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.

Reliable prediction of difficult airway for tracheal intubation from patient preoperative photographs by machine learning methods.

BACKGROUND: Estimating the risk of a difficult tracheal intubation should help clinicians in better anaesthesia planning, to maximize patient safety. Routine bedside screenings suffer from low sensitivity. OBJECTIVE: To develop and evaluate machine learning (ML) and deep learning (DL) algorithms for the reliable prediction of intubation risk, using information about airway morphology. METHODS: Observational, prospective cohort study enrolling n=623 patients who underwent tracheal intubation: 53/623 difficult cases (prevalence 8.51%). First, we used our previously validated deep convolutional neural network (DCNN) to extract 2D image coordinates for 27 + 13 relevant anatomical landmarks in two preoperative photos (frontal and lateral views). Here we propose a method to determine the 3D pose of the camera with respect to the patient and to obtain the 3D world coordinates of these landmarks. Then we compute a novel set of dM=59 morphological features (distances, areas, angles and ratios), engineered with our anaesthesiologists to characterize each individual's airway anatomy towards prediction. Subsequently, here we propose four ad hoc ML pipelines for difficult intubation prognosis, each with four stages: feature scaling, imputation, resampling for imbalanced learning, and binary classification (Logistic Regression, Support Vector Machines, Random Forests and eXtreme Gradient Boosting). These compound ML pipelines were fed with the dM=59 morphological features, alongside dD=7 demographic variables. Here we trained them with automatic hyperparameter tuning (Bayesian search) and probability calibration (Platt scaling). In addition, we developed an ad hoc multi-input DCNN to estimate the intubation risk directly from each pair of photographs, i.e. without any intermediate morphological description. Performance was evaluated using optimal Bayesian decision theory. It was compared against experts' judgement and against state-of-the-art methods (three clinical formulae, four ML, four DL models). RESULTS: Our four ad hoc ML pipelines with engineered morphological features achieved similar discrimination capabilities: median AUCs between 0.746 and 0.766. They significantly outperformed both expert judgement and all state-of-the-art methods (highest AUC at 0.716). Conversely, our multi-input DCNN yielded low performance due to overfitting. This same behaviour occurred for the state-of-the-art DL algorithms. Overall, the best method was our XGB pipeline, with the fewest false negatives at the optimal Bayesian decision threshold. CONCLUSIONS: We proposed and validated ML models to assist clinicians in anaesthesia planning, providing a reliable calibrated estimate of airway intubation risk, which outperformed expert assessments and state-of-the-art methods. Our novel set of engineered features succeeded in providing informative descriptions for prognosis.

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.

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.

Automated location of orofacial landmarks to characterize airway morphology in anaesthesia via deep convolutional neural networks.

BACKGROUND: A reliable anticipation of a difficult airway may notably enhance safety during anaesthesia. In current practice, clinicians use bedside screenings by manual measurements of patients' morphology. OBJECTIVE: To develop and evaluate algorithms for the automated extraction of orofacial landmarks, which characterize airway morphology. METHODS: We defined 27 frontal + 13 lateral landmarks. We collected n=317 pairs of pre-surgery photos from patients undergoing general anaesthesia (140 females, 177 males). As ground truth reference for supervised learning, landmarks were independently annotated by two anaesthesiologists. We trained two ad-hoc deep convolutional neural network architectures based on InceptionResNetV2 (IRNet) and MobileNetV2 (MNet), to predict simultaneously: (a) whether each landmark is visible or not (occluded, out of frame), (b) its 2D-coordinates (x,y). We implemented successive stages of transfer learning, combined with data augmentation. We added custom top layers on top of these networks, whose weights were fully tuned for our application. Performance in landmark extraction was evaluated by 10-fold cross-validation (CV) and compared against 5 state-of-the-art deformable models. RESULTS: With annotators' consensus as the 'gold standard', our IRNet-based network performed comparably to humans in the frontal view: median CV loss L=1.277·10-3, inter-quartile range (IQR) [1.001, 1.660]; versus median 1.360, IQR [1.172, 1.651], and median 1.352, IQR [1.172, 1.619], for each annotator against consensus, respectively. MNet yielded slightly worse results: median 1.471, IQR [1.139, 1.982]. In the lateral view, both networks attained performances statistically poorer than humans: median CV loss L=2.141·10-3, IQR [1.676, 2.915], and median 2.611, IQR [1.898, 3.535], respectively; versus median 1.507, IQR [1.188, 1.988], and median 1.442, IQR [1.147, 2.010] for both annotators. However, standardized effect sizes in CV loss were small: 0.0322 and 0.0235 (non-significant) for IRNet, 0.1431 and 0.1518 (p<0.05) for MNet; therefore quantitatively similar to humans. The best performing state-of-the-art model (a deformable regularized Supervised Descent Method, SDM) behaved comparably to our DCNNs in the frontal scenario, but notoriously worse in the lateral view. CONCLUSIONS: We successfully trained two DCNN models for the recognition of 27 + 13 orofacial landmarks pertaining to the airway. Using transfer learning and data augmentation, they were able to generalize without overfitting, reaching expert-like performances in CV. Our IRNet-based methodology achieved a satisfactory identification and location of landmarks: particularly in the frontal view, at the level of anaesthesiologists. In the lateral view, its performance decayed, although with a non-significant effect size. Independent authors had also reported lower lateral performances; as certain landmarks may not be clear salient points, even for a trained human eye.

