Geriatric influenza death (GID) score: a new tool for predicting mortality in older people with influenza in the emergency department

Abstract

Although influenza may cause death in the geriatric population, the best method for predicting mortality in this population is still unclear. We retrospectively recruited older people (≥65 yr) with influenza visiting the emergency department (ED) of a medical center between January 1, 2010, and December 31, 2015. We performed univariate and multivariate logistic regression to identify independent mortality predictors and then developed a prediction score. Four hundred nine older ED patients with a nearly equal sex ratio were recruited. Five independent mortality predictors were identified: severe coma (Glasgow Coma Scale score ≤8), past histories of cancer and coronary artery disease, elevated C-reactive protein levels (>10 mg/dl), and bandemia (>10% band cells). We divided the patients into three mortality risk and disposition groups: (1) low risk (1.1%; 95% confidence interval [CI], 0.5–3.0%); (2) moderate risk (16.7%; 95% CI, 9.3–28.0%); and (3) high risk (40%; 95% CI, 19.8–64.2%). The area under the receiver operating characteristic curve and the Hosmer-Lemeshow goodness of fit of the GID score were 0.86 and 0.578, respectively. The GID score is an efficient and simple tool for predicting mortality in older ED patients with influenza. Further studies are warranted to validate its use.

Utilization of Systemic Inflammatory Response Syndrome Criteria in predicting mortality among geriatric patients with influenza in the emergency department

“…We found that the sensitivity and specificity of SIRS score ≥ 3 were < 80%; however, SIRS ≥ 3 had a notable NPV of 97%, which may be useful in ruling out poor prognosis and mortality among geriatric patients with influenza [20]. Comorbidities such as CAD and cancer may affect mortality in geriatric patients with influenza infection [21]. Infection with influenza may aggravate underlying cardiac disease, deteriorate heart function, and increase the chance of myocardial infarction in patients with a past history of CAD [22].…”

Is there a clinical difference between influenza A and B virus infections in hospitalized patients?

“…The overall in-hospital mortality rate for both groups was 5.6% with a numerically but not statistically significantly higher mortality rate in the influenza A group (8.3% for influenza A vs. 4.7% for influenza B, p = 0.172). Other studies of hospitalized patients described a similar mortality rate (3.7-5.2%) without differences between influenza A and B virus infections [27,28,32]. Of the studies one analyzed community and healthcare-acquired influenza infections and described an in-hospital mortality rate of 366 Is there a clinical difference between influenza A and B virus infections in hospitalized patients?…”

Predicting Outcomes in Older ED Patients with Influenza in Real Time Using a Big Data-Driven and Machine Learning Approach to the Hospital Information System

“…We established a multi-disciplinary team, including emergency physicians, data scientists, information engineers, nurse practitioners, and quality managers for this project (Figure 1). After our literature review, we decided to use the previous study about predicting mortality in older ED patients with in uenza as the main reference [4]. We identi ed all older patients (≥65 years old) with in uenza who visited the ED between We included age, sex, vital signs, and past histories of hypertension (ICD-9: 401-405), diabetes (ICD-9-CM: 250), COPD (ICD-9-CM: 496), CAD (ICD-9-CM: 410-414), stroke (ICD-9: 436-438), malignancy (ICD-9: 140-208), congestive heart failure (CHF, ICD-9-CM: 428), dementia (ICD-9: 290), bedridden, feeding with a nasogastric tube, and nursing home resident, laboratory data including white blood cell count (WBC), bandemia, hemoglobin, platelet, serum creatinine, CRP, procalcitonin, glucose, Na, K, GOT, and GPT for this study.…”

“…We identi ed all older patients (≥65 years old) with in uenza who visited the ED between We included age, sex, vital signs, and past histories of hypertension (ICD-9: 401-405), diabetes (ICD-9-CM: 250), COPD (ICD-9-CM: 496), CAD (ICD-9-CM: 410-414), stroke (ICD-9: 436-438), malignancy (ICD-9: 140-208), congestive heart failure (CHF, ICD-9-CM: 428), dementia (ICD-9: 290), bedridden, feeding with a nasogastric tube, and nursing home resident, laboratory data including white blood cell count (WBC), bandemia, hemoglobin, platelet, serum creatinine, CRP, procalcitonin, glucose, Na, K, GOT, and GPT for this study. We adopted 10 potential predictors proposed in the previous study as the feature variables for the ML [4]: (1) tachypnea (respiratory rate >20/min); (2) severe coma (GCS ≤8); (3) history of hypertension; (4) history of CAD; (5) history of malignancy; (6) bedridden; (7) leukocytosis (WBC >12,000 cells/mm); (8) bandemia (>10% band cells); (9) anemia (hemoglobin <12 mg/dL); and (10) elevated CRP (>10 mg/dL). The patients who did not have a record of subsequent follow-up were excluded.…”