A predictive model for identifying patients at risk of delayed transfer of care: a retrospective, cross-sectional study of routinely collected data.

BACKGROUND: Delays to the transfer of care from hospital to other settings represent a significant human and financial cost. This delay occurs when a patient is clinically ready to leave the inpatient setting but is unable to because other necessary care, support or accommodation is unavailable. The aim of this study was to interrogate administrative and clinical data routinely collected when a patient is admitted to hospital following attendance at the emergency department (ED), to identify factors related to delayed transfer of care (DTOC) when the patient is discharged. We then used these factors to develop a predictive model for identifying patients at risk for delayed discharge of care. OBJECTIVE: To identify risk factors related to the delayed transfer of care and develop a prediction model using routinely collected data. METHODS: This is a single centre, retrospective, cross-sectional study of patients admitted to an English National Health Service university hospital following attendance at the ED between January 2018 and December 2020. Clinical information (e.g. national early warning score (NEWS)), as well as administrative data that had significant associations with admissions that resulted in delayed transfers of care, were used to develop a predictive model using a mixed-effects logistic model. Detailed model diagnostics and statistical significance, including receiver operating characteristic analysis, were performed. RESULTS: Three-year (2018-20) data were used; a total of 92 444 admissions (70%) were used for model development and 39 877 (30%) admissions for model validation. Age, gender, ethnicity, NEWS, Glasgow admission prediction score, Index of Multiple Deprivation decile, arrival by ambulance and admission within the last year were found to have a statistically significant association with delayed transfers of care. The proposed eight-variable predictive model showed good discrimination with 79% sensitivity (95% confidence intervals (CIs): 79%, 81%), 69% specificity (95% CI: 68%, 69%) and 70% (95% CIs: 69%, 70%) overall accuracy of identifying patients who experienced a DTOC. CONCLUSION: Several demographic, socio-economic and clinical factors were found to be significantly associated with whether a patient experiences a DTOC or not following an admission via the ED. An eight-variable model has been proposed, which is capable of identifying patients who experience delayed transfers of care with 70% accuracy. The eight-variable predictive tool calculates the probability of a patient experiencing a delayed transfer accurately at the time of admission.

Significance of hemogram-derived ratios for predicting in-hospital mortality in COVID-19: A multicenter study.

Background: To address the problem of resource limitation, biomarkers having a potential for mortality prediction are urgently required. This study was designed to evaluate whether hemogram-derived ratios could predict in-hospital deaths in COVID-19 patients. Materials and Methods: This multicenter retrospective study included hospitalized COVID-19 patients from four COVID-19 dedicated hospitals in Sylhet, Bangladesh. Data on clinical characteristics, laboratory parameters, and survival outcomes were analyzed. Logistic regression models were fitted to identify the predictors of in-hospital death. Results: Out of 442 patients, 55 (12.44%) suffered in-hospital death. The proportion of male was higher in nonsurvivor group (61.8%). The mean age was higher in nonsurvivors (69 ± 13 vs. 59 ± 14 years, p < 0.001). Compared to survivors, nonsurvivors exhibited higher frequency of comorbidities, such as chronic kidney disease (34.5% vs. 15.2%, p ≤ 0.001), chronic obstructive pulmonary disease (23.6% vs. 10.6%, p = 0.011), ischemic heart disease (41.8% vs. 19.4%, p < 0.001), and diabetes mellitus (76.4% vs. 61.8%, p = 0.05). Leukocytosis and lymphocytopenia were more prevalent in nonsurvivors (p < 0.05). Neutrophil-to-lymphocyte ratio (NLR), derived NLR (d-NLR), and neutrophil-to-platelet ratio (NPR) were significantly higher in nonsurvivors (p < 0.05). After adjusting for potential covariates, NLR (odds ratio [OR] 1.05; 95% confidence interval [CI] 1.009-1.08), d-NLR (OR 1.08; 95% CI 1.006-1.14), and NPR (OR 1.20; 95% CI 1.09-1.32) have been found to be significant predictors of mortality in hospitalized COVID-19 patients. The optimal cut-off points for NLR, d-NLR, and NPR for prediction of in-hospital mortality for COVID-19 patients were 7.57, 5.52 and 3.87, respectively. Conclusion: Initial assessment of NLR, d-NLR, and NPR values at hospital admission is of good prognostic value for predicting mortality of patients with COVID-19.

Role of hemogram-derived ratios in predicting intensive care unit admission in COVID-19 patients: a multicenter study.

