Short-term mortality in older medical emergency patients can be predicted using clinical intuition: A prospective study

Zelis, Noortje; Mauritz, Arisja N.; Kuijpers, Lonne I. J.; Buijs, Jacqueline; Leeuw, Peter W. de; Stassen, Patricia M.

doi:10.1371/journal.pone.0208741

Cited by 18 publications

(21 citation statements)

References 23 publications

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“…Future studies should benefit from combining surprise question and clinical predictors to formulate short-term prognosis of patients dying in medical and surgical wards [44,45].…”

Section: Study Limitsmentioning

confidence: 99%

Quality of dying in hospital general wards: a cross-sectional study about the end-of-life care

Binda

Clari²,

Nicolò

et al. 2021

BMC Palliat Care

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Background In the last decade, access to national palliative care programs have improved, however a large proportion of patients continued to die in hospital, particularly within internal medicine wards. Objectives To describe treatments, symptoms and clinical management of adult patients at the end of their life and explore whether these differ according to expectation of death. Methods Single-centre cross-sectional study performed in the medical and surgical wards of a large tertiary-level university teaching hospital in the north of Italy. Data on nursing interventions and diagnostic procedure in proximity of death were collected after interviewing the nurse and the physician responsible for the patient. Relationship between nursing treatments delivered and patients’ characteristics, quality of dying and nurses’ expectation about death was summarized by means of multiple correspondence analysis (MCA). Results Few treatments were found statistically associated with expectation of death in the 187 patients included. In the last 48 h, routine (70.6%) and biomarkers (41.7%) blood tests were performed, at higher extent on patients whose death was not expected. Many symptoms classified as severe were reported when death was highly expected, except for agitation and respiratory fatigue which were reported when death was moderately expected. A high Norton score and absence of anti-bedsore mattress were associated with unexpected death and poor quality of dying, as summarized by MCA. Quality of dying was perceived as good by nurses when death was moderately and highly expected. Physicians rated more frequently than nurses the quality of dying as good or very good, respectively 78.6 and 57.8%, denoting a fair agreement between the two professionals (k = 0.24, P < 0.001). The palliative care consultant was requested for only two patients. Conclusion Staff in medical and surgical wards still deal inadequately with the needs of dying people. Presence of hospital-based specialist palliative care could lead to improvements in the patients’ quality of life.

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“…Future studies should benefit from combining surprise question and clinical predictors to formulate short-term prognosis of patients dying in medical and surgical wards [44,45].…”

Section: Study Limitsmentioning

confidence: 99%

Quality of dying in hospital general wards: a cross-sectional study about the end-of-life care

Binda

Clari²,

Nicolò

et al. 2021

BMC Palliat Care

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“…The questionnaire contained questions about their concerns, disease perception and self-rated health. Results of this questionnaire, regarding the predictive value of clinical intuition for adverse outcomes, have been published online [ 12 ]. For the current study, we focused on the categorical question: ‘Are you concerned about your (his/her) condition?’ and the open question ‘If you are concerned about your (his/her) condition, what are you concerned about?’.…”

Section: Methodsmentioning

confidence: 99%

“…During the study period 603 patients were included. Detailed information regarding the patient selection was described in our previous article [12]. Questionnaires were missing in 9 patients, so 594 patients were included in the final analysis.…”

Section: Study Population and Characteristicsmentioning

confidence: 99%

“…Since many patients feel inhibited to talk about their feelings to the ED staff [8,10], it is likely that their concerns have not been completely exposed in these studies. Recently, we performed the RISE UP study to find predictors of adverse outcomes in older ED patients [11], and found that the severity of concerns in patients and caregivers is predictive of adverse outcomes [12]. It may, therefore, also be possible that specific concerns are predictive of that particular outcome.…”

