Mortality in hospitalised older patients: the WHALES short-term predictive score

Steinmeyer, Zara; Piau, Antoine; Thomazeau, Joséphine; Kai, Samantha Huo Yung; Nourhashémi, Fati

doi:10.1136/bmjspcare-2021-003258

Cited by 3 publications

(4 citation statements)

References 40 publications

(38 reference statements)

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“… 14 , 31 A discharge diagnosis of pneumonia and a self-reported feeling of physical fatigue have also previously been shown to be independent prognostic factors of death in older patients. 13 , 33 , 49 Previous studies also found a correlation between mortality and routine laboratory tests from hospitalization, such as C-reactive protein, creatinine, sodium, hemoglobin, and glucose. 15 , 50–52 We have not been able to find studies that have examined eGFR, bilirubin, or alkaline phosphatase as prognostic factors of mortality, though these were all high-ranking variables in our model.…”

Section: Discussionmentioning

confidence: 94%

“…The level 4 model achieved good discrimination (AUROC 0.860) and was comparable to other models that predict mortality in older patients, though we have not been able to find other models that focus on early discharge. [30][31][32][33] However, besides improving predictive performance, introducing comorbidities and number of prescription medications (level 4) makes the model more labor-intensive for the physician, as this information would often require the manual tallying of the patient's comorbidities and medications. The level 5 model performed comparably to the level 4 model, but with slightly higher specificity and lower sensitivity.…”

Section: Dovepressmentioning

confidence: 99%

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Predicting Short-Term Mortality in Older Patients Discharged from Acute Hospitalizations Lasting Less Than 24 Hours

Heltø,

Rosager,

Aasbrenn

et al. 2023

CLEP

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Purpose Over coming decades, a rise in the number of short, acute hospitalizations of older people is to be expected. To help physicians identify high-risk patients prior to discharge, we aimed to develop a model capable of predicting the risk of 30-day mortality for older patients discharged from short, acute hospitalizations and to examine how model performance changed with an increasing amount of information. Methods This registry-based study included acute hospitalizations in Denmark for 2016–2018 lasting ≤24 hours where patients were permanent residents, ≥65 years old, and discharged alive. Utilizing many different predictor variables, we developed random forest models with an increasing amount of information, compared their performance, and examined important variables. Results We included 107,132 patients with a median age of 75 years. Of these, 3.3% (n=3575) died within 30 days of discharge. Model performance improved especially with the addition of laboratory results and information on prior acute admissions (AUROC 0.835), and again with comorbidities and number of prescription drugs (AUROC 0.860). Model performance did not improve with the addition of sociodemographic variables (AUROC 0.861), apart from age and sex. Important variables included age, dementia, number of prescription drugs, C-reactive protein, and eGFR. Conclusion The best model accurately estimated the risk of short-term mortality for older patients following short, acute hospitalizations. Trained on a large and heterogeneous dataset, the model is applicable to most acute clinical settings and could be a useful tool for physicians prior to discharge.

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Section: Discussionmentioning

confidence: 94%

Section: Dovepressmentioning

confidence: 99%

Predicting Short-Term Mortality in Older Patients Discharged from Acute Hospitalizations Lasting Less Than 24 Hours

Heltø,

Rosager,

Aasbrenn

et al. 2023

CLEP

View full text Add to dashboard Cite

show abstract

“…The Hosmer-Lemeshow goodness-of-t test was used to assess the calibration of the constructed predictive models. If the obtained statistic is smaller, the corresponding P value is larger, which means that the calibration degree of the prediction model is higher, the prediction value and the actual observation value have better consistency, and the tting effect of the model is better [41][42] .…”

Section: Model Evaluationmentioning

confidence: 99%

Risk prediction of gestational diabetes mellitus with four machine learning models

