and algorithms are performed or trained on balanced datasets with sociodemographic characteristics that accurately represent the CDS target population and are assessed by sensitivity analyses focused on vulnerable subpopulations. CONCLUSION: Current surgical CDS literature reports little with respect to equity. Revising the RE-AIM framework to include an Equity element (RE 2 -AIM) promotes the development and implementation of CDS systems that, at minimum, do not worsen healthcare disparities and possibly improve their generalizability.
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Sistemas de Apoio a Decisões Clínicas , Disparidades em Assistência à Saúde , Humanos , Necessidades e Demandas de Serviços de Saúde , Populações VulneráveisRESUMO
BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccination has decreasing protection from acquiring any infection with emergence of new variants; however, vaccination continues to protect against progression to severe coronavirus disease 2019 (COVID-19). The impact of vaccination status on symptoms over time is less clear. METHODS: Within a randomized trial on early outpatient COVID-19 therapy testing metformin, ivermectin, and/or fluvoxamine, participants recorded symptoms daily for 14 days. Participants were given a paper symptom diary allowing them to circle the severity of 14 symptoms as none (0), mild (1), moderate (2), or severe (3). This is a secondary analysis of clinical trial data on symptom severity over time using generalized estimating equations comparing those unvaccinated, SARS-CoV-2 vaccinated with primary vaccine series only, or vaccine-boosted. RESULTS: The parent clinical trial prospectively enrolled 1323 participants, of whom 1062 (80%) prospectively recorded some daily symptom data. Of these, 480 (45%) were unvaccinated, 530 (50%) were vaccinated with primary series only, and 52 (5%) vaccine-boosted. Overall symptom severity was least for the vaccine-boosted group and most severe for unvaccinated at baseline and over the 14 days (P < .001). Individual symptoms were least severe in the vaccine-boosted group including cough, chills, fever, nausea, fatigue, myalgia, headache, and diarrhea, as well as smell and taste abnormalities. Results were consistent over Delta and Omicron variant time periods. CONCLUSIONS: SARS-CoV-2 vaccine-boosted participants had the least severe symptoms during COVID-19, which abated the quickest over time. Clinical Trial Registration. NCT04510194.
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COVID-19 , SARS-CoV-2 , Humanos , COVID-19/prevenção & controle , Vacinas contra COVID-19 , VacinaçãoRESUMO
BACKGROUND: Early treatment to prevent severe coronavirus disease 2019 (Covid-19) is an important component of the comprehensive response to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic. METHODS: In this phase 3, double-blind, randomized, placebo-controlled trial, we used a 2-by-3 factorial design to test the effectiveness of three repurposed drugs - metformin, ivermectin, and fluvoxamine - in preventing serious SARS-CoV-2 infection in nonhospitalized adults who had been enrolled within 3 days after a confirmed diagnosis of infection and less than 7 days after the onset of symptoms. The patients were between the ages of 30 and 85 years, and all had either overweight or obesity. The primary composite end point was hypoxemia (≤93% oxygen saturation on home oximetry), emergency department visit, hospitalization, or death. All analyses used controls who had undergone concurrent randomization and were adjusted for SARS-CoV-2 vaccination and receipt of other trial medications. RESULTS: A total of 1431 patients underwent randomization; of these patients, 1323 were included in the primary analysis. The median age of the patients was 46 years; 56% were female (6% of whom were pregnant), and 52% had been vaccinated. The adjusted odds ratio for a primary event was 0.84 (95% confidence interval [CI], 0.66 to 1.09; P = 0.19) with metformin, 1.05 (95% CI, 0.76 to 1.45; P = 0.78) with ivermectin, and 0.94 (95% CI, 0.66 to 1.36; P = 0.75) with fluvoxamine. In prespecified secondary analyses, the adjusted odds ratio for emergency department visit, hospitalization, or death was 0.58 (95% CI, 0.35 to 0.94) with metformin, 1.39 (95% CI, 0.72 to 2.69) with ivermectin, and 1.17 (95% CI, 0.57 to 2.40) with fluvoxamine. The adjusted odds ratio for hospitalization or death was 0.47 (95% CI, 0.20 to 1.11) with metformin, 0.73 (95% CI, 0.19 to 2.77) with ivermectin, and 1.11 (95% CI, 0.33 to 3.76) with fluvoxamine. CONCLUSIONS: None of the three medications that were evaluated prevented the occurrence of hypoxemia, an emergency department visit, hospitalization, or death associated with Covid-19. (Funded by the Parsemus Foundation and others; COVID-OUT ClinicalTrials.gov number, NCT04510194.).
