Caring for patients in the Covid era: Are the quality of life the same for doctors and nursing staff?

BACKGROUND: The Covid-19 pandemic has represented one of the most stressful events of recent times and has placed enormous psychological pressure on doctors and nurses. AIMS: The objective of this work is to evaluate the psychological impact of the Covid-19 outbreak on Spanish nurses and doctors, and to identify factors related to their mental health. METHODS: The study is a descriptive study and examined 812 doctors and 768 nurses. The dependent variables were health-related quality of life, anxiety, depression, perceived stress and insomnia. Participants completed the Health-related Quality of Life-Questionnaire, the Generalized Anxiety Disorder 7-item-Scale, the Patient Health Questionnaire-9, the Impact Event Scale-Revised, and the Insomnia Severity Index. Sociodemographic and Covid-related data were also recorded. Descriptive statistics, univariable analysis and multivariable linear regression models were used. RESULTS: A greater proportion of nurses than doctors suffered clinical anxiety, depression and insomnia (56.84 % vs 45.81 p-value<0.0001, 64.67 % vs 53.39 p-value<0.0001, and 23.04 % vs 18.02 p-value 0.01, respectively). Although in our study nurses were more likely to suffer clinical anxiety, stress and insomnia than doctors, our results nevertheless showed that there were no differences in terms of quality of life. Different factors related to mental health were identified for doctors and nurses. Nurses working in care homes or geriatric services (OR = 4.13, IC95% 1.71-9.99, p-value 0.002), and in services with greatest contact with Covid-19 patients (OR = 1.71,IC95% 1.10-2.68, p-value 0.02) were more likely to suffer depression. CONCLUSIONS: Our study confirms that doctors and nurses are at high risk of clinical anxiety, depression, stress or insomnia during the Covid-19 pandemic.

Prediction of mortality in acute pulmonary embolism in cancer-associated thrombosis (MAUPE-C): derivation and validation of a multivariable model.

Optimal risk stratification of patients with cancer and pulmonary embolism (PE) remains unclear. We constructed a clinical prediction rule (CPR) named 'MAUPE-C' to identify patients with low 30 days mortality. The study retrospectively developed and internally validated a CPR for 30 days mortality in a cohort of patients with cancer and PE (both suspected and unsuspected). Candidate variables were chosen based on the EPIPHANY study, which categorized patients into 3 groups based on symptoms, signs, suspicion and patient setting at PE diagnosis. The performance of 'MAUPE-C' was compared to RIETE and sPESI scores. Univariate analysis confirmed that the presence of symptoms, signs, suspicion and inpatient diagnosis were associated with 30 days mortality. Multivariable logistic regression analysis led to the exclusion of symptoms as predictive variable. 'MAUPE-C' was developed by assigning weights to risk factors related to the ß coefficient, yielding a score range of 0 to 4.5. After receiver operating characteristic (ROC) curve analysis, a cutoff point was established at ≤ 1. Prognostic accuracy was good with an area under the curve (AUC) of 0.77 (95% CI 0.71-0.82), outperforming RIETE and sPESI scores in this cohort (AUC of 0.64 [95% CI 0.57-0.71] and 0.57 [95% CI 0.49-0.65], respectively). Forty-five per cent of patients were classified as low risk and experienced a 2.79% 30 days mortality. MAUPE-C has good prognostic accuracy in identifying patients at low risk of 30 days mortality. This CPR could help physicians select patients for early discharge.

Nonadverse COVID-19 evolution predictors: the CoNAE scale. / Predictores de evolución no adversa en pacientes con COVID-19: escala CoNAE (COVID-19 non-adverse evolution).