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.

Reference values of EORTC QLQ-C30, EORTC QLQ-BR23, and EQ-5D-5L for women with non-metastatic breast cancer at diagnosis and 2 years after.

PURPOSE: To obtain reference norms of EORTC QLQ-C30, EORTC QLQ-BR23, and EQ-5D-5L, based on a population of Spanish non-metastatic breast cancer patients at diagnosis and 2 years after, according to relevant demographic and clinical characteristics. METHODS: Multicentric prospective cohort study including consecutive women aged ≥ 18 years with a diagnosis of incident non-metastatic breast cancer from April 2013 to May 2015. Health-related quality of life (HRQoL) questionnaires were administered between diagnosis and beginning the therapy, and 2 years after. HRQoL differences according to age, comorbidity and stage were tested with ANOVA or Chi Square test and multivariate linear regression models. RESULTS: 1276 patients were included, with a mean age of 58 years. Multivariate models of EORTC QLQ-C30 summary score and EQ-5D-5L index at diagnosis and at 2-year follow-up show the independent association of comorbidity and tumor stage with HRQoL. The standardized multivariate regression coefficient of EORTC QLQ-C30 summary score was lower (poorer HRQoL) for women with stage II and III than for those with stage 0 at diagnosis (- 0.11 and - 0.07, p < 0.05) and follow-up (- 0.15 and - 0.10, p < 0.01). The EQ-5D-5L index indicated poorer HRQoL for women with Charlson comorbidity index ≥ 2 than comorbidity 0 both at diagnosis (- 0.13, p < 0.001) and follow-up (- 0.18, p < 0.001). Therefore, we provided the reference norms at diagnosis and at the 2-year follow-up, stratified by age, comorbidity index, and tumor stage. CONCLUSION: These HRQoL reference norms can be useful to interpret the scores of women with non-metastatic breast cancer, comparing them with country-specific reference values for this population.

Quality Indicators and Outcomes in a Prospective Cohort of Colorectal Cancer Patients.

BACKGROUND: Some quality indicators of proper health care in patients with colorectal cancer have been established. AIMS: Our goal was to evaluate the relationship between performing of certain procedures or treatments, included as quality indicators, and some outcomes of indicators in the follow-up of colorectal cancer patients. METHODS: This was a prospective cohort study of patients diagnosed with colorectal cancer that underwent surgery and were followed at 1, 2, 3, and 5 years. CT scanning, colonoscopy, chemotherapy, and radiotherapy were evaluated in relation to various clinical outcomes and PROM changes over 5 years. Multivariable generalized linear mixed models were used to evaluate their effect on mortality, complications, recurrence, and PROM changes (HAD, EQ-5D, EORTC-Q30) at the next follow-up. RESULTS: CT scanning or colonoscopy was related to a decrease in the risk of dying, while chemotherapy at a specified moment was related to an increased risk. In the case of recurrence, CT scanning and chemotherapy showed statistically increased the risk, while all the procedures and treatments influenced complications. Regarding PROM scales, CT scanning, colonoscopy, and radiotherapy showed statistically significant results with respect to an increase in anxiety and decrease in quality of life measured by the EORTC. However, undergoing radiotherapy at a specified moment increased depression levels, and overall, receiving radiotherapy decreased the quality of life of the patients, as measured by the EuroQol-5d. CONCLUSIONS: After adjustment for sociodemographic factors, comorbidities, and severity of the disease, performing certain quality indicators of proper health care in patients with colorectal cancer was related to less mortality but higher adverse outcomes. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT02488161.

