Weak Handgrip at Index Admission for Acute Exacerbation of COPD Predicts All-Cause 30-Day Readmission.
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ABSTRACT:
SUBMITTER: Witt LJ
PROVIDER: S-EPMC8058414 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundReadmission following COPD exacerbation is a common and challenging clinical problem. New approaches to predicting readmissions are required to help mitigate risk and develop novel interventions.MethodsWe conducted a prospective cohort study in 82 COPD patients admitted due to an exacerbation of COPD. Lung function measures [spirometry, forced oscillation technique (FOT) indices and peak inspiratory flow rate (PIFR)], inflammatory biomarkers and patient-reported outcomes including previous exacerbation history, breathlessness, quality of life and frailty were measured at admission and discharge. We prospectively followed patients for 30 and 90 days to identify predictors for readmission.ResultsThe readmission rate within 30 days was 38%, and 56% within 90 days. Previous exacerbations, higher COPD Assessment Test score at discharge, frailty, reduced PIFR and increased length of stay were significantly associated with 30-day readmission. PIFR at discharge and frailty had the highest predictive ability for 30-day readmission using area under receiver operating characteristic curves (AUC 0.86, 95% CI 0.78-0.95, p < 0.001 and AUC 0.81, 95% CI 0.71-0.90, p < 0.001, respectively). Ninety-day readmissions were significantly associated with previous exacerbations and hospitalisations, higher CAT score at discharge, frailty, depression, lower PIFR and greater expiratory flow limitation (EFL) in the supine position. The best predictive variable in multivariable analysis for both 30- and 90-day readmission was PIFR at discharge.ConclusionPIFR, CAT score, frailty, and EFL were found to be associated with 30- and 90-day readmission following COPD exacerbation. These findings help identify those at highest risk and to optimise care prior to discharge.
| S-EPMC8504869 | biostudies-literature
Project description:BackgroundLow handgrip strength (HGS) is independently associated with a higher exacerbation risk in stable chronic obstructive pulmonary disease (COPD); however, the relationship between HGS while being admitted for COPD exacerbation and further exacerbation risk after discharge remains unclear.MethodsWe enrolled patients admitted for COPD exacerbation between January 2018 and June 2019. HGS tests were done within 3 days after admission. The primary endpoint was exacerbations within 12 months after the index admission, which needed emergency room visits or hospital admission. We analyzed the relationships among demographics, HGS, pulmonary function parameters, and acute exacerbation events.ResultsAmong 43 enrolled patients, 31 (72.1%) participants (HGSw) had HGS weakness (22.1 ± 4.1 kg). The other 12 (27.9%) participants (non-HGSw) had the strength of handgrips 33.7 ± 3.1 kg. HGSw group showed a significantly higher rate of emergency room visits within 6, 9, and 12 months after the index admission than non-HGSw group (0.81 ± 1.30 vs. 0.08 ± 0.29, p = 0.045; 1.26 ± 1.59 vs. 0.17 ± 0.38, P = 0.019; 1.48 ± 1.86 vs. 0.25 ± 0.62, P = 0.027, respectively). There was a trend to have higher admission rate within 9 and 12 months in HGSw group, which did not achieve statistical significance (0.77 ± 1.38 vs. 0.08 ± 0.29, P = 0.064; 0.94 ± 1.56 vs. 0.08 ± 0.29, P = 0.062, respectively).ConclusionsHGS weakness measured upon admission for COPD exacerbation was associated with a higher risk of exacerbation in the next year. Trial registration ClinicalTrials.gov Identifier: NCT04885933.
