International Validity of the HOSPITAL Score to Predict 30-Day Potentially Avoidable Hospital Readmissions

Donzé, Jacques; Williams, Mark; Robinson, Emily K.; Zimlichman, Eyal; Aujesky, Drahomir; Vasilevskis, Eduard E.; Kripalani, Sunil; Metlay, Joshua P.; Wallington, Tamara; Fletcher, Grant; Auerbach, Andrew D.; Schnipper, Jeffrey L.

doi:10.1001/jamainternmed.2015.8462

Cited by 199 publications

(208 citation statements)

References 27 publications

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“…An alternative strategy to identify patients with pneumonia at highest risk for readmission is to use existing multicondition risk prediction models, such as the LACE index (length of stay, acuity of admission, comorbidities, and emergency department visits), HOSPITAL score (hemoglobin level at discharge, discharge from oncology service, sodium level at discharge, procedure during hospital stay, index admission type as urgent or emergent, number of admissions in previous year, and length of stay), or EHR-based models (33)(34)(35). Implementing readmission risk prediction models for every condition may be time-consuming and costly.…”

Section: Systematic Reviewmentioning

confidence: 99%

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

Weinreich¹,

Nguyen²,

Wang³

et al. 2016

Annals ATS

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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.

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Section: Systematic Reviewmentioning

confidence: 99%

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

Weinreich¹,

Nguyen²,

Wang³

et al. 2016

Annals ATS

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“…The authors state that this scoring system identifies risk status before patient discharge and becomes a guideline for essential interventions during transfers between sites of care (32). Recently, the validity of the HOSPITAL scoring system was confirmed with a multi-centered study investigating 117.065 discharges and C-statistics, as the differential power was found to be 0.72 (95% CI, 0.72-0.72) (33).…”

Section: How Can High-risk Patients Be Detected?mentioning

confidence: 99%

Who is High Risk Elderly Patient?

Naharcı¹

2017

Istanbul Med J

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A 77-year-old male patient was brought to emergency services because he had fallen. It was learned from his history that he had been examined by cardiologists and orthopedists many times, and he had consulted a neurologist for gait difficulty approximately one month earlier. He had also fallen four times in the preceding month. He had comorbidities of coronary artery disease, type 2 diabetes mellitus, and lumbar disc herniation. He had undergone total right hip prosthesis replacement 3 years earlier. He was retired and lived with his wife. He had been having difficulty performing his activities of daily living due to gait difficulty for 6 months. The patient and his wife stated that he took 16 different medications. His physical examination revealed his arterial blood pressure while standing to be 86/62 mmHg, and his other vitals were normal. The movements of his right shoulder were restricted after he had fallen. Serious pain and restricted movement (probable effusion), accompanied by mild warmth and tenderness in the left knee, restricted left hip movements, and mild weakness in the left leg were detected. Apart from these, there were no other important findings. Detailed geriatric assessment revealed multiple factors that could lead to falls. These factors were evaluated to be postural hypotension, polypharmacy, degenerative joint disease in the left hip and knee, and spinal stenosis-induced neuropathy. Sample Case 2A 92-year-old female patient was brought to the hospital by her family because she had lost her appetite for 3 days. She had comorbidities of hypertension, congestive heart failure, urinary incontinence (overactive bladder), and depression. She used a walker. It was learned from her history that she had fallen because she had stepped on her urine in the restroom, broke her hip, and become dependent while walking. As a result of examination and analyses, she was hospitalized after having been diagnosed with malnutrition, dehydration, and a urinary tract infection. Because improvement was observed after treatment, the patient was discharged from the hospital on the fifth day of hospitalization. However, she was readmitted due to recurring urinary tract infection one week after being discharged. IntroductionAs in developed and developing countries, the proportion of the elderly population is increasing in Turkey, which is in a period of demographic change. According to the 2015 Who is High Risk Elderly Patient?The acute and post-acute care of the elderly is a process fraught with problems. Elderly patients at high risk are individuals who have to be admitted more frequently to a hospital due to developed complications, who are faced with a constant risk of hospitalization, and who have increased risks of functional decline, nursing home admittance, and death. These adverse outcomes cause discrepancies and problems in the implementation and regulation of treatment and coordination between health entities. Factors increasing these risks vary among these individuals, but existing chronic diseases and ge...

