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Prediction of post-acute care demand in medical and neurological inpatients: diagnostic assessment of the post-acute discharge score - a prospective cohort study.

ABSTRACT: Early identification of patients requiring transfer to post-acute care (PAC) facilities shortens hospital stays. With a focus on interprofessional assessment of biopsychosocial risk, this study's aim was to assess medical and neurological patients' post-acute care discharge (PACD) scores on days 1 and 3 after hospital admission regarding diagnostic accuracy and effectiveness as an early screening tool. The transfer to PAC facilities served as the outcome ("gold standard").In this prospective cohort study, registered at ClinicalTrial.gov (NCT01768494) on January 2013, 1432 medical and 464 neurological patients (total n =?1896) were included consecutively between February and October 2013. PACD scores and other relevant data were extracted from electronic records of patient admissions, hospital stays, and interviews at day 30 post-hospital admission. To gauge the scores' accuracy, we plotted receiver operating characteristic (ROC) curves, calculated area under the curve (AUC), and determined sensitivity and specificity at various cut-off levels.Medical patients' day 1 and day 3 PACD scores accurately predicted discharge to PAC facilities, with respective discriminating powers (AUC) of 0.77 and 0.82. With a PACD cut-off of ?8 points, day 1 and 3 sensitivities were respectively 72.6% and 83.6%, with respective specificities of 66.5% and 70.0%. Neurological patients' scores showed lower accuracy both days: using the same cut-off, respective day 1 and day 3 AUCs were 0.68 and 0.78, sensitivities 41.4% and 68.7% and specificities 81.4% and 83.4%.PACD scores at days 1 and 3 accurately predicted transfer to PAC facilities, especially in medical patients on day 3. To confirm and refine these results, PACD scores' value to guide discharge planning interventions and subsequent impact on hospital stay warrants further investigation.ClinialTrials.gov Identifier, NCT01768494 .

SUBMITTER: Conca A

PROVIDER: S-EPMC5812184 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature

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Prediction of post-acute care demand in medical and neurological inpatients: diagnostic assessment of the post-acute discharge score - a prospective cohort study.

Conca Antoinette A Gabele Angela A Reutlinger Barbara B Schuetz Philipp P Kutz Alexander A Haubitz Sebastian S Faessler Lukas L Batschwaroff Marcus M Schild Ursula U Caldara Zeljka Z Regez Katharina K Schirlo Susanne S Vossler Gabi G Kahles Timo T Nedeltchev Krassen K Keller Anja A Huber Andreas A De Geest Sabina S Buergi Ulrich U Tobias Petra P Louis Simonet Martine M Mueller Beat B Schäfer-Keller Petra P

BMC health services research 20180213 1

<h4>Background</h4>Early identification of patients requiring transfer to post-acute care (PAC) facilities shortens hospital stays. With a focus on interprofessional assessment of biopsychosocial risk, this study's aim was to assess medical and neurological patients' post-acute care discharge (PACD) scores on days 1 and 3 after hospital admission regarding diagnostic accuracy and effectiveness as an early screening tool. The transfer to PAC facilities served as the outcome ("gold standard").<h4> ...[more]

PMID: 29439684

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Project description:BackgroundDelays in patient discharge can not only lead to deterioration, especially among geriatric patients, but also incorporate unnecessary resources at the hospital level. Many of these delays and their negative impact may be preventable by early focused screening to identify patients at risk for transfer to a post-acute care facility. Early interprofessional discharge planning is crucial in order to fit the appropriate individual discharge destination. While prediction of discharge to a post-acute care facility using post-acute care discharge score, the self-care index, and a combination of both has been shown in a single-center pilot study, an external validation is still missing.ObjectiveThis paper outlines the study protocol and methodology currently being used to replicate the previous pilot findings and determine whether the post-acute care discharge score, the self-care index, or the combination of both can reliably identify patients requiring transfer to post-acute care facilities.MethodsThis study will use prospective data involving all phases of the quasi-experimental study "In-HospiTOOL" conducted at 7 Swiss hospitals in urban and rural areas. During an 18-month period, consecutive adult medical patients admitted to the hospitals through the emergency department will be included. We aim to include 6000 patients based on sample size calculation. These data will enable a prospective external validation of the prediction instruments.ResultsWe expect to gain more insight into the predictive capability of the above-mentioned prediction instruments. This approach will allow us to get important information about the generalizability of the three different models. The study was approved by the institutional review board on November 21, 2016, and funded in May 2020. Expected results are planned to be published in spring 2021.ConclusionsThis study will provide evidence on prognostic properties, comparative performance, reliability of scoring, and suitability of the instruments for the screening purpose in order to be able to recommend application in clinical practice.International registered report identifier (irrid)DERR1-10.2196/21447.

