UNASSIGNED: Medication history errors at hospital admission are common and effective strategies to improve the quality of medication histories are still being researched. However, studies on new approaches regarding medication history taking are often time-consuming and resource-intensive. The gold standard when evaluating the quality of medication histories is the comparison of a Best Possible Medication History to the original. However, this double collection requires significant resources, disrupts clinical procedures, and places an additional burden on patients. Therefore, more efficient study designs need to be explored. We aimed to develop a design for future studies on medication history taking that uses fewer research resources and places less strain on patients and staff.
UNASSIGNED: We first identified shortcomings of the established study designs on medication history taking and subsequently defined requirements for a new design. A pragmatic study with an alternative endpoint was identified in a previous literature search. It served as the starting point from which we developed a new study design to assess the quality of approaches to medication history taking. Instead of taking a second medication history, a patient\'s pre-existing medication document can be used as comparator to determine the quality of the medication history. Furthermore, we defined a new primary endpoint, i.e. the number of updates per patient. Updates are differences between the newly acquired medication history and the comparator. They include discontinued, initiated, and changed medications. To enhance our proposed design, we recommend a preparatory phase to identify a suitable comparator document, and a baseline phase to assess the current process.
UNASSIGNED: We propose a more resource-efficient study design with a new endpoint. We plan to test its feasibility and evaluate whether it could enhance the efficacy of research on medication history taking in a pilot project.

UNASSIGNED: This study measures the COVID-19 vaccine effectiveness (CVE) against hospital admission and severe COVID-19.
UNASSIGNED: This study is a test-negative case-control design using data from eight provinces in April, 2021 until March, 2022. The individuals were classified as cases and controls based on the results of the RT-PCR test for SARS-CoV-2 and matched based on the timing of the test being conducted as well as the timing of hospital admission. The measure of association was an odds ratio (OR) by univariate and multiple logistic regression. The multiple logistic regression has been carried out to take confounding factors and potential effect modifiers into account. The CVE was computed as CVE = (1 - OR)*100 with 95% confidence interval.
UNASSIGNED: Among 19314 admitted patients, of whom 13216 (68.4%) were cases and 6098 (31.6%) were controls, 1313 (6.8%) died. From total, 5959 (30.8%) patients had received the vaccine in which one, two, and booster doses were 2443 (12.6%), 2796 (14.5٪), and 720 (3.7٪), respectively. The estimated adjusted effectiveness of only one dose, two doses and booter vaccination were 22% (95% CI: 14%-29%), 35% (95% CI: 29%-41%) and 33% (95% CI: 16%-47%), respectively. In addition, the adjusted vaccine effectiveness against severe outcome was 33% (95% CI: 19%- 44%), 34% (95% CI: 20%- 45%) and 20% (95% CI: -29%- 50%) for those who received one, two and booster vaccinations, respectively.
UNASSIGNED: Our study concluded that full vaccination, though less effective compared to similar studies elsewhere, decreased hospital admissions and deaths from COVID-19 in Iran, particularly during the Delta variant period, with an observed decline during the Omicron variant dominance.

暂无摘要。

Complete medication reconciliation during hospital admission is the rationale for further treatment decisions. A consecutive, controlled intervention study was conducted to assess discrepancies in medication reconciliation performed by nurses of the Urology Department compared to the Best Possible Medication History (BPMH) established by pharmacists. This study included pre-intervention (control group, CG), nursing training as a pharmaceutical intervention, and post-intervention (intervention group, IG) groups. The discrepancies were classified as \"Missing\" (not recorded but taken), \"Added\" (additionally recorded) \"Strength\" (incorrect documented dosage), \"Intake\" (incorrect intake time/schedule), \"Double\" (double prescription), and \"Others\" (no clear assignment). Additionally, high-risk drug subgroup discrepancies were particularly prevalent and were evaluated. Training success was compared concerning discrepancies in the CG and IG. Generally, the percentage of discrepancies per patient found was lower in the IG than in the CG (78.1% vs. 87.5%, significantly). The category most identified was \"Missing\" (IG, 33.3% vs. CG, 35.2%). Overall, a discrepancy of 7.4% each (discrepancies: IG, 27 vs. CG, 38) was determined for high-risk drugs while \"Missing\" occurred (77.8% vs. 52.6%, out of 7.4%). Despite nursing training only partially reducing discrepancies, the implementation of medication reconciliation using BPMH by pharmacists could improve the process, especially for high-risk drugs.

