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Development and validation of a simplified BRASS index to screen hospital patients needing personalized discharge planning.

ABSTRACT: Discharge planning is an important component of hospital care. The Blaylock Risk Assessment Screening Score (BRASS) index is an instrument used to identify patients requiring complex discharge planning.(1) Evaluate the ability of the original BRASS index to predict the risk of complex discharge and hospital mortality. (2) Develop and validate a simplified BRASS index by eliminating redundant variables and re-estimating the predictor weights.Prospective cohort study.Patients admitted at the general internal medicine wards of tertiary referral hospital in Turin, Italy, and screened within 48 h using the BRASS index.The first phase of the study assessed the performance of the original BRASS index in predicting the risk of complex discharge and hospital mortality, then a simplified score was developed. In the second phase, temporal validation of the simplified BRASS index was performed. The probability of each discharge modality (discharged at home without complications, complex discharge, and dead in hospital) was modeled using polytomous logistic regression. The AUC was used to compare the performance of the different models.Among 6044 patients in the first phase of the study, 63% were discharged at home without complications, 31% had complex discharge, and 6% died during the hospital stay. The AUC of the simplified BRASS index, compared with the original index were 0.71 vs. 0.70 for complex discharge and 0.83 vs. 0.80 for hospital mortality. In the validation set (3325 patients), the simplified BRASS index discriminates the outcome categories with an AUC of 0.69 and 0.81 for complex discharge and hospital mortality, respectively.The new, simplified BRASS index showed a slightly better performance in predicting the risk of complex discharge and hospital mortality than the original tool and takes less time to be applied. These results were also confirmed in the validation set.

SUBMITTER: Zarovska A

PROVIDER: S-EPMC6025690 | biostudies-other | 2018 Jul

REPOSITORIES: biostudies-other

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Development and validation of a simplified BRASS index to screen hospital patients needing personalized discharge planning.

Zarovska Adriana A Evangelista Andrea A Boccia Tiziana T Ciccone Giovannino G Coggiola Daniela D Scarmozzino Antonio A Corsi Daniela D

Journal of general internal medicine 20180416 7

<h4>Background</h4>Discharge planning is an important component of hospital care. The Blaylock Risk Assessment Screening Score (BRASS) index is an instrument used to identify patients requiring complex discharge planning.<h4>Objectives</h4>(1) Evaluate the ability of the original BRASS index to predict the risk of complex discharge and hospital mortality. (2) Develop and validate a simplified BRASS index by eliminating redundant variables and re-estimating the predictor weights.<h4>Design</h4>Pr ...[more]

