Comparison of a Frailty Risk Score and Comorbidity Indices for Hospital Readmission Using Electronic Health Record Data

Changes in frailty among patients hospitalized for spine pathologies during the COVID-19 pandemic in Germany-a nationwide observational study

PURPOSE

In spine care, frailty is associated with poor outcomes. The aim of this study was to describe changes in frailty in spine care during the coronavirus disease 2019 (COVID-19) pandemic and their relation to surgical management and outcomes.

METHODS

Patients hospitalized for spine pathologies between January 1, 2019, and May 17, 2022, within a nationwide network of 76 hospitals in Germany were retrospectively included. Patient frailty, types of surgery, and in-hospital mortality rates were compared between pandemic and pre-pandemic periods.

RESULTS

Of the 223,418 included patients with spine pathologies, 151,766 were admitted during the pandemic and 71,652 during corresponding pre-pandemic periods in 2019. During the pandemic, the proportion of high-frailty patients increased from a range of 5.1-6.1% to 6.5-8.8% (p < 0.01), while the proportion of low frailty patients decreased from a range of 70.5-71.4% to 65.5-70.1% (p < 0.01). In most phases of the pandemic, the Elixhauser comorbidity index (ECI) showed larger increases among high compared to low frailty patients (by 0.2-1.8 vs. 0.2-0.8 [p < 0.01]). Changes in rates of spine surgery were associated with frailty, most clearly in rates of spine fusion, showing consistent increases among low frailty patients (by 2.2-2.5%) versus decreases (by 0.3-0.8%) among high-frailty patients (p < 0.02). Changes in rates of in-hospital mortality were not associated with frailty.

CONCLUSIONS

During the COVID-19 pandemic, the proportion of high-frailty patients increased among those hospitalized for spine pathologies in Germany. Low frailty was associated with a rise in rates of spine surgery and high frailty with comparably larger increases in rates of comorbidities.

Using EHR Data to Identify Patient Frailty and Risk for ICU Transfer

The predictive properties of four definitions of a frailty risk score (FRS) constructed using combinations of nursing flowsheet data, laboratory tests, and ICD-10 codes were examined for time to first intensive care unit (ICU) transfer in medical-surgical inpatients ≥50 years of age. Cox regression modeled time to first ICU transfer and Schemper-Henderson explained variance summarized predictive accuracy of FRS combinations. Modeling by age group and controlling for sex, all FRS measures significantly predicted time to first ICU transfer. Further multivariable modeling controlling for clinical characteristics substantially improved predictive accuracy. The effect of frailty on time to first ICU transfer depended on age, with highest risk in 50 to <60 years and ≥80 years age groups. Frailty prevalence ranged from 25.1% to 56.4%. Findings indicate that FRS-based frailty is a risk factor for time to first ICU transfer and should be considered in assessment and care-planning to address frailty in high-risk patients.Frailty prevalence was highest med-surg pts 60 to <70 years (56%); highest risk for time to first ICU transfer was in younger (50 to <60 years) and older (≥80 years) groups.

Salutogenic Model-Based Frailty Prevention Program for Pre-Frail Women Aged 55 Years and Over (SAFRAPP): A Study Protocol for a Randomized Controlled Trial

Background: Frailty is a geriatric syndrome which is more higher among women. Limited evidence suggests a model-based intervention for preventing worsening frailty for women. Purpose: This protocol describes a single-blinded, two-armed randomized controlled study purposing to examine the effectiveness of Salutogenic Model-Based Frailty Prevention Program (SAFRAPP) for pre-frail women. Methods: Eighty-four eligible participants from vocational institutions of a municipality in Turkey is randomly allocated to either the SAFRAPP intervention or the control group. The SAFRAPP is a 6-week online nurse-led intervention program comprising of laughter yoga, health education and case management. The intervention is rooted in the Salutogenic Model, which focuses on strengthening individuals' coping capacity to deal with stressors. The primary outcomes are the frailty and sence of coherence scores and the secondary outcomes are the well-being, quality of life and fear of fall scores, and number of falls and emergency admissions in the past three months. The study data for intervention and control group is obtained at four times: At baseline and at the 3-month, 6-month and 9-month follow-ups. Results: The protocol was registered at ClinicalTrials.gov (identifier number NCT04787432, registration date: 08/03/2021). Eligibility, baseline measurements, randomization, and intervention are completed. The follow-ups are ongoing. Implications for Practice: There is unsufficient evidence regarding the effectiveness of a model-based health promotion interventions for prevention of frailty. The SAFRAPP will provide evidence on prevention of frailty and improving sense of coherence of pre-frail women.