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Tratamento Farmacológico da COVID-19 , COVID-19 , Fluvoxamina , Ivermectina , Metformina , Adulto , Idoso , Idoso de 80 Anos ou mais , COVID-19/complicações , Vacinas contra COVID-19 , Método Duplo-Cego , Feminino , Fluvoxamina/uso terapêutico , Humanos , Hipóxia/etiologia , Ivermectina/uso terapêutico , Masculino , Metformina/uso terapêutico , Pessoa de Meia-Idade , Obesidade/complicações , Sobrepeso/complicações , Gravidez , Complicações Infecciosas na Gravidez/tratamento farmacológico , SARS-CoV-2RESUMO
Purpose: To conduct a prospective observational study across 12 U.S. hospitals to evaluate real-time performance of an interpretable artificial intelligence (AI) model to detect COVID-19 on chest radiographs. Materials and Methods: A total of 95 363 chest radiographs were included in model training, external validation, and real-time validation. The model was deployed as a clinical decision support system, and performance was prospectively evaluated. There were 5335 total real-time predictions and a COVID-19 prevalence of 4.8% (258 of 5335). Model performance was assessed with use of receiver operating characteristic analysis, precision-recall curves, and F1 score. Logistic regression was used to evaluate the association of race and sex with AI model diagnostic accuracy. To compare model accuracy with the performance of board-certified radiologists, a third dataset of 1638 images was read independently by two radiologists. Results: Participants positive for COVID-19 had higher COVID-19 diagnostic scores than participants negative for COVID-19 (median, 0.1 [IQR, 0.0-0.8] vs 0.0 [IQR, 0.0-0.1], respectively; P < .001). Real-time model performance was unchanged over 19 weeks of implementation (area under the receiver operating characteristic curve, 0.70; 95% CI: 0.66, 0.73). Model sensitivity was higher in men than women (P = .01), whereas model specificity was higher in women (P = .001). Sensitivity was higher for Asian (P = .002) and Black (P = .046) participants compared with White participants. The COVID-19 AI diagnostic system had worse accuracy (63.5% correct) compared with radiologist predictions (radiologist 1 = 67.8% correct, radiologist 2 = 68.6% correct; McNemar P < .001 for both). Conclusion: AI-based tools have not yet reached full diagnostic potential for COVID-19 and underperform compared with radiologist prediction.Keywords: Diagnosis, Classification, Application Domain, Infection, Lung Supplemental material is available for this article.. © RSNA, 2022.
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OBJECTIVE: To examine the frequency of postacute sequelae of SARS-CoV-2 (PASC) and the factors associated with rehabilitation utilization in a large adult population with PASC. DESIGN: Retrospective study. SETTING: Midwest hospital health system. PARTICIPANTS: 19,792 patients with COVID-19 from March 10, 2020, to January 17, 2021. INTERVENTION: Not applicable. MAIN OUTCOME MEASURES: Descriptive analyses were conducted across the entire cohort along with an adult subgroup analysis. A logistic regression was performed to assess factors associated with PASC development and rehabilitation utilization. RESULTS: In an analysis of 19,792 patients, the frequency of PASC was 42.8% in the adult population. Patients with PASC compared with those without had a higher utilization of rehabilitation services (8.6% vs 3.8%, P<.001). Risk factors for rehabilitation utilization in patients with PASC included younger age (odds ratio [OR], 0.99; 95% confidence interval [CI], 0.98-1.00; P=.01). In addition to several comorbidities and demographics factors, risk factors for rehabilitation utilization solely in the inpatient population included male sex (OR, 1.24; 95% CI, 1.02-1.50; P=.03) with patients on angiotensin-converting-enzyme inhibitors or angiotensin-receptor blockers 3 months prior to COVID-19 infections having a decreased risk of needing rehabilitation (OR, 0.80; 95% CI, 0.64-0.99; P=.04). CONCLUSIONS: Patients with PASC had higher rehabilitation utilization. We identified several clinical and demographic factors associated with the development of PASC and rehabilitation utilization.