| S-EPMC8296662 | biostudies-literature
Project description:OBJECTIVES:Few studies have evaluated national readmission rates after acute pancreatitis (AP) in the United States. We sought to evaluate modifiable factors impacting 30-day readmissions after AP hospitalizations. METHODS:We used the Nationwide Readmission Database (2013) involving all adults with a primary discharge diagnosis of AP. Multivariable logistic regression models assessed independent predictors for specific outcomes. RESULTS:Among 180,480 patients with AP index admissions, 41,094 (23%) had biliary AP, of which 10.5% were readmitted within 30 days. The 30-day readmission rate for patients who underwent same-admission cholecystectomy (CCY) was 6.5%, compared with 15.1% in those who did not (P < 0.001). Failure of index admission CCY increased the risk of readmissions (odds ratio [OR], 2.27; 95% confidence interval [CI], 2.04-2.56). Same-admission CCY occurred in 55% (n = 19,274) of patients without severe AP. Severe AP (OR, 0.73; 95% CI, 0.65-0.81), sepsis (OR, 0.63; 95% CI, 0.52-0.75), 3 or more comorbidities (OR, 0.74; 95% CI, 0.68-0.79), and admissions to small (OR, 0.76; 95% CI, 0.64-0.91) or rural (OR, 0.78; 95% CI, 0.65-0.95) hospitals were less likely to undergo same-admission CCY. CONCLUSIONS:Same-admission CCY should be considered in patients with biliary AP when feasible. This national appraisal recognizes modifiable risk factors to reduce readmission in biliary AP and reinforces adherence to major society guidelines.
| S-EPMC6203327 | biostudies-literature
Project description:OBJECTIVE:To investigate the longitudinal changes in principal readmission diagnoses within 30 days after hospitalization for acute exacerbation of chronic obstructive pulmonary disease (AECOPD). STUDY SETTING:Medicare claims data, 2010-2012. STUDY DESIGN:Retrospective cohort study. DATA COLLECTION METHODS:We identified AECOPD hospitalizations aged 65 years and above, and examined the principal 30-day readmission diagnoses (respiratory related, cardiac related, and other conditions). We also constructed Joinpoint regression models to test whether patients with each of the 3 major readmission conditions had a unique temporal pattern of readmission during the 30-day period. PRINCIPAL FINDINGS:Among 76,697 index hospitalizations with AECOPD, 14,090 (18.4%) were readmitted within 30 days. Respiratory-related conditions accounted for 55% of readmissions. The proportion of respiratory-related conditions as the readmission diagnosis decreased from postdischarge day 1 to day 8 (4.0% decrease), and then increased thereafter (13.2% increase; P=0.06). Cardiac-related conditions had a similar nonlinear trend with an inflection point at day 6 (P=0.02), with a subsequent downward trend from day 22 (P=0.01). By contrast, the other conditions increased from day 1 to day 6 (15.0% increase), and then significantly decreased (28.8% decrease; P=0.04). CONCLUSIONS:The proportions of principal discharge diagnosis of readmission changed significantly at around postdischarge day 7. Our findings advance research into identification of the underlying mechanisms and development of targeted interventions to prevent readmissions.
| S-EPMC6041171 | biostudies-literature
Project description:Reduction in 30-day readmission rate after chronic obstructive pulmonary disease (COPD)-related hospitalization is a national objective. However, little is known about trends in readmission rates in recent years, particularly in priority populations defined by the Agency for Healthcare Research and Quality (AHRQ)(e.g., the elderly, women, racial/ethnic minorities, low-income and rural populations, and populations with chronic illnesses).We conducted a retrospective cohort study using data from the State Inpatient Database of eight geographically-dispersed US states (Arkansas, California, Florida, Iowa, Nebraska, New York, Utah, and Washington) from 2006 through 2012. We identified all COPD-related hospitalizations by patients ?40 years old. The primary outcome was any-cause readmission within 30 days of discharge from the index hospitalization for COPD.From 2006 to 2012, a total of 845,465 hospitalizations at risk for 30-day readmissions were identified. Overall, 30-day readmission rate for COPD-related hospitalization decreased modestly from 20.0% in 2006 to 19.2% in 2012, an 0.8% absolute decrease (OR 0.991, 95%CI 0.989-0.995, Ptrend<0.001). This modest decline remained statistically significant after adjusting for patient demographics and comorbidities (adjusted OR 0.981, 95%CI 0.977-0.984, Ptrend<0.001). Similar to the overall population, the readmission rate over the 7-year period remained persistently high in most of AHRQ-defined priority populations.Our observations provide a benchmark for future investigation of the impact of Hospital Readmissions Reduction Program on readmissions after COPD hospitalization. Our findings encourage researchers and policymakers to develop effective strategies aimed at reducing readmissions among patients with COPD in an already-stressed healthcare system.
