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BACKGROUND: Following the initial acute phase of COVID-19, health care resource use has escalated among individuals with SARS-CoV-2 infection. OBJECTIVE: This study aimed to compare new diagnoses of long COVID and the demand for health services in the general population after the Omicron wave with those observed during the pre-Omicron waves, using similar assessment protocols for both periods and to analyze the influence of vaccination. METHODS: This matched retrospective case-control study included patients of both sexes diagnosed with acute SARS-CoV-2 infection using reverse transcription polymerase chain reaction or antigen tests in the hospital microbiology laboratory during the pandemic period regardless of whether the patients were hospitalized. We included patients of all ages from 2 health care departments that cover 604,000 subjects. The population was stratified into 2 groups, youths (<18 years) and adults (≥18 years). Patients were followed-up for 6 months after SARS-CoV-2 infection. Previous vaccination, new diagnoses, and the use of health care resources were recorded. Patients were compared with controls selected using a prospective score matched for age, sex, and the Charlson index. RESULTS: A total of 41,577 patients with a history of prior COVID-19 infection were included, alongside an equivalent number of controls. This cohort encompassed 33,249 (80%) adults aged ≥18 years and 8328 (20%) youths aged <18 years. Our analysis identified 40 new diagnoses during the observation period. The incidence rate per 100 patients over a 6-month period was 27.2 for vaccinated and 25.1 for unvaccinated adults (P=.09), while among youths, the corresponding rates were 25.7 for vaccinated and 36.7 for unvaccinated individuals (P<.001). Overall, the incidence of new diagnoses was notably higher in patients compared to matched controls. Additionally, vaccinated patients exhibited a reduced incidence of new diagnoses, particularly among women (P<.001) and younger patients (P<.001) irrespective of the number of vaccine doses administered and the duration since the last dose. Furthermore, an increase in the use of health care resources was observed in both adult and youth groups, albeit with lower figures noted in vaccinated individuals. In the comparative analysis between the pre-Omicron and Omicron waves, the incidence of new diagnoses was higher in the former; however, distinct patterns of diagnosis were evident. Specifically, depressed mood (P=.03), anosmia (P=.003), hair loss (P<.001), dyspnea (<0.001), chest pain (P=.04), dysmenorrhea (P<.001), myalgia (P=.011), weakness (P<.001), and tachycardia (P=.015) were more common in the pre-Omicron period. Similarly, health care resource use, encompassing primary care, specialist, and emergency services, was more pronounced in the pre-Omicron wave. CONCLUSIONS: The rise in new diagnoses following SARS-CoV-2 infection warrants attention due to its potential implications for health systems, which may necessitate the allocation of supplementary resources. The absence of vaccination protection presents a challenge to the health care system.

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Background and Objective: Unplanned hospital readmissions are a severe and recurrent problem that affects all health systems. Estimating the risk of being readmitted the following days after discharge is difficult since many heterogeneous factors can influence this. The extensive work concerning this problem proposes solutions mostly based on classification machine-learning models. Survival analysis methods could make a better match with the assessment of readmission risk and are yet to become well-established in this field. Methods: We compare different statistical and machine learning survival analysis models trained with right-censored all-cause hospital admission data with covariates available at the moment of discharge. The main focus is on tree-ensemble regression methods based on the assumption of proportional hazards. These models are more thoroughly evaluated at a 30-day time period after discharge, although the actual prediction could be set to any time up to 90 days. Results: The mean performance obtained by each of the proposed survival models ranges from 0.707 to 0.716 C-Index and 0.709 to 0.72 ROC-AUC at a 30-day time period after discharge. The model with the lower performance on both metrics was Cox Proportional Hazards, while the model marking the upper end on both ranges is an XGBoost Regression model with a Cox objective function. Conclusions: Our findings indicate that survival models perform well addressing the hospital readmission problem, machine-learning models getting the edge over statistical methods. There seems to be an improvement over classification models when attempting to predict at a 30-day period since discharge, perhaps due to a better handling of cases nearing the 30-day boundary. Some preprocessing steps, such as limiting the observation period to 90 days after discharge, are also highlighted since they resulted in a performance boost.

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Unplanned hospital readmissions mean a significant burden for health systems. Accurately estimating the patient's readmission risk could help to optimise the discharge decision-making process by smartly ordering patients based on a severity score, thus helping to improve the usage of clinical resources. A great number of heterogeneous factors can influence the readmission risk, which makes it highly difficult to be estimated by a human agent. However, this score could be achieved with the help of AI models, acting as aiding tools for decision support systems. In this paper, we propose a machine learning classification and risk stratification approach to assess the readmission problem and provide a decision support system based on estimated patient risk scores.