Laboratory protocol for the digital multiplexed gene expression analysis of nasopharyngeal swab samples using the NanoString nCounter system.

This paper describes a laboratory protocol to perform the NanoString nCounter Gene Expression Assay from nasopharyngeal swab samples.  It is urgently necessary to identify factors related to severe symptoms of respiratory infectious diseases, such as COVID-19, in order to assess the possibility of establishing preventive or preliminary therapeutic measures and to plan the services to be provided on hospital admission. At present, the samples recommended for microbiological diagnosis are those taken from the upper and/or the lower respiratory tract.  The NanoString nCounter Gene Expression Assay is a method based on the direct digital detection of mRNA molecules by means of target-specific, colour-coded probe pairs, without the need for mRNA conversion to cDNA by reverse transcription or the amplification of the resulting cDNA by PCR. This platform includes advanced analysis tools that reduce the need for bioinformatics support and also offers reliable sensitivity, reproducibility, technical robustness and utility for clinical application, even in RNA samples of low RNA quality or concentration, such as paraffin-embedded samples. Although the protocols for the analysis of blood or formalin-fixed paraffin-embedded samples are provided by the manufacturer, no corresponding protocol for the analysis of nasopharyngeal swab samples has yet been established. Therefore, the approach we describe could be adopted to determine the expression of target genes in samples obtained from nasopharyngeal swabs using the nCOUNTER technology.

Machine learning-based model for prediction of clinical deterioration in hospitalized patients by COVID 19.

Despite the publication of great number of tools to aid decisions in COVID-19 patients, there is a lack of good instruments to predict clinical deterioration. COVID19-Osakidetza is a prospective cohort study recruiting COVID-19 patients. We collected information from baseline to discharge on: sociodemographic characteristics, comorbidities and associated medications, vital signs, treatment received and lab test results. Outcome was need for intensive ventilatory support (with at least standard high-flow oxygen face mask with a reservoir bag for at least 6 h and need for more intensive therapy afterwards or Optiflow high-flow nasal cannula or noninvasive or invasive mechanical ventilation) and/or admission to a critical care unit and/or death during hospitalization. We developed a Catboost model summarizing the findings using Shapley Additive Explanations. Performance of the model was assessed using area under the receiver operating characteristic and prediction recall curves (AUROC and AUPRC respectively) and calibrated using the Hosmer-Lemeshow test. Overall, 1568 patients were included in the derivation cohort and 956 in the (external) validation cohort. The percentages of patients who reached the composite endpoint were 23.3% vs 20% respectively. The strongest predictors of clinical deterioration were arterial blood oxygen pressure, followed by age, levels of several markers of inflammation (procalcitonin, LDH, CRP) and alterations in blood count and coagulation. Some medications, namely, ATC AO2 (antiacids) and N05 (neuroleptics) were also among the group of main predictors, together with C03 (diuretics). In the validation set, the CatBoost AUROC was 0.79, AUPRC 0.21 and Hosmer-Lemeshow test statistic 0.36. We present a machine learning-based prediction model with excellent performance properties to implement in EHRs. Our main goal was to predict progression to a score of 5 or higher on the WHO Clinical Progression Scale before patients required mechanical ventilation. Future steps are to externally validate the model in other settings and in a cohort from a different period and to apply the algorithm in clinical practice.Registration: ClinicalTrials.gov Identifier: NCT04463706.

Influence of the COPD Assessment Test respiratory item score on the decision to hospitalize patients with disease exacerbation in a hospital emergency department. / Influencia de los ítems respiratorios del COPD Assessment Test (CAT) en la decisión de ingreso de las agudizaciones de enfermedad pulmonar obstructiva crónica (EPOC) atendidos en urgencias hospitalarias.