Purpose: As hyperinflammation is recognized as a driver of severe COVID-19 disease, checking markers of inflammation is gaining more attention. Our study aimed to evaluate the utility of cost-effective hemogram-derived ratios in predicting intensive care unit (ICU) admission in COVID-19 patients. Methods: This multicenter retrospective study included hospitalized COVID-19 patients from four dedicated COVID-19 hospitals in Sylhet, Bangladesh. Data on demographics, clinical characteristics, laboratory parameters and survival outcomes were analyzed. Logistic regression analysis was used to identify the significance of each hemogram-derived ratio in predicting ICU admission. Results: Of 442 included patients, 98 (22.17%) required ICU admission. At the time of admission, patients requiring ICU had a higher neutrophil count and lower lymphocyte and platelet counts than patients not requiring ICU. Peripheral capillary oxygen saturation at admission was significantly lower in those who subsequently required ICU admission. Neutrophil-to-lymphocyte ratio, derived neutrophil-to-lymphocyte ratio, neutrophil-to-platelet ratio, and systemic immune-inflammation index were significant predictors of ICU admission. Conclusion: Hemogram-derived ratios can be an effective tool in facilitating the early categorization of at-risk patients, enabling timely measures to be taken early in the disease course.

Genetic Variations of RAD51 and XRCC2 Genes Increase the Risk of Colorectal Cancer in Bangladeshi Population.

OBJECTIVES: In case of Bangladeshi population, no report is observed till now showing the genetic variations of RAD51 (rs1801320) and XRCC2 (rs3218536) genes polymorphism having association with colorectal cancer risk. For this reason the aim of this study is to ascertain their interrelation with colorectal cancer occurrence in Bangladeshi population. MATERIALS AND METHODS: A case control study was conducted where 200 colorectal cancer patients and 200 healthy volunteers were figured for this research using Polymerase Chain Reaction-Restriction Fragment Length Polymorphism (PCR-RFLP). RESULTS: Here, in case of RAD51 (rs1801320), G/C heterozygous genotype was found significant (p=0.037; OR=1.64; 95% CI=1.03 to 2.6). On the other hand, G/G genotype was not found statistically significant (p=0.423; OR=1.61; 95% CI=0.49 to 5.22) and significance was observed for GC+GG (p=0.030; OR=1.63; 95% CI=1.05 to 2.55). In case of XRCC2 (rs3218536), C/T heterozygous genotype was remarked statistically significant (p=0.033; OR=1.60; 95% CI=1.04 to 2.46). The T/T genotype was not recorded statistically significant (p=0.237; OR=1.65; 95% CI=0.72 to 3.76) but significance found for CT+TT (p=0.027; OR=1.61; 95% CI=1.05 to 2.45). Moreover, it is found that the risk factor of developing CRC is observed in G/C, C/T heterozygote and GC+GG, CT+TT (heterozygote+ mutant) in RAD51 (rs1801320) and XRCC2 (rs3218536) respectively although no significance is observed in case of G/G and T/T mutant. CONCLUSIONS: So, the association of RAD51 (rs1801320) and XRCC2 (rs3218536) genes polymorphism with colorectal cancer risk is observed in Bangladeshi population.

Maternal high-risk fertility behavior and association with chronic undernutrition among children under age 5 y in India, Bangladesh, and Nepal: Do poor children have a higher risk?

OBJECTIVES: We aimed to examine whether an association exists between maternal high-risk fertility behavior and chronic undernutrition among children under 5 y of age. In addition, we explored the relationship between poverty and high-risk fertility behavior and the relative roles they play as obstacles in the reduction of the risk of undernutrition among children. METHODS: The analysis was based on responses from married women ages 15 to 49 who lived with at least one child under the age of 5; and three cross-sectional, nationally representative samples from India, Bangladesh, and Nepal were considered. RESULTS: Maternal high-risk fertility behavior was associated with an increased risk of chronic undernutrition among children in India, Bangladesh, and Nepal. Multiple high-risk categories appeared to have more profound consequences on the outcomes measured. Findings also demonstrated that with regard to the risk of undernutrition, children of mothers who were either poor or who experienced high-risk fertility were not uniquely disadvantaged. CONCLUSIONS: The results suggest that with regard to the risk of chronic undernutrition, the negative effect of high-risk fertility behavior extends across all economic backgrounds and is not limited to children of mothers who were either poor or who experienced high-risk fertility.