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Electronic health records and computational modelling have paved the way for the development of Type 2 Diabetes risk scores to identify subjects at high risk. Unfortunately, few risk scores have been externally validated, and their performance can be compromised when routine clinical data is used. The aim of this study was to assess the performance of well-established risk scores for Type 2 Diabetes using routinely collected clinical data and to quantify their impact on the decision making process of endocrinologists. We tested six risk models that have been validated in external cohorts, as opposed to model development, on electronic health records collected from 2008-2015 from a population of 10,730 subjects. Unavailable or missing data in electronic health records was imputed using an existing validated Bayesian Network. Risk scores were assessed on the basis of statistical performance to differentiate between subjects who developed diabetes and those who did not. Eight endocrinologists provided clinical recommendations based on the risk score output. Due to inaccuracies and discrepancies regarding the exact date of Type 2 Diabetes onset, 76 subjects from the initial population were eligible for the study. Risk scores were useful for identifying subjects who developed diabetes (Framingham risk score yielded a c-statistic of 85%), however, our findings suggest that electronic health records are not prepared to massively use this type of risk scores. Use of a Bayesian Network was key for completion of the risk estimation and did not affect the risk score calculation (p > 0.05). Risk score estimation did not have a significant effect on the clinical recommendation except for starting pharmacological treatment (p = 0.004) and dietary counselling (p = 0.039). Despite their potential use, electronic health records should be carefully analyzed before the massive use of Type 2 Diabetes risk scores for the identification of high-risk subjects, and subsequent targeting of preventive actions.

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In the past few years, healthcare systems have been facing a growing demand related to the high prevalence of chronic diseases. Case management programs have emerged as an integrated care approach for the management of chronic disease. Nevertheless, there is little scientific evidence on the impact of using a case management program for patients with complex multimorbidity regarding hospital resource utilisation. We evaluated an integrated case management intervention set up by community-based care at outpatient clinics with nurse case managers from a telemedicine unit. The hypothesis to be tested was whether improved continuity of care resulting from the integration of community-based and hospital services reduced the use of hospital resources amongst patients with complex multimorbidity. A retrospective cohort study was performed using a sample of 714 adult patients admitted to the program between January 2012 and January 2015. We found a significant decrease in the number of emergency room visits, unplanned hospitalizations, and length of stay, and an expected increase in the home care hospital-based episodes. These results support the hypothesis that case management interventions can reduce the use of unplanned hospital admissions when applied to patients with complex multimorbidity.

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Se presentan datos de prevalencia de disfagia en población atendida tras hospitalización aguda en seguimiento por una unidad de hospitalización a domicilio (UHD). Se investigan 440 pacientes admitidos de forma consecutiva en la UHD del Hospital la Fe, en los que se valora la presencia de disfagia previa y tras el episodio agudo y su relación con edad y comorbilidad. Se realiza intervención nutricional en domicilio y control clínico al mes. La prevalencia de disfagia durante el proceso agudo de hospitalización y permanencia en la UHD fue del 31,8% (intervalo de confianza [IC] del 95%, 27,6-36,4%), siendo ésta significativamente mayor que la previa al ingreso: 23,0% (IC del 95%, 19,5-26,5%); p 0,05). La mortalidad durante el primer mes fue del 12,0%. Las medidas de detección y tratamiento específico adoptadas no evitaron cifras elevadas de disfagia (8%) en el seguimiento. Se concluye que la disfagia es un problema frecuente en unidades que atienden a población con edad avanzada y comorbilidad de tipo neurológico, que aumenta con la hospitalización reciente y requiere una intervención específica tras el alta

We present data on the prevalence of dysphagia in a population followed-up by a hospital at home unit after acute hospitalization. A total of 440 consecutive patients were studied. The presence of dysphagia before and after the acute episode and its association with age and comorbidity were evaluated. The patients underwent a domiciliary nutritional intervention and clinical evaluation at 1 month was performed. The prevalence of dysphagia during the acute hospitalization phase and follow-up by the hospital at home unit was 31.8% (95% confidence interval [CI], 27.6-36.4%), which was significantly higher than that before admission: 23.0% (95% CI, 19.5-26.5%); p 0.05). Mortality during the first month was 12.0%. The detection methods and specific treatments adopted did not prevent dysphagia in a high percentage of patients (8%) during follow-up. In conclusion, dysphagia is a frequent problem in units attending the elderly population with neurological comorbidity. This disorder increases with recent hospitalization and requires specific interventions after discharge

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