PMID: 29663280

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Project description:BACKGROUND:Discharge planning is a routine feature of health systems in many countries. The aim of discharge planning is to reduce hospital length of stay and unplanned readmission to hospital, and to improve the co-ordination of services following discharge from hospital.This is the third update of the original review. OBJECTIVES:To assess the effectiveness of planning the discharge of individual patients moving from hospital. SEARCH METHODS:We updated the review using the Cochrane Central Register of Controlled Trials (CENTRAL) (2015, Issue 9), MEDLINE, EMBASE, CINAHL, the Social Science Citation Index (last searched in October 2015), and the US National Institutes of Health trial register (ClinicalTrials.gov). SELECTION CRITERIA:Randomised controlled trials (RCTs) that compared an individualised discharge plan with routine discharge care that was not tailored to individual participants. Participants were hospital inpatients. DATA COLLECTION AND ANALYSIS:Two authors independently undertook data analysis and quality assessment using a pre-designed data extraction sheet. We grouped studies according to patient groups (elderly medical patients, patients recovering from surgery, and those with a mix of conditions) and by outcome. We performed our statistical analysis according to the intention-to-treat principle, calculating risk ratios (RRs) for dichotomous outcomes and mean differences (MDs) for continuous data using fixed-effect meta-analysis. When combining outcome data was not possible because of differences in the reporting of outcomes, we summarised the reported data in the text. MAIN RESULTS:We included 30 trials (11,964 participants), including six identified in this update. Twenty-one trials recruited older participants with a medical condition, five recruited participants with a mix of medical and surgical conditions, one recruited participants from a psychiatric hospital, one from both a psychiatric hospital and from a general hospital, and two trials recruited participants admitted to hospital following a fall. Hospital length of stay and readmissions to hospital were reduced for participants admitted to hospital with a medical diagnosis and who were allocated to discharge planning (length of stay MD - 0.73, 95% CI - 1.33 to - 0.12, 12 trials, moderate certainty evidence; readmission rates RR 0.87, 95% CI 0.79 to 0.97, 15 trials, moderate certainty evidence). It is uncertain whether discharge planning reduces readmission rates for patients admitted to hospital following a fall (RR 1.36, 95% CI 0.46 to 4.01, 2 trials, very low certainty evidence). For elderly patients with a medical condition, there was little or no difference between groups for mortality (RR 0.99, 95% CI 0.79 to 1.24, moderate certainty). There was also little evidence regarding mortality for participants recovering from surgery or who had a mix of medical and surgical conditions. Discharge planning may lead to increased satisfaction for patients and healthcare professionals (low certainty evidence, six trials). It is uncertain whether there is any difference in the cost of care when discharge planning is implemented with patients who have a medical condition (very low certainty evidence, five trials). AUTHORS' CONCLUSIONS:A discharge plan tailored to the individual patient probably brings about a small reduction in hospital length of stay and reduces the risk of readmission to hospital at three months follow-up for older people with a medical condition. Discharge planning may lead to increased satisfaction with healthcare for patients and professionals. There is little evidence that discharge planning reduces costs to the health service.

Project description:BackgroundDischarge planning is a routine feature of health systems in many countries that aims to reduce delayed discharge from hospital, and improve the co-ordination of services following discharge from hospital and reduce the risk of hospital readmission. This is the fifth update of the original review.ObjectivesTo assess the effectiveness of planning the discharge of individual patients moving from hospital.Search methodsWe searched CENTRAL, MEDLINE, Embase and two trials registers on 20 April 2021. We searched two other databases up to 31 March 2020. We also conducted reference checking, citation searching and contact with study authors to identify additional studies.Selection criteriaRandomised trials that compared an individualised discharge plan with routine discharge that was not tailored to individual participants. Participants were hospital inpatients.Data collection and analysisTwo review authors independently undertook data analysis and quality assessment using a pre-designed data extraction sheet. We grouped studies by older people with a medical condition, people recovering from surgery, and studies that recruited participants with a mix of conditions. We calculated risk ratios (RRs) for dichotomous outcomes and mean differences (MDs) for continuous data using fixed-effect meta-analysis. When combining outcome data it was not possible because of differences in the reporting of outcomes, we summarised the reported results for each trial in the text.Main resultsWe included 33 trials (12,242 participants), four new trials included in this update. The majority of trials (N = 30) recruited participants with a medical diagnosis, average age range 60 to 84 years; four of these trials also recruited participants who were in hospital for a surgical procedure. Participants allocated to discharge planning and who were in hospital for a medical condition had a small reduction in the initial hospital length of stay (MD - 0.73, 95% confidence interval (CI) - 1.33 to - 0.12; 11 trials, 2113 participants; moderate-certainty evidence), and a relative reduction in readmission to hospital over an average of three months follow-up (RR 0.89, 95% CI 0.81 to 0.97; 17 trials, 5126 participants; moderate-certainty evidence). There was little or no difference in participant's health status (mortality at three- to nine-month follow-up: RR 1.05, 95% CI 0.85 to 1.29; 8 trials, 2721 participants; moderate certainty) functional status and psychological health measured by a range of measures, 12 studies, 2927 participants; low certainty evidence). There was some evidence that satisfaction might be increased for patients (7 trials), caregivers (1 trial) or healthcare professionals (2 trials) (very low certainty evidence). The cost of a structured discharge plan compared with routine discharge is uncertain (7 trials recruiting 7873 participants with a medical condition; very low certainty evidence).Authors' conclusionsA structured discharge plan that is tailored to the individual patient probably brings about a small reduction in the initial hospital length of stay and readmissions to hospital for older people with a medical condition, may slightly increase patient satisfaction with healthcare received. The impact on patient health status and healthcare resource use or cost to the health service is uncertain.

