Prevalence of psychiatric morbidity following discharge after COVID-19 hospitalization.
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ABSTRACT:
SUBMITTER: Wang PR
PROVIDER: S-EPMC7834109 | biostudies-literature | 2021 Mar-Apr
REPOSITORIES: biostudies-literature
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Prevalence of psychiatric morbidity following discharge after COVID-19 hospitalization.
Wang Philip R PR Oyem Precious C PC Viguera Adele C AC
General hospital psychiatry 20201228
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Project description:ObjectivesCaring Contacts are an emerging intervention that aims to reduce distress and suicide risk after acute psychiatric care. This trial aimed to determine whether, during a pandemic, there was any evidence that the mental health benefits and reduction in suicidal ideation (SI) associated with delivering Caring Contacts to recently discharged psychiatric patients were greater than a control communication. The secondary objective was to identify whether the predicted benefits were greater among people living alone or those diagnosed with depression.MethodA single-site pilot randomized clinical trial (n = 100), with patients recruited from the adult Inpatient Psychiatry Unit at Sunnybrook Health Sciences Centre, Toronto, Canada between August 2020 and May 2021. Participants were randomized (1:1) to the Caring Contact or control group. Participants received three Caring Contact or control communications via email or mail (on days 4, 21, and 56 post-discharge). Mental health symptoms were assessed using the self-report Hopkins Symptom Checklist-25 (HSCL-25) scores at discharge (baseline) and when participants received each communication. Analysis of variance was used for the primary comparisons and exploratory analyses for subgroups.ResultsBoth groups experienced a significant worsening of mental health symptoms at all time points post-discharge relative to baseline. There were no significant differences between groups at any time point, however, on day 4 there was a 24.2% and 72.6% attenuated worsening in the Caring Contact group compared to the control group for total symptom severity and SI, respectively. There was no significant interaction effect for the depression subgroup or those living alone.ConclusionsWhile this pilot study was not powered to identify significant differences between groups, results are indicative of feasibility and acceptability of the intervention and provide some indication that Caring Contacts may have benefited patients in the days following discharge, supporting the need for larger-scale trials. The study was registered with clinicaltrials.gov (study ID NCT04456062).
| S-EPMC9412148 | biostudies-literature
Project description:BackgroundData on patients with coronavirus disease 2019 (COVID-19) who return to hospital after discharge are scarce. Characterization of these patients may inform post-hospitalization care.ObjectiveTo describe clinical characteristics of patients with COVID-19 who returned to the emergency department (ED) or required readmission within 14 days of discharge.DesignRetrospective cohort study of SARS-COV-2-positive patients with index hospitalization between February 27 and April 12, 2020, with ≥ 14-day follow-up. Significance was defined as P < 0.05 after multiplying P by 125 study-wide comparisons.ParticipantsHospitalized patients with confirmed SARS-CoV-2 discharged alive from five New York City hospitals.Main measuresReadmission or return to ED following discharge.ResultsOf 2864 discharged patients, 103 (3.6%) returned for emergency care after a median of 4.5 days, with 56 requiring inpatient readmission. The most common reason for return was respiratory distress (50%). Compared with patients who did not return, there were higher proportions of COPD (6.8% vs 2.9%) and hypertension (36% vs 22.1%) among those who returned. Patients who returned also had a shorter median length of stay (LOS) during index hospitalization (4.5 [2.9,9.1] vs 6.7 [3.5, 11.5] days; Padjusted = 0.006), and were less likely to have required intensive care on index hospitalization (5.8% vs 19%; Padjusted = 0.001). A trend towards association between absence of in-hospital treatment-dose anticoagulation on index admission and return to hospital was also observed (20.9% vs 30.9%, Padjusted = 0.06). On readmission, rates of intensive care and death were 5.8% and 3.6%, respectively.ConclusionsReturn to hospital after admission for COVID-19 was infrequent within 14 days of discharge. The most common cause for return was respiratory distress. Patients who returned more likely had COPD and hypertension, shorter LOS on index-hospitalization, and lower rates of in-hospital treatment-dose anticoagulation. Future studies should focus on whether these comorbid conditions, longer LOS, and anticoagulation are associated with reduced readmissions.
| S-EPMC7437962 | biostudies-literature
Project description:ObjectiveEmergency department (ED) use has increased disproportionately for pediatric psychiatric care. This study aimed to identify predictors of ED use within 30 days of discharge from a pediatric psychiatric hospitalization.MethodsED use was assessed in the 30 days after discharge. Univariate logistic regression modeling identified predictors of ED use, which were used in subsequent multivariate modeling.ResultsGreater number of trauma types (odds ratio [OR]=1.92, 95% confidence interval [CI]=1.50-2.45, z=2.67, p=0.008), generalized anxiety disorder (OR=3.20, 95% CI=1.78-5.76, z=1.98, p=.048), and longer length of stay (OR=1.05, 95% CI=1.03-1.07, z=2.74, p=0.006) were associated with increased ED use within 30 days of discharge.ConclusionsED use may be an important marker of negative outcomes within 30 days of discharge from pediatric psychiatric hospitalization. Patients with high trauma exposure, anxiety, and acuity marked by increased length of stay may require additional services to prevent unplanned ED use for psychiatric crises.