Yue

Pan

zhang

et al. 2023

Preprint

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Purpose To construct and compare machine learning models for predicting the risk of gestational diabetes mellitus (GDM). Method The clinical data of 2048 pregnant women who gave birth at Shunde Women’s and Children’s Hospital of Guangdong Medical University between June 2019 and June 2021 were retrospectively collected. Logistic regression, backpropagation neural networks, random forests, and support vector machines were constructed with the R studio and Python software programs. The logistic regression and random forest models were used to identify significant influencing factors. The area under the receiver operating characteristic curve (AUC) was used to evaluate the predictive performance and discriminative ability of the models, and the Hosmer-Lemeshow test was used to determine goodness of fit. Results Age, glycated hemoglobin, fasting blood glucose, white blood cell count, hemoglobin, and activated partial prothrombin time were identified as significant factors associated with GDM. The random forest model had the best prediction effect (accuracy, 78.07%; Youden index, 1.56). In all four models, AUC was greater than 78%. The Hosmer–Lemeshow fit test showed that all four models were a good fit. Conclusion It was concluded that age, GHB, FBG, WBC, HB, and APTT are the more important related influencing factors or early predictors of gestational diabetes. Among the tested models, random forest was the best one for predicting the risk of GDM in early pregnancy.

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“…Nowadays, thirty percent of hospital admissions for any cause belong to people 65 years and older. This takes relevancy since older patients have more prevalence of frailty, comorbidity, polypharmacy, and other geriatrics syndromes that worsen functional and mortality prognosis, have longer hospital length stays and increased admissions to the intensive care unit (3). According to Steinmeier and collaborators, intrahospital mortality can reach 17.1%, meanwhile this percentage increases to 33.7% in the intensive care unit (4).…”

Section: Introductionmentioning

confidence: 99%

Geriatric Consultation of Frail Patients in Internal Medicine Service and its association with hospital mortality, hospital stay and Intensive Care Unit admission. An observational study.

Contreras,

Torres,

Cortes

et al. 2023

Preprint

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Objective: The main objective is to describe the association between monodisciplinary management led by the attending physician without geriatric intervention and the involvement of geriatrics as consultants for frail patients over 65 years old admitted to the internal medicine service. This assessment is based on patient outcomes, inpatient length of stay, percentage of admissions to intensive care unit, and in-hospital mortality. Methods and Analysis: A prospective observational study was conducted in which all fragile patients over 65 years old admitted to internal medicine ward from March to November 2022 in two hospitals in northern Mexico were included. The outcomes were compared between the group of patients who received routine monodisciplinary management by their attending physician during hospitalization without the participation of a geriatrician, and those who had collaborative management with geriatrics as consultant. No interventions or suggestions were made to any of the groups assessed. Frailty stratification was performed on patients with the FRAIL scale. Results: A total of 111 patients were included with a mean age of 80.6 ± 7.9 years. Out of the total patients, 60 (54.1%) received management from their treating physician along with a geriatrician as a consultant, and the remaining 51 only received monodisciplinary management from their treating physician. There was an association between monodisciplinary management and longer in-hospital stay, compared to the patients who received medical management along with Geriatrics (13.4 vs 5.8 days, P=<0.0001). 29% had a prolonged in-hospital stay, defined as longer than 9 days, and 84.8% of them had monodisciplinary management without Geriatrics consultation (84.8% vs 15.2%, P=<0.005). Of the patients under study, 17.1% died during hospitalization. The mortality in patients consulted by geriatrics was lower, however this association was not statistically significant (47.4% vs 52.6%, P=0.521). Conclusion: The support of the geriatric physician in the in-hospital medical management of the frail elderly patient is significantly associated with a reduction in the length of in-hospital stay and rate of critical care unit admissions. It was not significantly associated with lower mortality; however this could be explained by the inherent nature of frailty as an unfavorable prognostic factor.

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Mortality in hospitalised older patients: the WHALES short-term predictive score

Cited by 3 publications

References 40 publications

Predicting Short-Term Mortality in Older Patients Discharged from Acute Hospitalizations Lasting Less Than 24 Hours

Predicting Short-Term Mortality in Older Patients Discharged from Acute Hospitalizations Lasting Less Than 24 Hours

Risk prediction of gestational diabetes mellitus with four machine learning models

Geriatric Consultation of Frail Patients in Internal Medicine Service and its association with hospital mortality, hospital stay and Intensive Care Unit admission. An observational study.

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