UNASSIGNED: The COVID-19 pandemic and the ensuing implementation of control measures caused widespread societal disruption. These disruptions may also have affected community transmission and seasonal circulation patterns of endemic respiratory viruses.
UNASSIGNED: We aimed to investigate the impact of COVID-19-related disruption on influenza-related emergency hospital admissions and deaths in Wales in the first 2 years of the pandemic.
UNASSIGNED: A descriptive analysis of influenza activity was conducted using anonymized pathology, hospitalization, and mortality data from the Secure Anonymised Information Linkage Databank in Wales. The annual incidence of emergency hospitalizations and deaths with influenza-specific diagnosis codes between January 1, 2015, and December 31, 2021, was estimated. Case definitions of emergency hospitalization and death required laboratory confirmation with a polymerase chain reaction test. Trends of admissions and deaths were analyzed monthly and yearly. We conducted 2 sensitivity analyses by extending case definitions to include acute respiratory illnesses with a positive influenza test and by limiting admissions to those with influenza as the primary diagnosis. We also examined yearly influenza testing trends to understand changes in testing behavior during the pandemic.
UNASSIGNED: We studied a population of 3,235,883 Welsh residents in 2020 with a median age of 42.5 (IQR 22.9-61.0) years. Influenza testing in Wales increased notably in the last 2 months of 2020, and particularly in 2021 to 39,720 per 100,000 people, compared to the prepandemic levels (1343 in 2019). The percentage of influenza admissions matched to an influenza polymerase chain reaction test increased from 74.8% (1890/2526) in 2019 to 85.2% (98/115) in 2021. However, admissions with a positive test per 100,000 population decreased from 17.0 in 2019 to 2.7 and 0.6 in 2020 and 2021, respectively. Similarly, deaths due to influenza with a positive influenza test per 100,000 population decreased from 0.4 in 2019 to 0.0 in 2020 and 2021. Sensitivity analyses showed similar patterns of decreasing influenza admissions and deaths in the first 2 years of the COVID-19 pandemic.
UNASSIGNED: Nonpharmaceutical interventions to control COVID-19 were associated with a substantial reduction in the transmission of the influenza virus, with associated substantial reductions in hospital cases and deaths observed. Beyond the pandemic context, consideration should be given to the role of nonpharmaceutical community-driven interventions to reduce the burden of influenza.

UNASSIGNED: Methamphetamine is an emerging drug threat. The disparity in cardiomyopathy-associated hospital admissions among methamphetamine users (CAHMA) over the decade remains unknown.
UNASSIGNED: The purpose of this study was to determine the trends and prevalence of CAHMA by age, sex, race, and geographical region.
UNASSIGNED: We used data from 2008 to 2020 from the National Inpatient Sample database. We identified 12,845,919 cardiomyopathy-associated hospital admissions; among them, 222,727 were diagnosed as methamphetamine users. A generalized linear model with binomial link function was used to compute the prevalence ratio and 95% CI. Those who used other substances along with methamphetamine were excluded from the analysis.
UNASSIGNED: CAHMA increased by 231% (P trend <0.001) from 2008 to 2020. CAHMA increased 345% for men (P trend <0.001) and 122% for women (P trend <0.001), 271% for non-Hispanic White (P trend <0.001), 254% for non-Hispanic Black (p trend <0.001), 565% for Hispanic (P trend <0.001), and 645% for non-Hispanic Asian (P trend <0.001) population. CAHMA also increased significantly in the West region (530%) (P trend <0.001) and South region (200%) (P trend <0.001) of the United States. Men, Hispanic population, age groups 26 to 40 and 41 to 64 years, and Western regions showed a significantly higher uptrend than their counterparts (P trend <0.001).
UNASSIGNED: CAHMA have increased significantly in the United States. Men, Hispanics, non-Hispanic Asian, age groups 41 to 64. and western regions showed a higher proportional increase highlighting gender-based, racial/ethnic, and regional disparities over the study period.