OBJECTIVES: The COPD Assessment Test (CAT) measures quality of life in patients with chronic obstructive pulmonary disease (COPD) as well as disease impact on activities of daily living. The questionnaire consists of 8 items related to breathing (cough, phlegm, chest tightness, and breathlessness) and other symptoms (low energy level, sleep disturbances, limitations on daily activities, and confidence when leaving the home). We investigated the relative impact of respiratory versus nonrespiratory scoring on the total CAT score at different moments in the course of COPD exacerbations: baseline (24 hours before an exacerbation), during the exacerbation, 15 days later, and 2 months later. To assess the influence of the respiratory item score on decisions to hospitalize patients treated for exacerbated COPD in our hospital emergency department (ED). MATERIAL AND METHODS: Prospective cohort study. We recruited patients who came to our ED for symptoms consistent with exacerbated COPD. Sociodemographic and clinical data were recorded. Clinical information, including treatments pleustarted in the ED and CAT scores, were also recorded. The event was defined as highly symptomatic if the patient's score was 3 points or higher on at least 3 of the 4 respiratory items at baseline. The outcome measures for the first objective were the total CAT score and item scores at the 4 time points before (baseline), during (ED), and after the exacerbation. The outcome for the second objective was hospital admission. RESULTS: A total of 587 patients were included. The mean (SD) total CAT score was 13.48 (7.29) at baseline, 24.86 (7.25) in the ED, 14.7 (7.47) at 15 days, and 13.45 (7.36) at 2 months. The respiratory item scores accounted for a mean 53.4% (20.76%) of the total score at baseline and 48.2% (11.47%) of the total score in the ED. Eighty-two patients (14.0%) were classified as being highly symptomatic. A total of 359 (61.2%) were admitted. Predictors of hospital admission were classification as highly symptomatic, odds ratio (OR, 3.045; 95% CI, 1.585-5.852, P .001), dyspnea at rest (OR, 2.906; 95% CI:1.943-4.346, P .001), and start of the following treatments in the ED: oxygen therapy (OR, 4.550; 95% CI, 3.056-6.773; P .0001), diuretic (OR, 1.754; 95% CI, 1.091-2.819; P = .02), and intravenous antibiotics (OR, 1.536; 95% CI, 1.034-2.281; P = .03). The model achieved an area under the receiver operating characteristic curve of 0.80 (95% CI, 0.763-0.836). CONCLUSION: Hospital admission from the ED is highly likely in patients with COPD exacerbation who have high baseline CAT scores, dyspnea at rest in the ED, and require oxygen therapy, diuretics, or intravenous antibiotics in the ED. The total CAT score and scores on respiratory items provide a tool for tailoring pharmacalogic and nonpharmacologic treaments and can facilitate follow-up evaluations.

OBJETIVO: El CAT (COPD Assessment Test) es un cuestionario de calidad de vida que mide el impacto que la enfermedad pulmonar obstructiva crónica (EPOC) está teniendo en el bienestar y vida diaria de los pacientes. Consta de 8 ítems divididos en 4 respiratorios y 4 no respiratorios. Conocer el impacto de las puntuaciones de los ítems respiratorios y no respiratorios en la puntuación CAT total, en diferentes momentos de la exacerbación de EPOC (24 horas antes de la exacerbación o basal, en la exacerbación, a los 15 días y a los 2 meses). Secundariamente, se valoró la influencia de los ítems respiratorios de la puntuación CAT total, en la decisión de ingreso de los pacientes atendidos por exacerbación de EPOC (EA-EPOC) en un servicio de urgencias hospitalario (SUH). METODO: Estudio de cohortes prospectivo. Se reclutaron pacientes que acudían al SUH con síntomas compatibles con EA-EPOC. La variable "Paciente respiratorio altamente sintomático"(PRAS) se definió como el paciente que tiene 3 puntos o más en al menos 3 de los 4 ítems respiratorios del CAT basal. Las variables de resultado fueron para el primer objetivo: la puntuación CAT total y desglosada por ítems, en los 4 momentos estudiados. Para el segundo objetivo fue el ingreso hospitalario. RESULTADOS: Se incluyeron 587 pacientes. La media de la puntuación CAT total basal fue 13,48 (7,29), en urgencias fue 24,86 (7,25), a los 15 días fue 14,7 (7,47) y a los 2 meses 13,45 fue (7,36). La proporción sobre la puntuación CAT basal total de los ítems respiratorios fue de 53,4% (20,76) y en el momento de llegar a urgencias del 48,2% (11,47). Los PRAS fueron 82 (14,0%). Ingresaron 359 pacientes (61,2%). Los predictores de ingreso hospitalario fueron: PRAS (OR 3,045, IC 95%: 1,585-5,852, p 0,001), disnea de reposo (OR 2,906, IC 95%: 1,943-4,346, p 0,001) y algunos tratamientos instaurados en el SUH (oxigenoterapia: OR 4,550, IC 95%: 3,056-6,773, p 0,001; diurético: OR 1,754, IC 95%: 1,091-2,819, p = 0,02; y antibiótico iv: OR 1,536, IC 95%: 1,034-2,281, p = 0,03). Este modelo logra un área bajo la curva COR de 0,80 (IC 95%: 0,763-0,836). CONCLUSIONES: En pacientes con EA-EPOC atendidos en urgencias, la alta puntuación de ítems respiratorios en el CAT basal, la disnea de reposo a su llegada al SUH y varios de los tratamientos instaurados en urgencias (oxigenoterapia, diuréticos y antibioterapia intravenosa) demostraron tener buena capacidad de predicción de ingreso hospitalario. La puntuación CAT total así como la puntuación en los ítems respiratorios del mismo son una herramienta que podría ayudar al clínico a individualizar el tratamiento o los controles posteriores.