| S-EPMC6602797 | biostudies-literature
Project description:ObjectiveThe goal of our study was to evaluate the development of new mental health diagnoses up to 6-months following COVID-19 hospitalization for in a large, national sample.MethodData were extracted for all Veterans hospitalized at Veterans Health Administration hospitals for COVID-19 from March through August of 2020 utilizing national administrative data. After identifying the cohort, follow-up data were linked through six months post-hospitalization. Data were analyzed using logistic regression.ResultsEight percent of patients developed a new mental health diagnosis following hospitalization. The most common new mental health diagnoses involved depressive, anxiety, and adjustment disorders. Younger and rural patients were more likely to develop new mental health diagnoses. Women and those with more comorbidities were less likely to develop new diagnoses.ConclusionA subpopulation of patients hospitalized for COVID-19 developed new mental health diagnoses. Unique demographics predictors indicate the potential need for additional outreach and screening to groups at elevated risk of post-hospitalization, mental health sequelae.
| S-EPMC9023376 | biostudies-literature
Project description:BackgroundThe clinical course of COVID-19 includes multiple disease phases. Data describing post-hospital discharge outcomes may provide insight into disease course. Studies describing post-hospitalization outcomes of adults following COVID-19 infection are limited to electronic medical record review, which may underestimate the incidence of outcomes.ObjectiveTo determine 30-day post-hospitalization outcomes following COVID-19 infection.DesignRetrospective cohort study SETTING: Quaternary referral hospital and community hospital in New York City.ParticipantsCOVID-19 infected patients discharged alive from the emergency department (ED) or hospital between March 3 and May 15, 2020.MeasurementOutcomes included return to an ED, re-hospitalization, and mortality within 30 days of hospital discharge.ResultsThirty-day follow-up data were successfully collected on 94.6% of eligible patients. Among 1344 patients, 16.5% returned to an ED, 9.8% were re-hospitalized, and 2.4% died. Among patients who returned to the ED, 50.0% (108/216) went to a different hospital from the hospital of the index presentation, and 61.1% (132/216) of those who returned were re-hospitalized. In Cox models adjusted for variables selected using the lasso method, age (HR 1.01 per year [95% CI 1.00-1.02]), diabetes (1.54 [1.06-2.23]), and the need for inpatient dialysis (3.78 [2.23-6.43]) during the index presentation were independently associated with a higher re-hospitalization rate. Older age (HR 1.08 [1.05-1.11]) and Asian race (2.89 [1.27-6.61]) were significantly associated with mortality.ConclusionsAmong patients discharged alive following their index presentation for COVID-19, risk for returning to a hospital within 30 days of discharge was substantial. These patients merit close post-discharge follow-up to optimize outcomes.
| S-EPMC8183585 | biostudies-literature
Project description:IntroductionMillions of U.S. patients have been hospitalized for COVID-19. After discharge, these patients often have extensive health care needs, but out-of-pocket burden for this care is poorly described. We assessed out-of-pocket spending within 90 days of discharge from COVID-19 hospitalization among privately insured and Medicare Advantage patients.MethodsIn May 2021, we conducted a cross-sectional analysis of the IQVIA PharMetrics ® Plus for Academics Database, a national de-identified claims database. Among privately insured and Medicare Advantage patients hospitalized for COVID-19 between March-June 2020, we calculated mean out-of-pocket spending for care within 90 days of discharge. For context, we repeated analyses for patients hospitalized for pneumonia.ResultsAmong 1,465 COVID-19 patients included, 516 (35.2%) and 949 (64.8%) were covered by private insurance and Medicare Advantage plans. Among these patients, mean (SD) post-discharge out-of-pocket spending was $534 (1,045) and $680 (1,360); spending exceeded $2,000 for 7.0% and 10.3%. Compared with pneumonia patients, mean post-discharge out-of-pocket spending among COVID-19 patients was higher among the privately insured ($534 vs $445) and lower among Medicare Advantage patients ($680 vs $918).ConclusionsFor the privately insured, post-discharge out-of-pocket spending was higher among patients hospitalized for COVID-19 than among patients hospitalized for pneumonia. The opposite was true for Medicare Advantage patients, potentially because insurer cost-sharing waivers for COVID-19 treatment covered the costs of some post-discharge care, such as COVID-19 readmissions. Nonetheless, given the high volume of U.S. COVID-19 hospitalizations to date, our findings suggest a large number of Americans have experienced substantial financial burden for post-discharge care.