BACKGROUND: Older adults are too often hospitalized from the emergency department (ED) without needing hospital care. Knowledge about rates and causes of these preventable emergency admissions (PEAs) is limited. This study aimed to assess the proportion of PEAs, the level of agreement on perceived preventability between physicians and patients, and to explore their underlying causes as perceived by patients, their relatives, and the admitting physician.
METHODS: A multi-center multi-method study at the ED of one academic and two regional hospitals in the Netherlands was performed. All patients aged > 70 years and hospitalized from the ED were consecutively sampled during a six-week period. Quantitative data regarding patient and clinical characteristics and perceived preventability of the admission were prospectively collected from the electronical medical record and analyzed using descriptive statistics. Agreement on preventability between patient, caregivers and physicians was assessed by using the Cohen\'s kappa. Underlying causes of a PEA were subsequently collected by semi-structured interviews with patients and caregivers. Physician\'s perceived causes of a PEA were collected by telephone interviews and by open-ended questions sent by email. Thematic content analysis was used to analyze the interview transcripts and email narratives.
RESULTS: Out of 773 admissions, 56 (7.2%) were deemed preventable by patients or their caregivers. Admitting physicians regarded 75 (9.7%) admissions as preventable. The level of agreement between these two groups was low with a Cohen\'s kappa score of 0.10 (p = 0.003). Perceived causes for PEAs related to six themes: (1) insufficient support at home, (2) suboptimal care in the community setting, (3) errors in hospital care, (4) time of presentation to ED and availability of resources, (5) delayed help seeking behavior, and (6) errors made by patients.
CONCLUSIONS: Our findings contribute to the existing evidence that a substantial part (almost one out of ten) of the older adults visiting the ED is perceived as unnecessary hospital care by patients, caregivers and health care providers. Findings also provide valuable insight into the causes for PEAs from a patient perspective. Further research is needed to understand why the perspectives of those responsible for hospital admission and those being admitted vary considerably.

BACKGROUND: Under- or late identification of pulmonary embolism (PE)-a thrombosis of 1 or more pulmonary arteries that seriously threatens patients\' lives-is a major challenge confronting modern medicine.
OBJECTIVE: We aimed to establish accurate and informative machine learning (ML) models to identify patients at high risk for PE as they are admitted to the hospital, before their initial clinical checkup, by using only the information in their medical records.
METHODS: We collected demographics, comorbidities, and medications data for 2568 patients with PE and 52,598 control patients. We focused on data available prior to emergency department admission, as these are the most universally accessible data. We trained an ML random forest algorithm to detect PE at the earliest possible time during a patient\'s hospitalization-at the time of his or her admission. We developed and applied 2 ML-based methods specifically to address the data imbalance between PE and non-PE patients, which causes misdiagnosis of PE.
RESULTS: The resulting models predicted PE based on age, sex, BMI, past clinical PE events, chronic lung disease, past thrombotic events, and usage of anticoagulants, obtaining an 80% geometric mean value for the PE and non-PE classification accuracies. Although on hospital admission only 4% (1942/46,639) of the patients had a diagnosis of PE, we identified 2 clustering schemes comprising subgroups with more than 61% (705/1120 in clustering scheme 1; 427/701 and 340/549 in clustering scheme 2) positive patients for PE. One subgroup in the first clustering scheme included 36% (705/1942) of all patients with PE who were characterized by a definite past PE diagnosis, a 6-fold higher prevalence of deep vein thrombosis, and a 3-fold higher prevalence of pneumonia, compared with patients of the other subgroups in this scheme. In the second clustering scheme, 2 subgroups (1 of only men and 1 of only women) included patients who all had a past PE diagnosis and a relatively high prevalence of pneumonia, and a third subgroup included only those patients with a past diagnosis of pneumonia.
CONCLUSIONS: This study established an ML tool for early diagnosis of PE almost immediately upon hospital admission. Despite the highly imbalanced scenario undermining accurate PE prediction and using information available only from the patient\'s medical history, our models were both accurate and informative, enabling the identification of patients already at high risk for PE upon hospital admission, even before the initial clinical checkup was performed. The fact that we did not restrict our patients to those at high risk for PE according to previously published scales (eg, Wells or revised Genova scores) enabled us to accurately assess the application of ML on raw medical data and identify new, previously unidentified risk factors for PE, such as previous pulmonary disease, in general populations.