Section: Introductionmentioning

confidence: 99%

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Concerns of older patients and their caregivers in the emergency department

et al. 2020

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Background Older emergency department (ED) patients often have complex problems and severe illnesses with a high risk of adverse outcomes. It is likely that these older patients are troubled with concerns, which might reflect their preferences and needs concerning medical care. However, data regarding this topic are lacking. Methods This study is a sub study of a prospective, multicenter, observational cohort study among older medical ED patients (�65 years). Patients or their caregivers were asked about their illness-related concerns during the first stage of the ED visit using a questionnaire. All concerns were categorized into 10 categories, and differences between patients and caregivers, and between age groups were analyzed. Odds Ratios were calculated to determine the association of the concerns for different adverse outcomes. Results Most of the 594 included patients (or their caregivers) were concerned (88%) about some aspects of their illness or their need for medical care. The most often reported concerns were about the severity of disease (43.6%), functional decline (9.4%) and dying (5.6%). Caregivers were more frequently concerned than patients (p<0.001) especially regarding the severity of disease (50.5 vs 39.6%, p = 0.016) and cognitive decline (10.8 vs. 0.3%, p <0.001). We found no difference between age groups. The concern about dying was associated with 30-day mortality (OR 2.89; 95%CI: 1.24-6.70) and the composite endpoint (intensive-or medium care admission, length of hospital stay >7 days, loss of independent living and unplanned readmission within 30 days) (OR 2.32; 95%CI: 1.12-4.82). In addition, unspecified concerns were associated with mortality (OR 1.88; 95%CI: 1.09-3.22).

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“…In addition, clinical judgment of the attending physician in the ED plays an important role in risk stratification. The judgment of physicians was found to be a moderate to good predictor (AUC of 0.68-0.81) of mortality in the ED [12,13].…”

Section: Introductionmentioning

confidence: 93%

A comparison of machine learning models versus clinical evaluation for mortality prediction in patients with sepsis

et al. 2021

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Introduction Patients with sepsis who present to an emergency department (ED) have highly variable underlying disease severity, and can be categorized from low to high risk. Development of a risk stratification tool for these patients is important for appropriate triage and early treatment. The aim of this study was to develop machine learning models predicting 31-day mortality in patients presenting to the ED with sepsis and to compare these to internal medicine physicians and clinical risk scores. Methods A single-center, retrospective cohort study was conducted amongst 1,344 emergency department patients fulfilling sepsis criteria. Laboratory and clinical data that was available in the first two hours of presentation from these patients were randomly partitioned into a development (n = 1,244) and validation dataset (n = 100). Machine learning models were trained and evaluated on the development dataset and compared to internal medicine physicians and risk scores in the independent validation dataset. The primary outcome was 31-day mortality. Results A number of 1,344 patients were included of whom 174 (13.0%) died. Machine learning models trained with laboratory or a combination of laboratory + clinical data achieved an area-under-the ROC curve of 0.82 (95% CI: 0.80–0.84) and 0.84 (95% CI: 0.81–0.87) for predicting 31-day mortality, respectively. In the validation set, models outperformed internal medicine physicians and clinical risk scores in sensitivity (92% vs. 72% vs. 78%;p<0.001,all comparisons) while retaining comparable specificity (78% vs. 74% vs. 72%;p>0.02). The model had higher diagnostic accuracy with an area-under-the-ROC curve of 0.85 (95%CI: 0.78–0.92) compared to abbMEDS (0.63,0.54–0.73), mREMS (0.63,0.54–0.72) and internal medicine physicians (0.74,0.65–0.82). Conclusion Machine learning models outperformed internal medicine physicians and clinical risk scores in predicting 31-day mortality. These models are a promising tool to aid in risk stratification of patients presenting to the ED with sepsis.

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Short-term mortality in older medical emergency patients can be predicted using clinical intuition: A prospective study

Cited by 18 publications

References 23 publications

Quality of dying in hospital general wards: a cross-sectional study about the end-of-life care

Quality of dying in hospital general wards: a cross-sectional study about the end-of-life care

Concerns of older patients and their caregivers in the emergency department

A comparison of machine learning models versus clinical evaluation for mortality prediction in patients with sepsis

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