| S-EPMC5718159 | biostudies-literature
Project description:ImportanceFrail patients are known to have poor perioperative outcomes. There is a paucity of literature investigating how the Modified Frailty Index (mFI), a validated measure of frailty, is associated with unplanned readmission among military veterans following surgery.ObjectiveTo understand the association between frailty and 30-day postoperative unplanned readmission.Design, setting, and participantsA retrospective cohort study was conducted among adult patients who underwent surgery and were discharged alive from Veterans Affairs hospitals for orthopedic, general, and vascular conditions between October 1, 2007, and September 30, 2014, with a postoperative length of stay between 2 and 30 days.ExposureFrailty, as calculated by the 11 variables on the mFI.Main outcomes and measuresThe primary outcome of interest is 30-day unplanned readmission. Secondary outcomes included any 30-day predischarge or postdischarge complication, 30-day postdischarge mortality, and 30-day emergency department visit.ResultsThe study sample included 236 957 surgical procedures (among 223 877 men and 13 080 women; mean [SD] age, 64.0 [11.3] years) from high-volume surgical specialties: 101 348 procedures (42.8%) in orthopedic surgery, 92 808 procedures (39.2%) in general surgery, and 42 801 procedures (18.1%) in vascular surgery. The mFI was associated with readmission (odds ratio [OR], 1.11; 95% CI, 1.10-1.12; R2 = 10.3%; C statistic, 0.71). Unadjusted rates of overall 30-day readmission (26 262 [11.1%]), postdischarge emergency department visit (34 204 [14.4%]), any predischarge (13 855 [5.9%]) or postdischarge (14 836 [6.3%]) complication, and postdischarge mortality (1985 [0.8%]) varied by frailty in a dose-dependent fashion. In analysis by individual mFI components using Harrell ranking, impaired functional status, identified as nonindependent functional status (OR, 1.16; 95% CI, 1.11-1.21; P < .01) or having a residual deficit from a prior cerebrovascular accident (OR, 1.17; 95% CI, 1.11-1.22; P < .01), contributed most to the ability of the mFI to anticipate readmission compared with the other components. Acutely impaired sensorium (OR, 1.12; 95% CI, 0.99-1.27; P = .08) and history of a myocardial infarction within 6 months (OR, 0.93; 95% CI, 0.81-1.06; P = .28) were not significantly associated with readmission.Conclusions and relevanceThe mFI is associated with poor surgical outcomes, including readmission, primarily due to impaired functional status. Targeting potentially modifiable aspects of frailty preoperatively, such as improving functional status, may improve perioperative outcomes and decrease readmissions.
| S-EPMC5710500 | biostudies-literature
Project description:Avoidable hospital readmissions not only contribute to the high costs of healthcare in the US, but also have an impact on the quality of care for patients. Large scale adoption of Electronic Health Records (EHR) has created the opportunity to proactively identify patients with high risk of hospital readmission, and apply effective interventions to mitigate that risk. To that end, in the past, numerous machine-learning models have been employed to predict the risk of 30-day hospital readmission. However, the need for an accurate and real-time predictive model, suitable for hospital setting applications still exists. Here, using data from more than 300,000 hospital stays in California from Sutter Health's EHR system, we built and tested an artificial neural network (NN) model based on Google's TensorFlow library. Through comparison with other traditional and non-traditional models, we demonstrated that neural networks are great candidates to capture the complexity and interdependency of various data fields in EHRs. LACE, the current industry standard, showed a precision (PPV) of 0.20 in identifying high-risk patients in our database. In contrast, our NN model yielded a PPV of 0.24, which is a 20% improvement over LACE. Additionally, we discussed the predictive power of Social Determinants of Health (SDoH) data, and presented a simple cost analysis to assist hospitalists in implementing helpful and cost-effective post-discharge interventions.