OBJECTIVES: Tools to identify patients with mild to moderate COVID-19 are as yet unavailable. Our aims were to identify factors associated with nonadverse outcomes and develop a scale to predict nonadverse evolution in patients with COVID-19 (the CoNAE scale) in hospital emergency departments. MATERIAL AND METHODS: Retrospective cohort study of patients who came to one of our area's national health service hospitals for treatment of SARS-CoV-2 infection from July 1, 2020, to July 31, 2021. From case records we collected sociodemographic information, underlying comorbidity and ongoing treatments, other relevant medical history details, and vital constants on arrival for triage. Multilevel multivariable logistic regression models were used to identify predictors. RESULTS: The model showed that patients who had nonadverse outcomes were younger, female, and vaccinated against COVID-19 (2 doses at the time of the study). They arrived with normal vital signs (heart rate, diastolic and systolic pressures, temperature, and oxygen saturation) and had none of the following concomitant diseases or factors: heart failure other heart disease, hypertension, diabetes, liver disease, dementia, history of malignant tumors, and they were not being treated with oral or other systemic corticosteroids or immunosuppressant therapy. The area under the receiver operating characteristic curve for the model was 0.840 (95% CI, 0.834-0.847). CONCLUSION: We developed the CoNAE scale to predict nonadverse outcomes. This scale may be useful in triage for evaluating patients with COVID-19. It may also help predict safe discharge or plan the level of care that patients require not only in a hospital emergency department but also in urgent primary care settings or out-of-hospital emergency care.

OBJETIVO: Faltan herramientas para identificar a los pacientes con COVID-19 moderado o leve. El objetivo de este estudio fue identificar variables asociadas a la evolución no adversa y diseñar un modelo predictivo de evolución favorable en pacientes atendidos en servicios de urgencias hospitalarios (SUH) por infección por SARS-CoV-2. METODO: Estudio de cohorte retrospectivo de pacientes con infección por SARS-CoV-2 que acudieron a alguno de los SUH de hospitales públicos de una área por una infección por COVID-19 entre el 1 de julio de 2020 y el 31 de julio de 2021. Los datos recogidos para este estudio incluyeron información sociodemográfica, comorbilidades basales y tratamientos, otros datos de antecedentes y registro de los signos vitales a la llegada (triaje) al SUH. Se utilizaron modelos de regresión logística multivariable multinivel para desarrollar los modelos predictivos. RESULTADOS: Las personas que tuvieron resultados no adversos eran más jóvenes, mujeres, habían recibido dos dosis de la vacuna COVID-19 en el momento del estudio, tenían signos vitales (frecuencia cardiaca-presión diastólica/sistólica, temperatura y saturación de oxígeno) dentro de un rango normal al llegar al triaje del SUH, y no tenían ninguna de las siguientes comorbilidades: insuficiencia cardiaca, enfermedad coronaria, hipertensión arterial, diabetes, enfermedad hepática, demencia, antecedentes de tumores malignos o prescripción de corticosteroides orales sistémicos o inmunosupresores como medicación basal. El modelo tenía un área bajo la curva (ABC) de 0,8404 (IC 95%: 0,8342-0,8466). CONCLUSIONES: Se ha desarrollado una escala de predicción de resultados no adversos que pueden ser útil como herramienta de triaje, así como para determinar el alta segura y para adaptar el nivel de atención que el paciente requiere, no sólo en el SUH, sino también a nivel de atención de emergencia primaria o extrahospitalaria.

Predictores de evolución no adversa en pacientes con COVID-19: escala CoNAE (COVID-19 non-adverse evolution) / Nonadverse COVID-19 evolution predictors: the CoNAE scale

Objetivos: Faltan herramientas para identificar a los pacientes con COVID-19 moderado o leve. El objetivo de este estudio fue identificar variables asociadas a la evolución no adversa y diseñar un modelo predictivo de evolución favorable en pacientes atendidos en servicios de urgencias hospitalarios (SUH) por infección por SARS-CoV-2. Métodos: Estudio de cohorte retrospectivo de pacientes con infección por SARS-CoV-2 que acudieron a alguno de los SUH de hospitales públicos de unaa área por una infección por COVID-19 entre el 1 de julio de 2020 y el 31 de julio de 2021. Los datos recogidos para este estudio incluyeron información sociodemográfica, comorbilidades basales y tratamientos, otros datos de antecedentes y registro de los signos vitales a la llegada (triaje) al SUH. Se utilizaron modelos de regresión logística multivariable multinivel para desarrollar los modelos predictivos. Resultados: Las personas que tuvieron resultados no adversos eran más jóvenes, mujeres, habían recibido dos dosis de la vacuna COVID-19 en el momento del estudio, tenían signos vitales (frecuencia cardiaca-presión diastólica/sistólica, temperatura y saturación de oxígeno) dentro de un rango normal al llegar al triaje del SUH, y no tenían ninguna de las siguientes comorbilidades: insuficiencia cardiaca, enfermedad coronaria, hipertensión arterial, diabetes, enfermedad hepática, demencia, antecedentes de tumores malignos o prescripción de corticosteroides orales sistémicos o inmunosupresores como medicación basal. El modelo tenía un área bajo la curva (ABC) de 0,8404 (IC 95%: 0,8342-0,8466). Conclusiones: Se ha desarrollado una escala de predicción de resultados no adversos que pueden ser útil como herramienta de triaje, así como para determinar el alta segura y para adaptar el nivel de atención que el paciente requiere, no sólo en el SUH, sino también a nivel de atención de emergencia primaria o extrahospitalaria. (AU)

