Predictors of Short-term Rehospitalization Following Discharge of Patients Hospitalized With Community-Acquired Pneumonia

Capelastegui, Alberto; Yandiola, Pedro Pablo España; Quintana, José M.; Bilbao, Amaia; Díez, Rosa; Pascual, Sílvia; Pulido, Esther; Egurrola, Mikel

doi:10.1378/chest.08-2950

Cited by 83 publications

(101 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Of 992 titles identified by our search algorithm, 91 qualified for abstract review, 12 for full-text review, and 7 met our inclusion criteria (Figure 1) (11)(12)(13)(14)(15)(16)(17). Of the seven included studies, 11 unique riskprediction models were tested.…”

Section: Resultsmentioning

confidence: 99%

“…The CMS Pneumonia Administrative Model was the most commonly studied-validated in five separate cohorts (12,14,15). The objective of eight of the models was to identify patients hospitalized for pneumonia at high risk for readmission for potential intervention (11,13,16,17), whereas for three of the models the objective was to estimate hospital-level risk-adjusted 30-day SYSTEMATIC REVIEW readmission rates for the purpose of hospital profiling (12,14,15).…”

Section: Resultsmentioning

confidence: 99%

“…All studies were based in the United States except for Capelastegui and colleagues (11), which was conducted at a single academic medical center in Spain. All studies defined pneumonia as the primary discharge diagnosis using International Classification of Diseases, Ninth Revision codes for any type, except for one prospective study that defined communityacquired pneumonia using a clinical diagnosis of symptoms and imaging (11). The study populations ranged from single academic medical centers to national data (Medicare and Veterans Affairs).…”

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

Predicting the Risk of Readmission in Pneumonia. A Systematic Review of Model Performance

Weinreich¹,

Nguyen²,

Wang³

et al. 2016

Annals ATS

View full text Add to dashboard Cite

Rationale: Predicting which patients are at highest risk for readmission after hospitalization for pneumonia could enable hospitals to proactively reallocate scarce resources to reduce 30-day readmissions.Objectives: To synthesize the available literature on readmission risk prediction models for adults who are hospitalized because of pneumonia and describe their performance. Methods:We systematically searched Ovid MEDLINE, Embase, The Cochrane Library, and Cumulative Index to Nursing and Allied Health Literature databases from inception through July 2015. We included studies of adults discharged with pneumonia that developed or validated a model that predicted hospital readmission. Two independent reviewers abstracted data and assessed the risk of bias.Measurements and Main Results: Of 992 citations reviewed, 7 studies met inclusion criteria, which included 11 unique risk prediction models. All-cause 30-day readmission rates ranged from 11.8 to 20.8% (median, 17.3%). Model discrimination (C statistic) ranged from 0.59 to 0.77 (median, 0.63) with the highest-quality, best-validated model, the Centers for Medicare and Medicaid Services Pneumonia Administrative Model performing modestly (C Statistic of 0.63 in 4 separate multicenter cohorts). The best performing model (C statistic of 0.77) was a single-site study that lacked internal validation. The models had adequate calibration, with patients predicted as high risk for readmission having a higher average observed readmission rate than those predicted to be low risk. None of the studies included pneumonia illness severity scores, and only one included measures of in-hospital clinical trajectory and stability on discharge, robust predictors of readmission. Conclusions:We found a limited number of validated pneumoniaspecific readmission models, and their predictive ability was modest. To improve predictive accuracy, future models should include measures of pneumonia illness severity, hospital complications, and stability on discharge.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Predicting the Risk of Readmission in Pneumonia. A Systematic Review of Model Performance

Weinreich¹,

Nguyen²,

Wang³

et al. 2016

Annals ATS

View full text Add to dashboard Cite

show abstract

“…A recent systematic review 2 of 26 models uncovered: most models perform poorly, including the 3 CMS models (pneumonia, heart failure, and myocardial infarction); only one clinical model successfully met the goals set forth for clinical application and had what is considered acceptable discriminative ability when detecting readmission for heart failure 3 ; the successful addition of social or functional variables offer opportunities for improvement in model performance. 4,5 There are few readmission models centered on pneumonia 30 day readmissions, [6][7][8][9][10][11][12] and only a few reported on discriminative ability. 6,11,12 In this single-center, retrospective analysis our intention was to leverage variables shown to be significant in previously reported predictive models and supplement the CMS medical record model 6 with additional variables shown to be of benefit in other models, in an effort to produce a model to identify before discharge patients at high risk for readmission.…”

Section: Introductionmentioning

confidence: 99%

“…4,5 There are few readmission models centered on pneumonia 30 day readmissions, [6][7][8][9][10][11][12] and only a few reported on discriminative ability. 6,11,12 In this single-center, retrospective analysis our intention was to leverage variables shown to be significant in previously reported predictive models and supplement the CMS medical record model 6 with additional variables shown to be of benefit in other models, in an effort to produce a model to identify before discharge patients at high risk for readmission.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Pneumonia 30-Day Readmissions: A Single-Center Attempt to Increase Model Performance