UNASSIGNED: Lymphocytopenia has emerged as a simply obtained laboratory value that may correlate with prognosis. In this study we aim to study absolute Lymphocyte count after clinical recovery.
UNASSIGNED: Observational study was conducted in Covid dedicated Hospital in Mizoram. Absolute lymphocyte count is obtained from the differential leucocyte count of the patients. The obsolute Lymphocyte count at the time of hospital admission is compared with the Absolute Lymphocyte count at the time of hospital discharge after the patient obtained clinical recovery.
UNASSIGNED: Absolute Lymphocyte Count at the time of admission has a mean of 2004.48 and standard deviation of 1204.868. Absolute Lymphocyte Count at the time of discharge has a mean of 1943.68 and standard devaiton of 842.228. Pearsons correlation coefficientis showed that there is positive correlation between the variables (Correlation coeffiecient = .325). Also, the correlation is statistically significant (P < 0.05). Paired Sample t-test showed there is no statistical significant difference between Absolute Lymphocyte Count- at the time of admission and at the time of discharge (P > 0.05) at 95% Conficence Interval.
UNASSIGNED: Our study showed that Absolute Lymphocyte count had no signicant difference at the time of hospital admission and after clinical recovery.

OBJECTIVE: Patient reported experience measures (PREMs) are tools often utilised in hospitals to support quality improvements and to provide objective feedback on care experiences. Less commonly PREMs can be used to support consumers choices in their hospital care. Little is known about the experience and views of the Australian consumer regarding PREMs nor the considerations these consumers have when they need to make decisions about attending hospital. This study aimed to explore consumer awareness of PREMs, consumer attitudes towards PREMs and the utility of PREMs as a decision-making tool in accessing hospital care.
METHODS: Qualitative study involving semi-structured interviews conducted over the phone. Participants (n = 40) were recruited from across Australia and purposively sampled according to key characteristics: holding private health insurance, > 30-years of age, may have accessed private hospital care in the past year, variety of educational and cultural backgrounds, and if urban or rural residing. Interviews were audio-recorded, transcribed, and analysed thematically.
RESULTS: Four overarching themes and six subthemes were identified from the data. Major findings were that prior awareness of PREMs was limited; however, many had filled in a PREM either for themselves or for someone they cared for following a hospital stay. Most respondents preferred to listen to experience of self or family/friends or the recommendation of their physician when choosing a hospital to attend. Participants appeared to be more interested in the treating clinician than the hospital with this clinician often dictating the hospital or hospital options. If provided choice in hospital, issues of additional costs, timeliness of treatment and location were important factors.
CONCLUSIONS: While PREMs were considered a possible tool to assist in hospital decision-making process, previous hospital experiences, the doctor and knowing up-front cost are an overriding consideration for consumers when choosing their hospital. Consideration to format and presentation of PREMs data is needed to facilitate understanding and allow meaningful comparisons. Future research could examine the considerations of those consumers who primarily access public healthcare facilities and how to improve the utility of PREMs.