Background and objectives: Tools to identify patients with mild to moderate COVID-19 are as yet unavailable. Our aims were to identify factors associated with nonadverse outcomes and develop a scale to predict nonadverse evolution in patients with COVID-19 (the CoNAE scale) in hospital emergency departments. Methods: Retrospective cohort study of patients who came to one of our area’s national health service hospitals for treatment of SARS-CoV-2 infection from July 1, 2020, to July 31, 2021. From case records we collected sociodemographicinformation, underlying comorbidity and ongoing treatments, other relevant medical history details, and vital constants on arrival for triage. Multilevel multivariable logistic regression models were used to identify predictors. Results: The model showed that patients who had nonadverse outcomes were younger, female, and vaccinated against COVID-19 (2 doses at the time of the study). They arrived with normal vital signs (heart rate, diastolic and systolic pressures, temperature, and oxygen saturation) and had none of the following concomitant diseases or factors: heart failure other heart disease, hypertension, diabetes, liver disease, dementia, history of malignant tumors, and they were not being treated with oral or other systemic corticosteroids or immunosuppressant therapy. The area under the receiver operating characteristic curve for the model was 0.840 (95% CI, 0.834-0.847). Conclusions: We developed the CoNAE scale to predict nonadverse outcomes. This scale may be useful in triage for evaluating patients with COVID-19. It may also help predict safe discharge or plan the level of care that patients require not only in a hospital emergency department but also in urgent primary care settings or out-of-hospital emergency care. (AU)

Impact of prior SARS-COV-2 infection and vaccination on COVID-19 hospital admission and mortality amongst nursing home residents.

BACKGROUND: Nursing home residents (NHRs) have experienced disproportionately high risk of severe outcomes due to COVID-19 infection. AIM: We investigated the impact of COVID-19 vaccinations and previous SARS-CoV-2 episodes in preventing hospitalization and mortality in NHRs. METHODS: Retrospective study of a cohort of all NHRs in our area who were alive at the start of the vaccination campaign. The first three doses of SARS-CoV-2 vaccine and prior COVID-19 infections were registered. The main outcomes were hospital admission and mortality during each follow up. Random effects time-varying Cox models adjusted for age, sex, and comorbidities were fitted to estimate hazard ratios (HRs) according to vaccination status. RESULTS: COVID-19 hospitalization and death rates for unvaccinated NHRs were respectively 2.39 and 1.42 per 10,000 person-days, falling after administration of the second dose (0.37 and 0.34) and rising with the third dose (1.08 and 0.8). Rates were much lower amongst people who had previously had COVID-19. Adjusted HRs indicated a significant decrease in hospital admission amongst those with a two- and three-dose status; those who had had a previous COVID-19 infection had even lower hospital admission rates. Death rates decreased as NHRs received two and three doses, and the probability of death was much lower among those who had previously had the infection. CONCLUSIONS: The effectiveness of current vaccines against severe COVID-19 disease in NHRs remains high and SARS-CoV-2 episodes prior to vaccination entail a major reduction in hospitalization and mortality rates. The protection conferred by vaccines appears to decline in the following months. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04463706.

[Influence of the social deprivation index on outcomes during the COVID-19 pandemic]. / Influencia del índice de privación social en resultados durante la pandemia de COVID-19.