Effect of comorbidities on long-term outcomes of colorectal cancer patients.

OBJECTIVE: The objective of this work is to evaluate the association of comorbidities with various outcomes in patients diagnosed with colon or rectal cancer. METHODS: We conducted a prospective cohort study of patients diagnosed with colon or rectal cancer who underwent surgery. Data were gathered on sociodemographic, clinical characteristics, disease course, and the EuroQol EQ-5D and EORTC QLQ-C30 scores, up to 5 years after surgery. The main outcomes of the study were mortality, complications, readmissions, reoperations, and changes in PROMs up to 5 years. Multivariable multilevel logistic regression models were used in the analyses. RESULTS: Mortality at some point during the 5-year follow-up was related to cardiocerebrovascular, hemiplegia and/or stroke, chronic obstructive pulmonary disease (COPD), diabetes, cancer, and dementia. Similarly, complications were related to cardiovascular disease, COPD, diabetes, hepatitis, hepatic or renal pathologies, and dementia; readmissions to cardiovascular disease, COPD, and hepatic pathologies; and reoperations to cerebrovascular and diabetes. Finally, changes in EQ-5D scores at some point during follow-up were related to cardiocerebrovascular disease, COPD, diabetes, pre-existing cancer, hepatic and gastrointestinal pathologies, and changes in EORTC QLQ-C30 scores to cardiovascular disease, COPD, diabetes, and hepatic and gastrointestinal pathologies. CONCLUSIONS: Optimising the management of the comorbidities most strongly related to adverse outcomes may help to reduce those events in these patients.

Modelling the risk of hospital admission of lab confirmed SARS-CoV-2-infected patients in primary care: a population-based study.

The objectives of this study are to develop a predictive model of hospital admission for COVID-19 to help in the activation of emergency services, early referrals from primary care, and the improvement of clinical decision-making in emergency room services. The method is the retrospective cohort study of 49,750 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 data, baseline comorbidities, baseline treatments, and other background data. Multilevel analyses with generalized estimated equations were used to develop the predictive model. Male sex and the gradual effect of age were the main risk factors for hospital admission. Regarding baseline comorbidities, coagulopathies, cancer, cardiovascular diseases, diabetes with organ damage, and liver disease were among the five most notable. Flu vaccination was a risk factor for hospital admission. Drugs that increased risk were chronic systemic steroids, immunosuppressants, angiotensin-converting enzyme inhibitors, and NSAIDs. The AUC of the risk score was 0.821 and 0.828 in the derivation and validation samples, respectively. Based on the risk score, five risk groups were derived with hospital admission ranging from 2.94 to 51.87%. In conclusion, we propose a classification system for people with COVID-19 with a higher risk of hospitalization, and indirectly with it a greater severity of the disease, easy to be completed both in primary care, as well as in emergency services and in hospital emergency room to help in clinical decision-making.Registration: ClinicalTrials.gov Identifier: NCT04463706.

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.

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.