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COVID-19 , Adulto , Inibidores da Enzima Conversora de Angiotensina , Angiotensinas , COVID-19/epidemiologia , Humanos , Masculino , Estudos Retrospectivos , SARS-CoV-2RESUMO
Importance: SARS-CoV-2 viral entry may disrupt angiotensin II (AII) homeostasis, contributing to COVID-19 induced lung injury. AII type 1 receptor blockade mitigates lung injury in preclinical models, although data in humans with COVID-19 remain mixed. Objective: To test the efficacy of losartan to reduce lung injury in hospitalized patients with COVID-19. Design, Setting, and Participants: This blinded, placebo-controlled randomized clinical trial was conducted in 13 hospitals in the United States from April 2020 to February 2021. Hospitalized patients with COVID-19 and a respiratory sequential organ failure assessment score of at least 1 and not already using a renin-angiotensin-aldosterone system (RAAS) inhibitor were eligible for participation. Data were analyzed from April 19 to August 24, 2021. Interventions: Losartan 50 mg orally twice daily vs equivalent placebo for 10 days or until hospital discharge. Main Outcomes and Measures: The primary outcome was the imputed arterial partial pressure of oxygen to fraction of inspired oxygen (Pao2:Fio2) ratio at 7 days. Secondary outcomes included ordinal COVID-19 severity; days without supplemental o2, ventilation, or vasopressors; and mortality. Losartan pharmacokinetics and RAAS components (AII, angiotensin-[1-7] and angiotensin-converting enzymes 1 and 2)] were measured in a subgroup of participants. Results: A total of 205 participants (mean [SD] age, 55.2 [15.7] years; 123 [60.0%] men) were randomized, with 101 participants assigned to losartan and 104 participants assigned to placebo. Compared with placebo, losartan did not significantly affect Pao2:Fio2 ratio at 7 days (difference, -24.8 [95%, -55.6 to 6.1]; P = .12). Compared with placebo, losartan did not improve any secondary clinical outcomes and led to fewer vasopressor-free days than placebo (median [IQR], 9.4 [9.1-9.8] vasopressor-free days vs 8.7 [8.2-9.3] vasopressor-free days). Conclusions and Relevance: This randomized clinical trial found that initiation of orally administered losartan to hospitalized patients with COVID-19 and acute lung injury did not improve Pao2:Fio2 ratio at 7 days. These data may have implications for ongoing clinical trials. Trial Registration: ClinicalTrials.gov Identifier: NCT04312009.
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Bloqueadores do Receptor Tipo 1 de Angiotensina II/uso terapêutico , Tratamento Farmacológico da COVID-19 , COVID-19/complicações , Losartan/uso terapêutico , Lesão Pulmonar/prevenção & controle , Lesão Pulmonar/virologia , Adulto , Idoso , COVID-19/diagnóstico , Método Duplo-Cego , Feminino , Hospitalização , Humanos , Lesão Pulmonar/diagnóstico , Masculino , Pessoa de Meia-Idade , Escores de Disfunção Orgânica , Testes de Função Respiratória , Estados UnidosRESUMO
Sepsis-induced metabolic dysfunction is associated with mortality, but the signatures that differentiate variable clinical outcomes among survivors are unknown. Our aim was to determine the relationship between host metabolism and chronic critical illness (CCI) in patients with septic shock. We analyzed metabolomics data from mechanically ventilated patients with vasopressor-dependent septic shock from the placebo arm of a recently completed clinical trial. Baseline serum metabolites were measured by liquid chromatography-mass spectrometry and 1H-nuclear magnetic resonance. We conducted a time-to-event analysis censored at 28 days. Specifically, we determined the relationship between metabolites and time to extubation and freedom from vasopressors using a competing risk survival model, with death as a competing risk. We also compared metabolite concentrations between CCI patients, defined as intensive care unit level of care ≥ 14 days, and those with rapid recovery. Elevations in two acylcarnitines and four amino acids were related to the freedom from organ support (subdistributional hazard ratio < 1 and false discovery rate < 0.05). Proline, glycine, glutamine, and methionine were also elevated in patients who developed CCI. Our work highlights the need for further testing of metabolomics to identify patients at risk of CCI and to elucidate potential mechanisms that contribute to its etiology.