| S-EPMC5510858 | biostudies-other
Project description:BackgroundThere is a growing understanding of the prevalence and impact of psychological disorders on COPD. However, the role of these disorders in early readmission is unclear.MethodsWe analyzed data from 5% fee-for-service Medicare beneficiaries diagnosed with COPD (International Classification of Diseases, Ninth Revision code, 491.xx, 492.xx, 493.xx, and 496.xx) between 2001 and 2011 who were hospitalized with a primary discharge diagnosis of COPD or a primary discharge diagnosis of respiratory failure (518.xx and 799.1) with secondary diagnosis of COPD. We hypothesized that such psychological disorders as depression, anxiety, psychosis, alcohol abuse, and drug abuse are independently associated with an increased risk of 30-day readmission in patients hospitalized for COPD.ResultsBetween 2001 and 2011, 135,498 hospitalizations occurred for COPD in 80,088 fee-for-service Medicare beneficiaries. Of these, 30,218 (22.30%) patients had one or more psychological disorders. In multivariate analyses, odds of 30-day readmission were higher in patients with COPD who had depression (OR, 1.34; 95% CI, 1.29-1.39), anxiety (OR, 1.43; 95% CI, 1.37-1.50), psychosis (OR, 1.18; 95% CI, 1.10-1.27), alcohol abuse (OR, 1.30; 95% CI, 1.15-1.47), and drug abuse (OR, 1.29; 95% CI, 1.11-1.50) compared with those who did not have these disorders. These psychological disorders increased amount of variation in 30-day readmission attributed to patient characteristics by 37%.ConclusionsPsychological disorders like depression, anxiety, psychosis, alcohol abuse, and drug abuse are independently associated with higher all-cause 30-day readmission rates for Medicare beneficiaries with COPD.
| S-EPMC4944783 | biostudies-literature
Project description:Chronic obstructive pulmonary disease (COPD) claimed 3.0 million lives in 2016 and ranked 3rd among the top 10 global causes of death. Moreover, once diagnosed and discharged from the hospital, the 30-day readmission risk in COPD patients is found to be the highest among all chronic diseases. The existing diagnosis methods, such as Global Initiative for Chronic Obstructive Lung Disease (GOLD) 2019, Body-mass index, airflow Obstruction, Dyspnea, and Exercise (BODE) index, modified Medical Research Council (mMRC), COPD assessment test (CAT), 6-minute walking distance, which are adopted currently by physicians cannot predict the potential readmission of COPD patients, especially within the 30 days after discharge from the hospital. In this paper, a statistical model was proposed to predict the readmission risk of COPD patients within 30-days by monitoring their physical activity (PA) in daily living with accelerometer-based wrist-worn wearable devices. This proposed model was based on our previously reported PA models for activity index (AI) and regularity index (RI) and it introduced a new parameter, quality of activity (QoA), which incorporates previously proposed parameters, such as AI and RI, with other activity-based indices to predict the readmission risk. Data were collected from continuous PA monitoring of 16 COPD patients after hospital discharge as test subjects and readmission prediction criteria were proposed, with a 63% sensitivity and a 37.78% positive prediction rate. Compared to other clinical assessment, diagnosis, and prevention methods, the proposed model showed significant improvement in predicting the 30-day readmission risk.
| S-EPMC6982816 | biostudies-literature
Project description:Reduction in 30-day readmission rate is an important quality factor for hospitals as it can reduce the overall cost of care and improve patient post-discharge outcomes. While deep-learning-based studies have shown promising empirical results, several limitations exist in prior models for hospital readmission prediction, such as: (a) only patients with certain conditions are considered, (b) do not leverage data temporality, (c) individual admissions are assumed independent of each other, which ignores patient similarity, (d) limited to single modality or single center data. In this study, we propose a multimodal, spatiotemporal graph neural network (MM-STGNN) for prediction of 30-day all-cause hospital readmission, which fuses in-patient multimodal, longitudinal data and models patient similarity using a graph. Using longitudinal chest radiographs and electronic health records from two independent centers, we show that MM-STGNN achieved an area under the receiver operating characteristic curve (AUROC) of 0.79 on both datasets. Furthermore, MM-STGNN significantly outperformed the current clinical reference standard, LACE+ (AUROC=0.61), on the internal dataset. For subset populations of patients with heart disease, our model significantly outperformed baselines, such as gradient-boosting and Long Short-Term Memory models (e.g., AUROC improved by 3.7 points in patients with heart disease). Qualitative interpretability analysis indicated that while patients' primary diagnoses were not explicitly used to train the model, features crucial for model prediction may reflect patients' diagnoses. Our model could be utilized as an additional clinical decision aid during discharge disposition and triaging high-risk patients for closer post-discharge follow-up for potential preventive measures.
| S-EPMC11073780 | biostudies-literature