Mather¹,

Fortunato²,

Ash³

et al. 2013

Respir Care

View full text Add to dashboard Cite

BACKGROUND: Existing models developed to predict 30 days readmissions for pneumonia lack discriminative ability. We attempted to increase model performance with the addition of variables found to be of benefit in other studies. METHODS: From 133,368 admissions to a tertiary-care hospital from January 2009 to March 2012, the study cohort consisted of 956 index admissions for pneumonia, using the Centers for Medicare and Medicaid Services definition. We collected variables previously reported to be associated with 30-day all-cause readmission, including vital signs, comorbidities, laboratory values, demographics, socioeconomic indicators, and indicators of hospital utilization. Separate logistic regression models were developed to identify the predictors of all-cause hospital readmission 30 days after discharge from the index pneumonia admission for pneumonia-related readmissions, and for pneumonia-unrelated readmissions. RESULTS: Of the 965 index admissions for pneumonia, 148 (15.5%) subjects were readmitted within 30 days. The variables in the multivariate-model that were significantly associated with 30-day all-cause readmission were male sex (odds ratio 1.59, 95% CI 1.03-2.45), 3 or more previous admissions (odds ratio 1.84, 95% CI 1.22-2.78), chronic lung disease (odds ratio 1.63, 95% CI 1.07-2.48), cancer (odds ratio 2.18, 95% CI 1.24 -3.84), median income < $43,000 (odds ratio 1.82, 95% CI 1.18 -2.81), history of anxiety or depression (odds ratio 1.62, 95% CI 1.04 -2.52), and hematocrit < 30% (odds ratio 1.86, 95% CI 1.07-3.22). The model performance, as measured by the C statistic, was 0.71 (0.66 -0.75), with minimal optimism according to bootstrap re-sampling (optimism corrected C statistic 0.67). CONCLUSIONS: The addition of socioeconomic status and healthcare utilization variables significantly improved model performance, compared to the model using only the Centers for Medicare and Medicaid Services variables.

show abstract

Incidence of Avoidable 30-Day Readmissions Following Hospitalization for Community-Acquired Pneumonia in France

et al. 2022

View full text Add to dashboard Cite

IMPORTANCE Rates of 30-day readmissions following hospitalization for pneumonia are used to publicly report on hospital performance and to set financial penalties for the worst-performing hospitals. However, the rate of avoidable readmission following hospitalization for pneumonia is undefined. OBJECTIVETo assess how often 30-day readmissions following hospitalization for communityacquired pneumonia (CAP) are avoidable. DESIGN, SETTING, AND PARTICIPANTS This cohort study analyzed the results of an independent review of readmissions following hospitalization for CAP within 30 days among patients discharged from 2 large hospitals in France in 2014. Structured clinical records including clinical information (ie, baseline characteristics, physical examination, laboratory findings, x-ray or computed tomography scan findings, discharge plan, and treatments) for both index and readmission stays were independently reviewed by 4 certified board physicians. All consecutive adult patients hospitalized in 2014 with a diagnosis of CAP in our 2 eligible hospitals were eligible. All analyses presented were performed in March 2021. MAIN OUTCOMES AND MEASURESAvoidable readmission within 30 days of discharge from index hospitalization. The likelihood that a readmission was avoidable was quantified using latent class analysis based on the independent reviews. A readmission was considered avoidable if Bayes posterior probability exceeded 50%. RESULTSThe total analytical sample consisted of 1150 index hospital stays with a diagnosis of CAP, which included 651 (56.6%) male patients. The median (IQR) age for all patients was 77.8 (IQR, 62.7-86.4) years. Out of the 1150 index hospital stays, 98 patients (8.5%) died in hospital, and 108 (9.4%) unplanned readmissions were found. Overall, 15 readmissions had a posterior probability of avoidability exceeding 0.50 (13.9% of the 108 unplanned readmissions; 95% CI, 8.0%-21.9%). The median (IQR) delay between the hospital discharge index and readmission was considerably shorter when readmission was deemed avoidable (4 [6-21] days vs 12 [2-18] days; P = .02). CONCLUSIONS AND RELEVANCEOnly a small number of readmissions following hospitalization for CAP were deemed avoidable, comprising less than 10% of all readmissions. Shorter time interval between hospitalization discharge and readmission was associated with avoidability.

show abstract

Predictors of Short-term Rehospitalization Following Discharge of Patients Hospitalized With Community-Acquired Pneumonia

Cited by 83 publications

References 20 publications

Predicting the Risk of Readmission in Pneumonia. A Systematic Review of Model Performance

Predicting the Risk of Readmission in Pneumonia. A Systematic Review of Model Performance

Prediction of Pneumonia 30-Day Readmissions: A Single-Center Attempt to Increase Model Performance

Incidence of Avoidable 30-Day Readmissions Following Hospitalization for Community-Acquired Pneumonia in France

Contact Info

Product

Resources

About