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Majority of 30-Day Readmissions After Intracerebral Hemorrhage Are Related to Infections.

ABSTRACT: Infections are common after intracerebral hemorrhage, but little is known about the risk of serious infection requiring readmission after hospital discharge.To determine if infections are prevalent in patients readmitted within 30 days of discharge, we performed a retrospective cohort study of patients discharged from nonfederal acute care hospitals in California with a primary diagnosis of intracerebral hemorrhage between 2006 and 2010. We excluded patients who died during the index admission, were discharged against medical advice, or were not California residents. Our main outcome was 30-day unplanned readmission with primary infection-related International Classification of Diseases, Ninth Revision, Clinical Modification code.There were 24?540 index intracerebral hemorrhage visits from 2006 to 2010. Unplanned readmissions occurred in 14.5% (n=3550) of index patients. Of 3550 readmissions, 777 (22%) had an infection-related primary diagnosis code. When evaluating primary and all secondary diagnosis codes, infection was associated with 1826 (51%) of readmissions. Other common diagnoses associated with readmission included stroke-related codes (n=840, 23.7%) and aspiration pneumonitis (n=154, 4.3%). The most common infection-related primary diagnosis codes were septicemia (n=420, 11.8%), pneumonia (n=124, 3.5%), urinary tract infection (n=141, 4.0%), and gastrointestinal infection (n=42, 1.2%). Patients with a primary infection-related International Classification of Diseases, Ninth Revision, Clinical Modification code on readmission had higher in-hospital mortality compared with other types of readmission (15.6% versus 8.0%, P<0.001). After controlling for other predictors of mortality, primary infection-related readmissions remained associated with in-hospital mortality (relative risk, 1.7; 95% confidence interval, 1.3-2.2).Infections are associated with a majority of 30-day readmissions after intracerebral hemorrhage and increased mortality. Efforts should be made to reduce infection-related complications after hospital discharge.

SUBMITTER: Lord AS

PROVIDER: S-EPMC4927367 | biostudies-literature | 2016 Jul

REPOSITORIES: biostudies-literature

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Majority of 30-Day Readmissions After Intracerebral Hemorrhage Are Related to Infections.

Lord Aaron S AS Lewis Ariane A Czeisler Barry B Ishida Koto K Torres Jose J Kamel Hooman H Woo Daniel D Elkind Mitchell S V MS Boden-Albala Bernadette B

Stroke 20160614 7

<h4>Background and purpose</h4>Infections are common after intracerebral hemorrhage, but little is known about the risk of serious infection requiring readmission after hospital discharge.<h4>Methods</h4>To determine if infections are prevalent in patients readmitted within 30 days of discharge, we performed a retrospective cohort study of patients discharged from nonfederal acute care hospitals in California with a primary diagnosis of intracerebral hemorrhage between 2006 and 2010. We excluded ...[more]

PMID: 27301933

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Project description:BackgroundIntracerebral hemorrhage (ICH) is a stroke syndrome with an unfavorable prognosis. Currently, there is no comprehensive clinical indicator for mortality prediction of ICH patients. The purpose of our study was to construct and evaluate a nomogram for predicting the 30-day mortality risk of ICH patients.MethodsICH patients were extracted from the MIMIC-III database according to the ICD-9 code and randomly divided into training and verification cohorts. The least absolute shrinkage and selection operator (LASSO) method and multivariate logistic regression were applied to determine independent risk factors. These risk factors were used to construct a nomogram model for predicting the 30-day mortality risk of ICH patients. The nomogram was verified by the area under the receiver operating characteristic curve (AUC), integrated discrimination improvement (IDI), net reclassification improvement (NRI), and decision curve analysis (DCA).ResultsA total of 890 ICH patients were included in the study. Logistic regression analysis revealed that age (OR = 1.05, P < 0.001), Glasgow Coma Scale score (OR = 0.91, P < 0.001), creatinine (OR = 1.30, P < 0.001), white blood cell count (OR = 1.10, P < 0.001), temperature (OR = 1.73, P < 0.001), glucose (OR = 1.01, P < 0.001), urine output (OR = 1.00, P = 0.020), and bleeding volume (OR = 1.02, P < 0.001) were independent risk factors for 30-day mortality of ICH patients. The calibration curve indicated that the nomogram was well calibrated. When predicting the 30-day mortality risk, the nomogram exhibited good discrimination in the training and validation cohorts (C-index: 0.782 and 0.778, respectively). The AUCs were 0.778, 0.733, and 0.728 for the nomogram, Simplified Acute Physiology Score II (SAPSII), and Oxford Acute Severity of Illness Score (OASIS), respectively, in the validation cohort. The IDI and NRI calculations and DCA analysis revealed that the nomogram model had a greater net benefit than the SAPSII and OASIS scoring systems.ConclusionThis study identified independent risk factors for 30-day mortality of ICH patients and constructed a predictive nomogram model, which may help to improve the prognosis of ICH patients.

Project description:BACKGROUND:Readmissions after hospitalization for pneumonia are common, but the few risk-prediction models have poor to modest predictive ability. Data routinely collected in the electronic health record (EHR) may improve prediction. OBJECTIVE:To develop pneumonia-specific readmission risk-prediction models using EHR data from the first day and from the entire hospital stay ("full stay"). DESIGN:Observational cohort study using stepwise-backward selection and cross-validation. SUBJECTS:Consecutive pneumonia hospitalizations from 6 diverse hospitals in north Texas from 2009-2010. MEASURES:All-cause nonelective 30-day readmissions, ascertained from 75 regional hospitals. RESULTS:Of 1463 patients, 13.6% were readmitted. The first-day pneumonia-specific model included sociodemographic factors, prior hospitalizations, thrombocytosis, and a modified pneumonia severity index; the full-stay model included disposition status, vital sign instabilities on discharge, and an updated pneumonia severity index calculated using values from the day of discharge as additional predictors. The full-stay pneumonia-specific model outperformed the first-day model (C statistic 0.731 vs 0.695; P = 0.02; net reclassification index = 0.08). Compared to a validated multi-condition readmission model, the Centers for Medicare and Medicaid Services pneumonia model, and 2 commonly used pneumonia severity of illness scores, the full-stay pneumonia-specific model had better discrimination (C statistic range 0.604-0.681; P < 0.01 for all comparisons), predicted a broader range of risk, and better reclassified individuals by their true risk (net reclassification index range, 0.09-0.18). CONCLUSIONS:EHR data collected from the entire hospitalization can accurately predict readmission risk among patients hospitalized for pneumonia. This approach outperforms a first-day pneumonia-specific model, the Centers for Medicare and Medicaid Services pneumonia model, and 2 commonly used pneumonia severity of illness scores. Journal of Hospital Medicine 2017;12:209-216.

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Majority of 30-Day Readmissions After Intracerebral Hemorrhage Are Related to Infections.

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Majority of 30-Day Readmissions After Intracerebral Hemorrhage Are Related to Infections.

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