Machine learning for predicting readmission risk among the frail: Explainable AI for healthcare

Healthcare costs due to unplanned readmissions are high and negatively affect health and wellness of patients. Hospital readmission is an undesirable outcome for elderly patients. Here, we present readmission risk prediction using five machine learning approaches for predicting 30-day unplanned readmission for elderly patients (age ≥ 50 years). We use a comprehensive and curated set of variables that include frailty, comorbidities, high-risk medications, demographics, hospital, and insurance utilization to build these models. We conduct a large-scale study with electronic health record (her) data with over 145,000 observations from 76,000 patients. Findings indicate that the category boost (CatBoost) model outperforms other models with a mean area under the curve (AUC) of 0.79. We find that prior readmissions, discharge to a rehabilitation facility, length of stay, comorbidities, and frailty indicators were all strong predictors of 30-day readmission. We present in-depth insights using Shapley additive explanations (SHAP), the state of the art in machine learning explainability.

Frailty and In-Hospital Mortality Risk Using EHR Nursing Data

PurposeThe purpose of this study was to evaluate four definitions of a Frailty Risk Score (FRS) derived from EHR data that includes combinations of biopsychosocial risk factors using nursing flowsheet data or International Classification of Disease, 10th revision (ICD-10) codes and blood biomarkers and its predictive properties for in-hospital mortality in adults ≥50 years admitted to medical-surgical units. Methods In this retrospective observational study and secondary analysis of an EHR dataset, survival analysis and Cox regression models were performed with sociodemographic and clinical covariates. Integrated area under the ROC curve (iAUC) across follow-up time based on Cox modeling was estimated. Results The 46,645 patients averaged 1.5 hospitalizations (SD = 1.1) over the study period and 63.3% were emergent admissions. The average age was 70.4 years (SD = 11.4), 55.3% were female, 73.0% were non-Hispanic White (73.0%), mean comorbidity score was 3.9 (SD = 2.9), 80.5% were taking 1.5 high risk medications, and 42% recorded polypharmacy. The best performing FRS-NF-26-LABS included nursing flowsheet data and blood biomarkers (Adj. HR = 1.30, 95% CI [1.28, 1.33]), with good accuracy (iAUC = .794); the reduced model with age, sex, and FRS only demonstrated similar accuracy. The poorest performance was the ICD-10 code-based FRS. Conclusion The FRS captures information about the patient that increases risk for in-hospital mortality not accounted for by other factors. Identification of frailty enables providers to enhance various aspects of care, including increased monitoring, applying more intensive, individualized resources, and initiating more informed discussions about treatments and discharge planning.

Frailty assessment using routine clinical data: An integrative review

BACKGROUND

Frailty is a common but complex problem in older adults. Frailty assessment using routine clinical data has been suggested as a pragmatic approach based on electronic health records from primary care center or hospital settings.

PURPOSE

We aimed to explore the tools and outcome variables used in the published studies on frailty assessment using routine clinical data.

METHODS

An integrative literature review was conducted using the method of Whittemore and Knafl. A literature search was conducted in PubMed, EMBASE, and CINAHL from January 2010 to October 2021.

RESULTS

A total of 45 studies and thirteen frailty assessment tools were analyzed. The assessment items were generally biased toward frailty's risk factors rather than the mechanisms or phenotypes of frailty. Similar to using conventional tools, routine clinical data-based frailty was associated with adverse health outcomes.

CONCLUSIONS

Frailty assessment based on routine clinical data could efficiently evaluate frailty using electronic health records from primary care centers or hospitals. However, they need refinement to consider the risk factors, mechanisms, and frailty phenotypes.

Hospital Readmission Outcomes by Frailty Risk in Adults in Behavioral Health Acute Care

The purpose of the current retrospective study was to determine whether frailty is predictive of 30-day readmission in adults aged ≥50 years who were admitted with a psychiatric diagnosis to a behavioral health hospital from 2013 to 2017. A total of 1,063 patients were included. A 26-item frailty risk score (FRS-26-ICD) was constructed from electronic health record (EHR) data. There were 114 readmissions. Cox regression modeling for demographic characteristics, emergent admission, comorbidity, and FRS-26-ICD determined prediction of time to readmission was modest (incremental area under the receiver operating characteristic curve = 0.671). The FRS-26-ICD was a significant predictor of readmission alone and in models with demographics and emergent admission; however, only the Elixhauser Comorbidity Index was significantly related to hazard of readmission adjusting for other factors (adjusted hazard ratio = 1.26, 95% confidence interval [1.17, 1.37]; p < 0.001), whereas FRS-26-ICD became non-significant. Frailty is a relevant syndrome in behavioral health that should be further studied in risk prediction and incorporated into care planning to prevent hospital readmissions. [Journal of Psychosocial Nursing and Mental Health Services, xx(x), xx-xx.].