OBJECTIVE: To see the relationship between the population deprivation index and the use of the health services, adverse evolution and mortality during the COVID-19 pandemic. METHOD: Retrospective cohort study of patients with SARS-CoV-2 infection from March 1, 2020 to January 9, 2022. The data collected included sociodemographic data, comorbidities and prescribed baseline treatments, other baseline data and the deprivation index, estimated by census section. Multivariable multilevel logistic regression models were performed for each outcome variable: death, poor outcome (defined as death or intensive care unit), hospital admission, and emergency room visits. RESULTS: The cohort consists of 371,237 people with SARS-CoV-2 infection. In the multivariable models, a higher risk of death or poor evolution or hospital admission or emergency room visit was observed within the quintiles with the greatest deprivation compared to the quintile with the least. For the risk of being hospitalized or going to the emergency room, there were differences between most quintiles. It has also been observed that these differences occurred in the first and third periods of the pandemic for mortality and poor outcome, and in all due for the risk of being admitted or going to the emergency room. CONCLUSIONS: The groups with the highest level of deprivation have had worse outcomes compared to the groups with lower deprivation rates. It is necessary to carry out interventions that minimize these inequalities.

Clinical prediction rules for adverse evolution in patients with COVID-19 by the Omicron variant.

OBJECTIVE: We identify factors related to SARS-CoV-2 infection linked to hospitalization, ICU admission, and mortality and develop clinical prediction rules. METHODS: Retrospective cohort study of 380,081 patients with SARS-CoV-2 infection from March 1, 2020 to January 9, 2022, including a subsample of 46,402 patients who attended Emergency Departments (EDs) having data on vital signs. For derivation and external validation of the prediction rule, two different periods were considered: before and after emergence of the Omicron variant, respectively. Data collected included sociodemographic data, COVID-19 vaccination status, baseline comorbidities and treatments, other background data and vital signs at triage at EDs. The predictive models for the EDs and the whole samples were developed using multivariate logistic regression models using Lasso penalization. RESULTS: In the multivariable models, common predictive factors of death among EDs patients were greater age; being male; having no vaccination, dementia; heart failure; liver and kidney disease; hemiplegia or paraplegia; coagulopathy; interstitial pulmonary disease; malignant tumors; use chronic systemic use of steroids, higher temperature, low O2 saturation and altered blood pressure-heart rate. The predictors of an adverse evolution were the same, with the exception of liver disease and the inclusion of cystic fibrosis. Similar predictors were found to be related to hospital admission, including liver disease, arterial hypertension, and basal prescription of immunosuppressants. Similarly, models for the whole sample, without vital signs, are presented. CONCLUSIONS: We propose risk scales, based on basic information, easily-calculable, high-predictive that also function with the current Omicron variant and may help manage such patients in primary, emergency, and hospital care.

Factors related to early readmissions after acute heart failure: a nested case-control study.

AIMS: To describe the main characteristics of patients who were readmitted to hospital within 1 month after an index episode for acute decompensated heart failure (ADHF). METHODS AND RESULTS: This is a nested case-control study in the ReIC cohort, cases being consecutive patients readmitted after hospitalization for an episode of ADHF and matched controls selected from those who were not readmitted. We collected clinical data and also patient-reported outcome measures, including dyspnea, Minnesota Living with Heart Failure Questionnaire (MLHFQ), Tilburg Frailty Indicator (TFI) and Hospital Anxiety and Depression Scale scores, as well as symptoms during a transition period of 1 month after discharge. We created a multivariable conditional logistic regression model. Despite cases consulted more than controls, there were no statistically significant differences in changes in treatment during this first month. Patients with chronic decompensated heart failure were 2.25 [1.25, 4.05] more likely to be readmitted than de novo patients. Previous diagnosis of arrhythmia and time since diagnosis ≥ 3 years, worsening in dyspnea, and changes in MLWHF and TFI scores were significant in the final model. CONCLUSION: We present a model with explanatory variables for readmission in the short term for ADHF. Our study shows that in addition to variables classically related to readmission, there are others related to the presence of residual congestion, quality of life and frailty that are determining factors for readmission for heart failure in the first month after discharge. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT03300791. First registration: 03/10/2017.