Project description:BackgroundA standardized discharge plan is important to continuous medical care and discharge management of stroke patients. Currently, there is a lack of high-quality, evidence-based discharge planning guidelines for stroke patients. Most existing discharge planning guidelines have been developed for other diseases and stroke-related guidelines focus more on prevention, treatment, and rehabilitation and less on discharge planning. Therefore, they do not provide a systematic and comprehensive answer to the key issues of discharge planning for stroke patients. To improve the level of recovery and quality of life of stroke patients, to better guide clinical caregivers in developing and implementing discharge plans, the Evidence-based Nursing Center of West China Hospital, Sichuan University and the World Health Organization (WHO) Collaborating Centre for Guideline Implementation and Knowledge Translation have jointly initiated the development of the clinical practice guideline for discharge planning of patients with stroke.MethodsThe guideline development process is designed to follow the WHO handbook for guideline development and Guidelines 2.0. Evidence grading and guideline recommendations are based on the Grading of Recommendations Assessment, Development, and Evaluation (GRADE). The key steps in developing the guideline include: (I) establishing the guideline working groups; (II) selecting the priority clinical questions; (III) evidence retrieval and evaluation; (IV) grading the quality of evidence; (V) forming recommendations; and (VI) external review.DiscussionThis guideline will follow the clinical characteristics and management priorities of stroke and will be developed by a multidisciplinary guideline development team, in strict accordance with the core principles and methods of guideline development. This guideline will provide an evidence-based reference for standardized discharge screening, assessment, discharge procedures, and outpatient follow up, so as to improve the quality of discharge services and standardize the discharge management of stroke patients, and ultimately improve their post-discharge rehabilitation and quality of life.Trial registrationThe guideline was registered at the Practice guidelines REgistration for transPAREncy. The registration No. is IPGRP-2022CN331.

Project description:BackgroundReadmissions to hospital are common, costly and often preventable. An easy-to-use index to quantify the risk of readmission or death after discharge from hospital would help clinicians identify patients who might benefit from more intensive post-discharge care. We sought to derive and validate an index to predict the risk of death or unplanned readmission within 30 days after discharge from hospital to the community.MethodsIn a prospective cohort study, 48 patient-level and admission-level variables were collected for 4812 medical and surgical patients who were discharged to the community from 11 hospitals in Ontario. We used a split-sample design to derive and validate an index to predict the risk of death or nonelective readmission within 30 days after discharge. This index was externally validated using administrative data in a random selection of 1,000,000 Ontarians discharged from hospital between 2004 and 2008.ResultsOf the 4812 participating patients, 385 (8.0%) died or were readmitted on an unplanned basis within 30 days after discharge. Variables independently associated with this outcome (from which we derived the mnemonic "LACE") included length of stay ("L"); acuity of the admission ("A"); comorbidity of the patient (measured with the Charlson comorbidity index score) ("C"); and emergency department use (measured as the number of visits in the six months before admission) ("E"). Scores using the LACE index ranged from 0 (2.0% expected risk of death or urgent readmission within 30 days) to 19 (43.7% expected risk). The LACE index was discriminative (C statistic 0.684) and very accurate (Hosmer-Lemeshow goodness-of-fit statistic 14.1, p=0.59) at predicting outcome risk.InterpretationThe LACE index can be used to quantify risk of death or unplanned readmission within 30 days after discharge from hospital. This index can be used with both primary and administrative data. Further research is required to determine whether such quantification changes patient care or outcomes.