Project description:BACKGROUND:Reducing delays in hospital discharge is important to improve transition processes and reduce health care costs. The recently proposed post-acute care discharge score focusing on the self-care abilities before hospital admission allows early identification of patients with a need for post-acute care. New limitations in self-care abilities identified during hospitalization may also indicate a risk. Our aim was to investigate whether the addition of the post-acute care discharge score and a validated self-care instrument would improve the prognostic accuracy to predict post-acute discharge needs in unselected medical inpatients. METHODS:We included consecutive adult medical and neurological inpatients. Logistic regression models with area under the receiver operating characteristic curve were calculated to study associations of post-acute discharge score and self-care index with post-acute discharge risk. We calculated joint regression models and reclassification statistics including the net reclassification index and integrated discrimination improvement to investigate whether merging the self-care index and the post-acute discharge score leads to better diagnostic accuracy. RESULTS:Out of 1342 medical and 402 neurological patients, 150 (11.18%) and 94 (23.38%) have reached the primary endpoint of being discharged to a post-acute care facility. Multivariate analysis showed that the self-care index is an outcome predictor (OR 0.897, 95%CI 0.864-0.930). By combining the self-care index and the post-acute care discharge score discrimination for medical (from area under the curve 0.77 to 0.83) and neurological patients (from area under the curve 0.68 to 0.78) could be significantly improved. Reclassification statistics also showed significant improvements with regard to net reclassification index (14.2%, p<0.05) and integrated discrimination improvement (4.83%, p<0.05). CONCLUSIONS:Incorporating an early assessment of patients' actual intrahospital self-care ability to the post-acute care discharge score led to an improved prognostic accuracy for identifying adult, medical and neurological patients at risk for discharge to a post-acute care facility.

Project description:ObjectiveTo understand what patterns of health care use are associated with higher post-hospitalization spending.Data sourcesMedicare hospital, skilled nursing, inpatient rehabilitation, and home health agency claims, and Medicare enrollment data from 2007 and 2008.Study designFor 10 common inpatient conditions, we calculated variation across hospitals in price-standardized and case mix-adjusted Medicare spending in the 30 days following hospital discharge. We estimated the fraction of spending differences between low- and high-spending hospitals attributable to readmissions versus post-acute care, and within post-acute care between inpatient rehabilitation facility (IRF) versus skilled nursing facility (SNF) use. For each service, we distinguished between differences in probability of use and spending conditional on use.Data extraction methodsWe identified index hospital claims and examined hospital and post-acute care occurring within a 30-day period following hospital discharge. For each Medicare Severity Diagnosis-Related Group (MS-DRG) at each hospital, we calculated average price-standardized Medicare payments for readmissions, SNFs, IRFs, and post-acute care overall (also including home health agencies and long-term care hospitals).Principal findingsThere was extensive variation across hospitals in Medicare spending in the 30 days following hospital discharge. For example, the interquartile range across hospitals ranged from $1,245 for chronic obstructive pulmonary disease to over $4,000 for myocardial infarction MS-DRGs. The proportion of differences attributable to readmissions versus post-acute care differed across conditions. For myocardial infarction, 74 to 93 percent of the variation was due to readmissions. For hip and femur procedures and joint replacement, 72 to 92 percent of the variation was due to differences in post-acute care spending. There was also variation in the relative importance of the type of post-acute spending. For hip and femur procedures, joint replacement, and stroke, whether patients received IRF was the key driver of variation in post-acute care spending In contrast, for pneumonia and heart failure, whether patients received SNF care was the key driver of variation in post-acute spending.ConclusionsThrough initiatives such as bundled payment, hospitals are financially responsible for spending in the post-hospitalization period. The key driver of variation in post-hospitalization spending varied greatly across conditions. For some conditions, the key driver was having a readmission, for others it was whether patients receive any post-acute care, and for others the key driver was the type of post-acute care. These findings may help hospitals implement strategies to reduce post-discharge spending.