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OBJECTIVE: To prospectively evaluate a logistic regression-based machine learning (ML) prognostic algorithm implemented in real-time as a clinical decision support (CDS) system for symptomatic persons under investigation (PUI) for Coronavirus disease 2019 (COVID-19) in the emergency department (ED). METHODS: We developed in a 12-hospital system a model using training and validation followed by a real-time assessment. The LASSO guided feature selection included demographics, comorbidities, home medications, vital signs. We constructed a logistic regression-based ML algorithm to predict "severe" COVID-19, defined as patients requiring intensive care unit (ICU) admission, invasive mechanical ventilation, or died in or out-of-hospital. Training data included 1,469 adult patients who tested positive for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) within 14 days of acute care. We performed: 1) temporal validation in 414 SARS-CoV-2 positive patients, 2) validation in a PUI set of 13,271 patients with symptomatic SARS-CoV-2 test during an acute care visit, and 3) real-time validation in 2,174 ED patients with PUI test or positive SARS-CoV-2 result. Subgroup analysis was conducted across race and gender to ensure equity in performance. RESULTS: The algorithm performed well on pre-implementation validations for predicting COVID-19 severity: 1) the temporal validation had an area under the receiver operating characteristic (AUROC) of 0.87 (95%-CI: 0.83, 0.91); 2) validation in the PUI population had an AUROC of 0.82 (95%-CI: 0.81, 0.83). The ED CDS system performed well in real-time with an AUROC of 0.85 (95%-CI, 0.83, 0.87). Zero patients in the lowest quintile developed "severe" COVID-19. Patients in the highest quintile developed "severe" COVID-19 in 33.2% of cases. The models performed without significant differences between genders and among race/ethnicities (all p-values > 0.05). CONCLUSION: A logistic regression model-based ML-enabled CDS can be developed, validated, and implemented with high performance across multiple hospitals while being equitable and maintaining performance in real-time validation.
Assuntos
COVID-19/diagnóstico , Sistemas de Apoio a Decisões Clínicas , Modelos Logísticos , Aprendizado de Máquina , Triagem/métodos , COVID-19/fisiopatologia , Serviço Hospitalar de Emergência , Humanos , Curva ROC , Índice de Gravidade de DoençaRESUMO
OBJECTIVES: The COVID-19 pandemic stressed hospital operations, requiring rapid innovations to address rise in demand and specialized COVID-19 services while maintaining access to hospital-based care and facilitating expertise. We aimed to describe a novel hospital system approach to managing the COVID-19 pandemic, including multihospital coordination capability and transfer of COVID-19 patients to a single, dedicated hospital. METHODS: We included patients who tested positive for SARS-CoV-2 by polymerase chain reaction admitted to a 12-hospital network including a dedicated COVID-19 hospital. Our primary outcome was adherence to local guidelines, including admission risk stratification, anticoagulation, and dexamethasone treatment assessed by differences-in-differences analysis after guideline dissemination. We evaluated outcomes and health care worker satisfaction. Finally, we assessed barriers to safe transfer including transfer across different electronic health record systems. RESULTS: During the study, the system admitted a total of 1209 patients. Of these, 56.3% underwent transfer, supported by a physician-led System Operations Center. Patients who were transferred were older (P = 0.001) and had similar risk-adjusted mortality rates. Guideline adherence after dissemination was higher among patients who underwent transfer: admission risk stratification (P < 0.001), anticoagulation (P < 0.001), and dexamethasone administration (P = 0.003). Transfer across electronic health record systems was a perceived barrier to safety and reduced quality. Providers positively viewed our transfer approach. CONCLUSIONS: With standardized communication, interhospital transfers can be a safe and effective method of cohorting COVID-19 patients, are well received by health care providers, and have the potential to improve care quality.