INTRODUCTIONWhile the readmission rate from community hospitals is known, the factors affecting it are not. Our aim was to determine the factors predicting unplanned readmissions from community hospitals (CHs) to acute hospitals (AHs).

MATERIALS AND METHODSThis was an observational prospective cohort study, involving 842 patients requiring post-acute rehabilitation in 2 CHs admitted from 3 AHs in Singapore. We studied the role of the Cumulative Illness Rating Scale (CIRS) organ impairment scores, the Mini-mental State Examination (MMSE) score, the Shah modified Barthel Index (BI) score, and the triceps skin fold thickness (TSFT) in predicting the rate of unplanned readmissions (UR), early unplanned readmissions (EUPR) and late unplanned readmissions (LUPR). We developed a clinical prediction rule to determine the risk of UR and EUPR.

RESULTSThe rates of EUPR and LUPR were 7.6% and 10.3% respectively. The factors that predicted UR were the CIRS-heart score, the CIRS-haemopoietic score, the CIRS-endocrine / metabolic score and the BI on admission. The MMSE was predictive of EUPR. The TSFT and CIRS-liver score were predictive of LUPR. Upon receiver operator characteristics analysis, the clinical prediction rules for the prediction of EUPR and UR had areas under the curve of 0.745 and 0.733 respectively. The likelihood ratios of the clinical prediction rules for EUPR and UR ranged from 0.42 to 5.69 and 0.34 to 3.16 respectively.

CONCLUSIONSPatients who have UR can be identified by the admission BI, the MMSE, the TSFT and CIRS scores in the cardiac, haemopoietic, liver and endocrine/metabolic systems.

Acute Disease ; therapy ; Aged ; Female ; Follow-Up Studies ; Hospitals, Community ; statistics & numerical data ; Hospitals, Special ; statistics & numerical data ; Humans ; Intensive Care Units ; statistics & numerical data ; Male ; Patient Readmission ; trends ; Prospective Studies ; Risk Factors ; Severity of Illness Index ; Singapore

INTRODUCTIONThis study aimed to examine the attendance rates of post-discharge supervised rehabilitation as recommended by the multidisciplinary team at discharge among subacutely disabled adults and the barriers preventing adherence.

MATERIALS AND METHODSPatients were from a community hospital, aged 40 years or older. They had been assessed by a multidisciplinary team to benefit from rehabilitation after discharge, were mentally competent and communicative. We used a sequential qualitative-quantitative mixed methods study design. In the initial qualitative phase, we studied the patient-perceived barriers to adherence to rehabilitation using semi-structured interviews. Emerging themes were then analysed and used to develop a questionnaire to measure the extent of these barriers. In the subsequent quantitative phase, the questionnaire was used with telephone follow-up at 3, 6, 9 and 12 months after discharge.

RESULTSQualitative phase interviews (n = 41) revealed specific perceived financial, social, physical and health barriers. At the start of the quantitative phase (n = 70), 87.1% of the patients viewed rehabilitation as beneficial, but overall longitudinal attendance rate fell from 100% as inpatient to 20.3% at 3 months, 9.8% at 6 months, 6.3% at 9 months and 4.3% at 12 months. The prevalence of physical and social barriers were high initially but decreased with time. In contrast, the prevalence of financial and perceptual barriers increased with time.

CONCLUSIONAttendance of post-hospitalisation rehabilitation in Singapore is low. Self-perceived barriers to post-discharge rehabilitation attendance were functional, social, financial and perceptual, and their prevalence varied with time.

Adult ; Aged ; Aged, 80 and over ; Female ; Health Services Accessibility ; Hospitals, Community ; Humans ; Male ; Middle Aged ; Patient Compliance ; Patient Discharge ; Qualitative Research ; Rehabilitation ; Self Concept ; Surveys and Questionnaires ; Time Factors

Background: and Purpose Recent studies suggested an increased incidence of cerebral venous thrombosis (CVT) during the coronavirus disease 2019 (COVID-19) pandemic. We evaluated the volume of CVT hospitalization and in-hospital mortality during the 1st year of the COVID-19 pandemic compared to the preceding year. Methods: We conducted a cross-sectional retrospective study of 171 stroke centers from 49 countries. We recorded COVID-19 admission volumes, CVT hospitalization, and CVT in-hospital mortality from January 1, 2019, to May 31, 2021. CVT diagnoses were identified by International Classification of Disease-10 (ICD-10) codes or stroke databases. We additionally sought to compare the same metrics in the first 5 months of 2021 compared to the corresponding months in 2019 and 2020 (ClinicalTrials.gov Identifier: NCT04934020). Results: There were 2,313 CVT admissions across the 1-year pre-pandemic (2019) and pandemic year (2020); no differences in CVT volume or CVT mortality were observed. During the first 5 months of 2021, there was an increase in CVT volumes compared to 2019 (27.5%; 95% confidence interval [CI], 24.2 to 32.0; P<0.0001) and 2020 (41.4%; 95% CI, 37.0 to 46.0; P<0.0001). A COVID-19 diagnosis was present in 7.6% (132/1,738) of CVT hospitalizations. CVT was present in 0.04% (103/292,080) of COVID-19 hospitalizations. During the first pandemic year, CVT mortality was higher in patients who were COVID positive compared to COVID negative patients (8/53 [15.0%] vs. 41/910 [4.5%], P=0.004). There was an increase in CVT mortality during the first 5 months of pandemic years 2020 and 2021 compared to the first 5 months of the pre-pandemic year 2019 (2019 vs. 2020: 2.26% vs. 4.74%, P=0.05; 2019 vs. 2021: 2.26% vs. 4.99%, P=0.03). In the first 5 months of 2021, there were 26 cases of vaccine-induced immune thrombotic thrombocytopenia (VITT), resulting in six deaths. Conclusions During the 1st year of the COVID-19 pandemic, CVT hospitalization volume and CVT in-hospital mortality did not change compared to the prior year. COVID-19 diagnosis was associated with higher CVT in-hospital mortality. During the first 5 months of 2021, there was an increase in CVT hospitalization volume and increase in CVT-related mortality, partially attributable to VITT.