Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 2 de 2
Filter
Add filters

Database
Language
Document Type
Year range
1.
Healthcare (Basel) ; 11(9)2023 Apr 30.
Article in English | MEDLINE | ID: covidwho-2312784

ABSTRACT

INTRODUCTION: The COVID-19 pandemic brought a burden and represented a challenge for the Romanian medical system. This study explored the consequences that COVID epidemiological measures had on the quality of the mental health care provided to hospitalized patients in a regional psychiatric hospital in Romania. MATERIALS AND METHODS: Both patient-level and hospital-level indicators were considered for this comparative retrospective study. On the one hand, we extracted patient-level indicators, such as sociodemographics, diagnosis, admission, and discharge dates for 7026 hospitalized patients (3701 women, average age = 55.14) from hospital records. On the other hand, for the hospital-level indicators, we included indicators referring to the aggregated concept of mental health services, such as case mix index, length of stay, bed occupancy rate and patients' degree of satisfaction. Data extracted covered a period of two years (1 March 2019-28 February 2021) before and during the first year of the COVID-19 pandemic. RESULTS: We found that, compared to the pre-pandemic period, the pandemic period was marked by a drastic decrease in hospitalized patient admissions, coupled with an increase in emergency-based admissions. Other management indicators, such as the case mix index, the number of cases contracted/performed, and the degree of patient satisfaction, decreased. In contrast, the average length of stay and bed occupancy rate increased. CONCLUSIONS: The COVID-19 pandemic, especially in the first year, raised multiple difficult issues for the management of psychiatric hospitals. It imposed an application of strict measures designed to face these new and unprecedented challenges. Our findings offer a detailed snapshot of the first year of the COVID-19 pandemic in terms of its impact on mental health services and suggest some future directions. Implications for hospital management are discussed.

2.
Int J Environ Res Public Health ; 18(9)2021 04 30.
Article in English | MEDLINE | ID: covidwho-1231474

ABSTRACT

Neonatal brain injury or neonatal encephalopathy (NE) is a significant morbidity and mortality factor in preterm and full-term newborns. NE has an incidence in the range of 2.5 to 3.5 per 1000 live births carrying a considerable burden for neurological outcomes such as epilepsy, cerebral palsy, cognitive impairments, and hydrocephaly. Many scoring systems based on different risk factor combinations in regression models have been proposed to predict abnormal outcomes. Birthweight, gestational age, Apgar scores, pH, ultrasound and MRI biomarkers, seizures onset, EEG pattern, and seizure duration were the most referred predictors in the literature. Our study proposes a decision-tree approach based on clinical risk factors for abnormal outcomes in newborns with the neurological syndrome to assist in neonatal encephalopathy prognosis as a complementary tool to the acknowledged scoring systems. We retrospectively studied 188 newborns with associated encephalopathy and seizures in the perinatal period. Etiology and abnormal outcomes were assessed through correlations with the risk factors. We computed mean, median, odds ratios values for birth weight, gestational age, 1-min Apgar Score, 5-min Apgar score, seizures onset, and seizures duration monitoring, applying standard statistical methods first. Subsequently, CART (classification and regression trees) and cluster analysis were employed, further adjusting the medians. Out of 188 cases, 84 were associated to abnormal outcomes. The hierarchy on etiology frequencies was dominated by cerebrovascular impairments, metabolic anomalies, and infections. Both preterms and full-terms at risk were bundled in specific categories defined as high-risk 75-100%, intermediate risk 52.9%, and low risk 0-25% after CART algorithm implementation. Cluster analysis illustrated the median values, profiling at a glance the preterm model in high-risk groups and a full-term model in the inter-mediate-risk category. Our study illustrates that, in addition to standard statistics methodologies, decision-tree approaches could provide a first-step tool for the prognosis of the abnormal outcome in newborns with encephalopathy.


Subject(s)
Brain Injuries , Epilepsy , Apgar Score , Electroencephalography , Female , Humans , Infant , Infant, Newborn , Pregnancy , Retrospective Studies , Seizures/epidemiology
SELECTION OF CITATIONS
SEARCH DETAIL