Project description:Objective:In the era of fast track surgery, early and accurately estimating whether postoperative length of stay (p-LOS) will be prolonged after lung cancer surgery is very important, both for patient's discharge planning and hospital bed management. Pulmonary function tests (PFTs) are very valuable routine examinations which should not be underutilized before lung cancer surgery. Thus, this study aimed to establish an accurate but simple prediction tool, based on PFTs, for achieving a personalized prediction of prolonged p-LOS in patients following lung resection. Methods:The medical information of 1257 patients undergoing lung cancer surgery were retrospectively reviewed and served as the training set. p-LOS exceeding the third quartile value was considered prolonged. Using logistic regression analyses, potential predictors of prolonged p-LOS were identified among various preoperative factors containing PFTs and intraoperative factors. A nomogram was constructed and subjected to internal and external validation. Results:Five independent risk factors for prolonged p-LOS were identified, including older age, being male, and ratio of residual volume to total lung capacity (RV/TLC)???45.0% which is the only modifiable risk factor, more invasive surgical approach, and surgical type. The nomogram comprised of these five predictors exhibited sufficient predictive accuracy, with the area under the receiver operating characteristic curve (AUC) of 0.76 [95% confidence interval (CI) 0.73-0.79] in the internal validation. Also its predictive performance remained fine in the external validation, with the AUC of 0.70 (95% CI 0.60-0.79). The calibration curves showed satisfactory agreements between the model predicted probability and the actually observed probability. Conclusions:Preoperative amelioration of RV/TLC may prevent lung cancer patients from unnecessary prolonged p-LOS. The integrated nomogram we developed could provide personalized risk prediction of prolonged p-LOS. This prediction tool may help patients perceive expected hospital stays and enable clinicians to achieve better bed management after lung cancer surgery.

Project description:PurposeTo translate the Core Outcome Measures Index (COMI) into Simplified Chinese and then validate it for Mainland Chinese patients with low back pain (LBP).MethodsA total of 120 consecutive patients with LBP >3 months who visited our outpatient clinic from December 2011 to March 2012 were asked to complete a questionnaire booklet including the following: (1) the Roland Morris disability questionnaire (RMQ) (Fan et al. in Spine 37(10):875-880, 2012), (2) the Short Form Health Survey (SF-36) (Zhang et al. in Int J Med Sci 9(7):521-526, 2012), (3) the Oswestry Disability Index (ODI) (Liu et al. in Spine 34(11):1211-1216, 2009), (4) visual analogue scale (VAS) measure of pain, and (5) COMI. These patients were also asked to complete a second COMI questionnaire and a transition questionnaire (5-point Likert scale: better, a little better, no change, a little worse, worse) and to return the second COMI questionnaire via mail within 1 month.ResultsThe floor effects for the COMI items ranged from 5.8 to 12.5 %. High values (28.3, 27.5, and 25.8 %, respectively) were found for symptom-specific quality of life, social disability, and work disability. Regarding the ceiling effects, the social and work disabilities were relatively high at 17.5 and 24.2 %, respectively. For other items, the values ranged from 0 to 14.2 %. Neither floor nor ceiling effects were found for the COMI summary score. Excellent correlations were found between the COMI pain scores and VAS scores (Rho = 0.89) and between the COMI pain and the SF-36 bodily pain domain (Rho = 0.84). Other individual items and summary scores showed a very good correlation (Rho = 0.54-0.72) with the corresponding questionnaires except for "symptom-specific well-being" (0.31-0.45). One-way repeated measures ANOVA was used to determine the intraclass correlation coefficient (ICC). The ICC for the entire COMI score was 0.91 (95 % CI 0.85-0.94) and 0.81-0.86 for the two pain scores (back and leg). The "minimum detectable change'' (MDC 95 %) for the COMI summary score was 1.91 points. No significant difference in the mean values was found for the repeated scores of individual items or the summary score.ConclusionThe Simplified Chinese version of COMI showed satisfactory reliability and good psychometric properties. This concise questionnaire is suitable for widespread use in Mainland China.

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Development and validation of a simplified BRASS index to screen hospital patients needing personalized discharge planning.

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Development and validation of a simplified BRASS index to screen hospital patients needing personalized discharge planning.

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