Influencia del índice de privación social en resultados durante la pandemia de COVID-19 / Influence of the social deprivation index on outcomes during the COVID-19 pandemic

Objetivo: Determinar la relación del índice de privación de la población con la utilización del sistema sanitario, la mala evolución y la mortalidad durante la pandemia de COVID-19. Método: Estudio de cohorte retrospectivo de personas con infección por SARS-CoV-2 del 1 de marzo de 2020 al 9 de enero de 2022. Se recopilaron datos sociodemográficos, comorbilidad y tratamientos basales prescritos, otros datos basales y el índice de privación, estimado por sección censal. Se realizaron modelos multivariable de regresión logística multinivel para cada variable de resultado: fallecimiento, mala evolución (definida como fallecimiento o ingreso en la unidad de cuidados intensivos), ingreso y visitas a urgencias. Resultados: La cohorte se compone de 371.237 personas con infección por SARS-CoV-2. En los modelos multivariable se observó un mayor riesgo de fallecimiento, de mala evolución, de ingreso hospitalario o de visita a urgencias en los quintiles de mayor privación en comparación con el quintil de menor privación. Para el riesgo de ser hospitalizado o de acudir a urgencias, en términos generales hubo diferencias entre todos los quintiles. También se observó que estas diferencias se daban en el primer y el tercer periodos de la pandemia para la mortalidad y la mala evolución, y en todos para el riesgo de ser ingresado o de acudir a urgencias. Conclusiones: Los colectivos con mayor nivel de privación han tenido mayores tasas de mortalidad y de ingreso en comparación con los colectivos con unas tasas de privación más bajas. Es necesario realizar intervenciones que minimicen estas desigualdades. (AU)

Objective: To see the relationship between the population deprivation index and the use of the health services, adverse evolution and mortality during the COVID-19 pandemic. Method: Retrospective cohort study of patients with SARS-CoV-2 infection from March 1, 2020 to January 9, 2022. The data collected included sociodemographic data, comorbidities and prescribed baseline treatments, other baseline data and the deprivation index, estimated by census section. Multivariable multilevel logistic regression models were performed for each outcome variable: death, poor outcome (defined as death or intensive care unit), hospital admission, and emergency room visits. Results: The cohort consists of 371,237 people with SARS-CoV-2 infection. In the multivariable models, a higher risk of death or poor evolution or hospital admission or emergency room visit was observed within the quintiles with the greatest deprivation compared to the quintile with the least. For the risk of being hospitalized or going to the emergency room, there were differences between most quintiles. It has also been observed that these differences occurred in the first and third periods of the pandemic for mortality and poor outcome, and in all due for the risk of being admitted or going to the emergency room. Conclusions: The groups with the highest level of deprivation have had worse outcomes compared to the groups with lower deprivation rates. It is necessary to carry out interventions that minimize these inequalities. (AU)

COPD in SARS-CoV-2 pandemic. baseline characteristics related to hospital admissions.

OBJECTIVE: To develop a predictive model for COPD patients admitted for COVID-19 to support clinical decision-making. METHOD: Retrospective cohort study of 1313 COPD patients with microbiological confirmation of SARS-CoV-2 infection. The sample was randomly divided into two subsamples, for the purposes of derivation and validation of the prediction rule (60% and 40%,respectively). Data collected for this study included sociodemographic characteristics, baseline comorbidities, baseline treatments, and other background data. Multivariable logistic regression analysis was used to develop the predictive model. RESULTS: Male sex, older age, hospital admissions in the previous year, flu vaccination in the previous season, a Charlson Index>3 and a prescription of renin-angiotensin aldosterone system inhibitors at baseline were the main risk factors for hospital admission. The AUC of the categorized risk score was 0.72 and 0.69 in the derivation and validation samples, respectively. Based on the risk score, four groups were identified with a risk of hospital admission ranging from 21% to 80%. CONCLUSIONS: We propose a classification system to identify COPD people with COVID-19 with a higher risk of hospitalization, and indirectly, more severe disease, that is easy to use in primary care, as well as hospital emergency room settings to help clinical decision-making. CLINICALTRIALS.GOV IDENTIFIER: NCT04463706.

Evaluation of the mental health of care home staff in the Covid-19 era. What price did care home workers pay for standing by their patients?