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“…Parameters were adjusted to minimize the sum of loss function and the overfitting control term. The sum term at the t iteration was as follows: (2) where l was the loss function, ŷ i t−1 was the predictive value at the t-1 iteration, and Ω was to control overfitting. was a function of the number of trees and weights of each tree Ω in the algorithm.…”

Section: Model Derivationmentioning

confidence: 99%

“…However, among the 50-70% of patient with a terminal illness who prefer to be cared for and die at home, only about 25% have a home death and more than 50% die in hospital 1 . Nearly a third of Americans who die after 65 years of age will have spent time in an intensive care unit in their final three months of life, and almost a fifth undergo surgery in their last month 2 . Even more, a "disproportionate" amount of health care resources and expenditures are spent on patients who are terminally ill 3 .…”

Section: Introductionmentioning

confidence: 99%

Assessing Statewide All-Cause Future One-Year Mortality: Prospective Study With Implications for Quality of Life, Resource Utilization, and Medical Futility (Preprint)

Yang¹,

Gang²,

Fu³

et al. 2018

Preprint

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1 Background: For many elderly patients, a "disproportionate" amount of health care resources and expenditures is spent during the last year of life, despite the discomfort and reduced quality of life associated with many aggressive medical approaches. However, few prognostic tools have focused on predicting all-cause one-year mortality among elderly patients at a statewide level, an issue that has implications for improving quality of life while distributing scarce resource fairly. Objective: Using data from a statewide elderly population (≥65 years old), we sought to validate prospectively an algorithm to identify patients at risk for dying in the future one year, for the purpose of minimizing decision uncertainty, improving quality of life, and reducing futile treatment. Methods: Analysis was performed using electronic medical records (EMRs) from the Health Information Exchange (HIE) in the state of Maine, which covered records of nearly 95% of the statewide population. The model was developed from 125,896 patients aged at least 65 years who were discharged from any care facility in the HIE network from September 5, 2013 to September 4, 2015. Validation was conducted using 153,199 patients with same inclusion and exclusion criteria from September 5, 2014 to September 4, 2016. Patients were stratified into risk groups. The association between all-cause one-year mortality and risk factors was screened by chi-squared test and manually reviewed by two clinicians. We calculated risk scores for individual patients using a gradient tree based boost algorithm, which measured the probability of mortality within the next 1 year based on the preceding one-year clinical profile. A total of 99 independent risk factors (n=99) for mortality were identified (reported as odds ratios; 95%CIs). Different tumor sites were recognized as driving risk factors, such as ovary (14.42; 2.24-53.04), colon (14.07; 10.08-19.08), stomach (13.64; 3.26-86.57), and bronchus/lung (12.38; 2.91-36.04). Disparities were also found in patients' social determinants like respiratory hazard index (1.24; 0.92-1.40), and unemployment rate (1.18; 0.98-1.24). Among high-risk patients who expired in our dataset, cerebrovascular accident, amputation, and type 1 diabetes were the top 3 diseases in terms of average cost in the last year of life. Conclusions: With statewide EMRs datasets, our study prospectively validated an accurate one-year risk prediction model and stratification for the elderly population (≥65 years old) at risk of mortality. It should be a valuable adjunct for helping patients to make better quality-of-life choices, and alerting care givers to target high-risk elderly for appropriate care and discussions, thus cutting back on futile treatment. Results

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International Validity of the HOSPITAL Score to Predict 30-Day Potentially Avoidable Hospital Readmissions

Cited by 199 publications

References 27 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

Who is High Risk Elderly Patient?

Assessing Statewide All-Cause Future One-Year Mortality: Prospective Study With Implications for Quality of Life, Resource Utilization, and Medical Futility (Preprint)

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