Project description:BackgroundDelirium affects > 15% of hospitalised patients but is grossly underdetected, contributing to poor care. The 4 'A's Test (4AT, www.the4AT.com ) is a short delirium assessment tool designed for routine use without special training. The primary objective was to assess the accuracy of the 4AT for delirium detection. The secondary objective was to compare the 4AT with another commonly used delirium assessment tool, the Confusion Assessment Method (CAM).MethodsThis was a prospective diagnostic test accuracy study set in emergency departments or acute medical wards involving acute medical patients aged ≥ 70. All those without acutely life-threatening illness or coma were eligible. Patients underwent (1) reference standard delirium assessment based on DSM-IV criteria and (2) were randomised to either the index test (4AT, scores 0-12; prespecified score of > 3 considered positive) or the comparator (CAM; scored positive or negative), in a random order, using computer-generated pseudo-random numbers, stratified by study site, with block allocation. Reference standard and 4AT or CAM assessments were performed by pairs of independent raters blinded to the results of the other assessment.ResultsEight hundred forty-three individuals were randomised: 21 withdrew, 3 lost contact, 32 indeterminate diagnosis, 2 missing outcome, and 785 were included in the analysis. Mean age was 81.4 (SD 6.4) years. 12.1% (95/785) had delirium by reference standard assessment, 14.3% (56/392) by 4AT, and 4.7% (18/384) by CAM. The 4AT had an area under the receiver operating characteristic curve of 0.90 (95% CI 0.84-0.96). The 4AT had a sensitivity of 76% (95% CI 61-87%) and a specificity of 94% (95% CI 92-97%). The CAM had a sensitivity of 40% (95% CI 26-57%) and a specificity of 100% (95% CI 98-100%).ConclusionsThe 4AT is a short, pragmatic tool which can help improving detection rates of delirium in routine clinical care.Trial registrationInternational standard randomised controlled trial number (ISRCTN) 53388093 . Date applied 30/05/2014; date assigned 02/06/2014.

Project description:Background: Acute heart failure (AHF) represents a leading cause of unscheduled hospital stays, frequent rehospitalisations, and mortality worldwide. The aim of our study was to develop a bedside prognostic tool, a multivariable predictive risk score, that is useful in daily practice, thus providing an early prognostic evaluation at admission and an accurate risk stratification after discharge in patients with AHF. Methods: This study is a subanalysis of the STADE HF study, which is a single-centre, prospective, randomised controlled trial enrolling 123 patients admitted to hospital for AHF. Here, 117 patients were included in the analysis, due to data exhaustivity. Regression analysis was performed to determine predictive variables for one-year mortality and/or rehospitalisation after discharge. Results: During the first year after discharge, 23 patients died. After modellisation, the variables considered to be of prognostic relevance in terms of mortality were (1) non-ischaemic aetiology of HF, (2) elevated creatinine levels at admission, (3) moderate/severe mitral regurgitation, and (4) prior HF hospitalisation. We designed a linear model based on these four independent predictive variables, and it showed a good ability to score and predict patient mortality with an AUC of 0.84 (95%CI: 0.76-0.92), thus denoting a high discriminative ability. A risk score equation was developed. During the first year after discharge, we observed as well that 41 patients died or were rehospitalised; hence, while searching for a model that could predict worsening health conditions (i.e., death and/or rehospitalisation), only two predictive variables were identified: non-ischaemic HF aetiology and previous HF hospitalisation (also included in the one-year mortality model). This second modellisation showed a more discrete discriminative ability with an AUC of 0.67 (95% C.I. 0.59-0.77). Conclusions: The proposed risk score and model, based on readily available predictive variables, are promising and useful tools to assess, respectively, the one-year mortality risk and the one-year mortality and/or rehospitalisations in patients hospitalised for AHF and to assist clinicians in the management of patients with HF aiming at improving their prognosis.

Dataset Information

Prediction of post-acute care demand in medical and neurological inpatients: diagnostic assessment of the post-acute discharge score - a prospective cohort study.

Publications

Prediction of post-acute care demand in medical and neurological inpatients: diagnostic assessment of the post-acute discharge score - a prospective cohort study.

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