BACKGROUND: The characteristics of this pandemic increase the potential psychological impact on care homes workers (CHWs). The aims of this study were to analyse the mental health and health-related quality of life (HRQoL) of a broad sample of CHWs in Spain and to identify potential factors that have a significant effect on their mental health and HRQoL. METHOD: This descriptive study comprised 210 CHWs who completed the Generalized Anxiety Disorder 7-item Scale, the Patient Health Questionnaire-9, the Impact Event Scale-Revised, the Insomnia Severity Index, and the Health-related Quality of Life Questionnaire. Sociodemographic and clinical data in relation to COVID-19 were also recorded. Descriptive statistics, univariable analysis and multivariable linear regression models were applied to identify factors associated with mental health and HRQoL. RESULTS: Of total, 86.19% of participants were female; 86.67% were aged under 55 years; 11% were physicians and 64.19% were nurses or auxiliaries; 77.62% have themselves tested positive for Covid-19; and 67.94% of CHWs have directly treated patients with Covid-19. 49.28% had clinical depression; over half (58.57%) had clinical anxiety; 70.95% had clinical stress; and 28.57% had clinical insomnia. Increased use of tranquilizers/sedatives appears to be an explanatory variable of suffering greater anxiety, depression, stress and insomnia, and of having a worse HRQoL amongst our CHWs. CONCLUSIONS: Our study confirms that symptomatology of anxiety, depression, stress, insomnia and HRQoL were affected amongst CHWs during the Covid-19 pandemic.

Evaluation of the mental health of health professionals in the COVID-19 era. What mental health conditions are our health care workers facing in the new wave of coronavirus?

BACKGROUND: The aims of this study were to analyse the mental health and health-related quality of life (HRQoL) of a broad sample of healthcare workers (HCWs) in Spain and to identify potential factors that have a significant effect on their mental health and HRQoL. METHOD: This prospective cohort study comprised 2089 HCWs who completed the Generalized Anxiety Disorder 7-item (GAD-7) Scale, the Patient Health Questionnaire-9 (PHQ-9), the Impact Event Scale-Revised (IES-R), the Insomnia Severity Index (ISI), and the health-related quality of life Questionnaire (EQ-5D). Sociodemographic and clinical data in relation to Covid-19 were also recorded. Descriptive statistics, univariable analysis and multivariable linear regression models were applied to identify factors associated with mental health and HRQoL. RESULTS: 80.87% of participants were female and 19.13% male; 82.38% were aged under 55 years; 39.13% were physicians and 50.17% were nurses or auxiliaries; 80.42% of the health workers have directly treated patients with Covid-19 and 12.28% have themselves tested positive for Covid-19. 38.58% of HCWs had clinical depression; over half (51.75%) had clinical anxiety; 60.4% had clinical stress; and 21.57% had clinical insomnia. Older professionals (>55 years) reported lower rates of anxiety, depression, insomnia, and stress. Having worked directly with Covid-19 patients appears to be an explanatory variable of suffering greater anxiety, depression, stress and insomnia, and of having a worse HRQoL amongst our HCWs. The group of HCWs suffering the worst mental state were nursing home workers. CONCLUSIONS: Our study confirms that symptomatology of anxiety, depression, stress, insomnia, and HRQoL were affected amongst HCW during the Covid-19 pandemic.

Predictors of mortality of COVID-19 in the general population and nursing homes.

The factors that predispose an individual to a higher risk of death from COVID-19 are poorly understood. The goal of the study was to identify factors associated with risk of death among patients with COVID-19. This is a retrospective cohort study of people with laboratory-confirmed SARS-CoV-2 infection from February to May 22, 2020. Data retrieved for this study included patient sociodemographic data, baseline comorbidities, baseline treatments, other background data on care provided in hospital or primary care settings, and vital status. Main outcome was deaths until June 29, 2020. In the multivariable model based on nursing home residents, predictors of mortality were being male, older than 80 years, admitted to a hospital for COVID-19, and having cardiovascular disease, kidney disease or dementia while taking anticoagulants or lipid-lowering drugs at baseline was protective. The AUC was 0.754 for the risk score based on this model and 0.717 in the validation subsample. Predictors of death among people from the general population were being male and/or older than 60 years, having been hospitalized in the month before admission for COVID-19, being admitted to a hospital for COVID-19, having cardiovascular disease, dementia, respiratory disease, liver disease, diabetes with organ damage, or cancer while being on anticoagulants was protective. The AUC was 0.941 for this model's risk score and 0.938 in the validation subsample. Our risk scores could help physicians identify high-risk groups and establish preventive measures and better follow-up for patients at high risk of dying.ClinicalTrials.gov Identifier: NCT04463706.