| S-EPMC8219107 | biostudies-literature
Project description:BackgroundAccurate prediction models for whether patients on the verge of a psychiatric criseis need hospitalization are lacking and machine learning methods may help improve the accuracy of psychiatric hospitalization prediction models. In this paper we evaluate the accuracy of ten machine learning algorithms, including the generalized linear model (GLM/logistic regression) to predict psychiatric hospitalization in the first 12 months after a psychiatric crisis care contact. We also evaluate an ensemble model to optimize the accuracy and we explore individual predictors of hospitalization.MethodsData from 2084 patients included in the longitudinal Amsterdam Study of Acute Psychiatry with at least one reported psychiatric crisis care contact were included. Target variable for the prediction models was whether the patient was hospitalized in the 12 months following inclusion. The predictive power of 39 variables related to patients' socio-demographics, clinical characteristics and previous mental health care contacts was evaluated. The accuracy and area under the receiver operating characteristic curve (AUC) of the machine learning algorithms were compared and we also estimated the relative importance of each predictor variable. The best and least performing algorithms were compared with GLM/logistic regression using net reclassification improvement analysis and the five best performing algorithms were combined in an ensemble model using stacking.ResultsAll models performed above chance level. We found Gradient Boosting to be the best performing algorithm (AUC = 0.774) and K-Nearest Neighbors to be the least performing (AUC = 0.702). The performance of GLM/logistic regression (AUC = 0.76) was slightly above average among the tested algorithms. In a Net Reclassification Improvement analysis Gradient Boosting outperformed GLM/logistic regression by 2.9% and K-Nearest Neighbors by 11.3%. GLM/logistic regression outperformed K-Nearest Neighbors by 8.7%. Nine of the top-10 most important predictor variables were related to previous mental health care use.ConclusionsGradient Boosting led to the highest predictive accuracy and AUC while GLM/logistic regression performed average among the tested algorithms. Although statistically significant, the magnitude of the differences between the machine learning algorithms was in most cases modest. The results show that a predictive accuracy similar to the best performing model can be achieved when combining multiple algorithms in an ensemble model.
| S-EPMC7731561 | biostudies-literature
Project description:Purpose: To investigate molecular mechanisms of SARS-CoV-2-induced mucin expression and synthesis and test candidate countermeasures. Methods: Bulk RNA-seq was performed on well-differentiated human bronchial epithelial (HBE) cell culture lysates with/without SARS-CoV-2 inoculation. Results: SARS-CoV-2-infected HBE cultures exhibited peak titers 3 days post inoculation, whereas induction of MUC5B/MUC5AC peaked 7-14 days post inoculation. Conclusions: SARS-CoV-2-infection to HBE culture causes mucus goblet cell metaplasia and increased expression of MUC5B-dominated mucin overproduction.
2022-08-03 | GSE210223 | GEO
Project description:ObjectiveThe present study aims to investigate the occurrence of psychiatric and cognitive impairments in a cohort of survivors of moderate or severe forms of COVID-19.Method425 adults were assessed 6 to 9 months after hospital discharge with a structured psychiatric interview, psychometric tests and a cognitive battery. A large, multidisciplinary, set of clinical data depicting the acute phase of the disease, along with relevant psychosocial variables, were used to predict psychiatric and cognitive outcomes using the 'Least Absolute Shrinkage and Selection Operator' (LASSO) method.ResultsDiagnoses of 'depression', 'generalized anxiety disorder' and 'post-traumatic stress disorder' were established respectively in 8%, 15.5% and 13.6% of the sample. After pandemic onset (i.e., within the previous year), the prevalence of 'depression' and 'generalized anxiety disorder' were 2.56% and 8.14%, respectively. Memory decline was subjectively reported by 51.1% of the patients. Psychiatric or cognitive outcomes were not associated with any clinical variables related to the severity of acute-phase disease, nor by disease-related psychosocial stressors.ConclusionsThis is the first study to access rates of psychiatric and cognitive morbidity in the long-term outcome after moderate or severe forms of COVID-19 using standardized measures. As a key finding, there was no significant association between clinical severity in the acute-phase of SARS-CoV-2 infection and the neuropsychiatric impairment 6 to 9 months thereafter.
| S-EPMC8734055 | biostudies-literature
Project description:This retrospective cohort study analyzed the administrative hospital records of 91,500 patients with the aim of assessing adverse drug reaction (ADR)-related hospital admission risk after discharge from ADR and non-ADR-related admission. Patients aged ≥18 years with an acute admission to public hospitals in Tasmania, Australia between 2011 and 2015 were followed until May 2017. The index admissions (n = 91,550) were stratified based on whether they were ADR-related (n = 2843, 3.1%) or non-ADR-related (n = 88,707, 96.9%). Survival analysis assessed the post-index ADR-related admission risk using (1) the full dataset, and (2) a matched subset of patients using a propensity score analysis. Logistic regression was used to identify the risk factors for ADR-related admissions within 90 days of post-index discharge. The patients with an ADR-related index admission were almost five times more likely to experience another ADR-related admission within 90 days (p < 0.001). An increased risk persisted for at least 5 years (p < 0.001), which was substantially longer than previously reported. From the matched subset of patients, the risk of ADR-related admission within 90 and 365 days more than doubled in the patients with an ADR-related index admission (p < 0.0001). These admissions were often attributed to the same drug class as the patients' index ADR-related admission. Cancer was a major risk factor for ADR-related re-hospitalization within 90 days; other factors included heart failure and increasing age.
| S-EPMC9101512 | biostudies-literature