The prognostic value of the Barthel Index for mortality in patients with COVID-19: A cross-sectional study

The Correlation Between Medication Self-Management with Rational Medication Use Self-Efficacy and Medication Literacy in Patients with Stroke

Objective

To investigate medication self-management in patients with stroke and its relationship with general demographics, self-efficacy and medication literacy.

Methods

This was a cross-sectional study. Patients with stroke who received treatment in Jiangnan University Affiliated Hospital between July 2023 and January 2024 were selected as the study participants. The General Characteristics Questionnaire, the Self-Efficacy for Appropriate Medication Use Scale (SEAMS), the Chinese version of the Drug Literacy Scale and the Self-Administration of Medication tool were used to investigate patients with stroke and to analyse the factors influencing the self-management of their medication.

Results

A total of 210 patients were included in this study. The average score of medication self-management was 66.71 (standard deviation = 9.55), and SEAMS and medication literacy scores were positively correlated with the total score of medication self-management behaviour. Furthermore, we found that the Barthel index (BI), SEAMS and medication literacy scores were the main predictors of medication self-management behaviour (R 2 = 0.790, p < 0.001).

Conclusion

This study found that patients with stroke with a lower BI and higher SEAMS or medication literacy scores also had higher levels of medication self-management. The factors discussed in this study may help develop individualised interventions in medication self-management for patients with stroke.

Dependence in Activities of Daily Living as a Predictor of In-Hospital Mortality During COVID-19 in Older Individuals

Activities of Daily Living (ADL) are fundamental tasks for individuals to manage their basic needs. Our study aims to examine ADL at admission (adADL) and the Pre-COVID-19 to Admission ADL Difference (ADL-change) as potential predictors of in-hospital mortality. This is a retrospective analysis of clinical data (including the Katz index for ADL) from 141 older patients aged at least 65 years hospitalized in a COVID-19-dedicated unit (not requiring ICU) from September 2021 until January 2022 in Poznań, Poland. Thirty patients (21.3% of all) died during hospitalization. Non-survivors were older than survivors, exhibited lower oxygen saturation, more severe inflammation, higher D-dimer concentrations, and were more commonly prescribed antibiotics. The AUC for in-hospital mortality was for adADL: 0.7417 (95% CI: 0.6478-0.8357; p < 0.0001) and for ADL-change: 0.6869 (95% CI: 0.579-0.7928; p = 0.0018). The corresponding cut-offs were 0 for adADL and 3 for ADL-change. Cox proportional hazard models yielded hazard ratios of 3.57 (95% CI 1.57-8.10; p = 0.0024) for adADL and 3.78 (95% CI 1.49-9.54; p = 0.005) for ADL-change. ADL assessment offers valuable insights into in-hospital mortality among older COVID-19 patients. Monitoring ADL in these patients indicates high-risk individuals for in-hospital death. Integrating ADL into routine clinical practice might enhance care for older patients.

Reduced functional independence and multimorbidity increases the risk of severe infection among older patients with Omicron: a multicenter retrospective cohort study

BACKGROUND

Multimorbidity and physical function in older adults have been identified as associated with coronavirus disease 2019 (COVID-19) outcomes. This study aimed to investigate whether multimorbidity affects the association of impaired functional independence (FI) with critical COVID-19 among older inpatients during the peak of Omicron infection in China.

METHODS

This is a multicentre, retrospective cohort study in northeastern China. Patients aged ≥ 60 years, who were diagnosed with COVID-19 at the time of admission or during hospitalisation. The Barthel index was used to assess FI. Patients were classified into independent, mildly dependent, moderately dependent, and severely dependent groups. Disease severity was classified as critical, severe, and non-severe and combined into severe or critical and non-severe. Binary logistic regression analysis was used to investigate any correlation between FI and disease severity. Patients were further stratified by presence or absence of multimorbidity.

FINDINGS

In this study, of 1598 patients, 530 (33.17%) developed severe or critical infections during the entire hospital stay. Patients with severe dependency had 7.39 times (95% CI: [4.60, 12.15]) higher risk of serious or critical infections than those without dependency. An interaction was noted between reduced FI and multimorbidity (p for interaction < 0.001). Compared to non-multimorbid patients (OR = 3.71, 95% CI: [1.58, 9.16]), multimorbid patients (OR = 10.04, 95% CI: [5.63, 18.57]) had a more pronounced risk of severe or critical infection.

CONCLUSIONS

Our results provide further scientific evidence on the association between FI, multimorbidity, and disease severity in older COVID-19 patients, contributing to future health decision-making for COVID-19 and other infectious diseases.

The relationship between activities of daily living and speech impediments based on evidence from statistical and machine learning analyses

Introduction

Speech impediments (SIs) are increasingly prevalent among middle-aged and older adults, raising concerns within public health. Early detection of potential SI in this demographic is critical. This study investigates the potential of Activities of Daily Living (ADL) as a predictive marker for SI, utilizing data from the 2018 China Health and Retirement Longitudinal Study (CHARLS), which includes 10,136 individuals aged 45 and above. The Barthel Index (BI) was used to assess ADL, and the correlation between ADL and SI was examined through statistical analyses. Machine learning algorithms (Support Vector Machine, Decision Tree, and Logistic Regression) were employed to validate the findings and elucidate the underlying relationship between ADL and SI.

Background

SI poses significant challenges to the health and quality of life of middle-aged and older adults, increasing the demands on community-based and home care services. In the context of global aging, it is crucial to investigate the factors contributing to SI. While the role of ADL as a potential biomarker for SI remains unclear, this study aims to provide new evidence supporting ADL as an early predictor of SI through statistical analysis and machine learning validation.

Methods

Data were derived from the 2018 CHARLS national baseline survey, comprising 10,136 participants aged 45 and above. ADL was evaluated using the BI, and SI was assessed based on the CHARLS records of "Speech impediments." Statistical analyses, including independent sample t-tests, chi-square tests, Pearson and Spearman correlation tests, and hierarchical multiple linear regression, were conducted using SPSS 25.0. Machine learning algorithms, specifically Support Vector Machine (SVM), Decision Tree (DT), and Logistic Regression (LR), were implemented in Python 3.10.2.

Results

Analysis of demographic characteristics revealed that the average BI score in the "With Speech impediments" group was 49.46, significantly lower than the average score of 85.11 in the "Without Speech impediments" group. Pearson correlation analysis indicated a significant negative correlation between ADL and SI (r = -0.205, p < 0.001). Hierarchical multiple linear regression confirmed the robustness of this negative correlation across three models (B = -0.001, β = -0.168, t = -16.16, 95% CI = -0.001 to -0.001, p = 0.000). Machine learning algorithms validated the statistical findings, confirming the predictive accuracy of ADL for SI, with the area under the curve (AUC) scores of SVM-AUC = 0.648, DT-AUC = 0.931, and LR-AUC = 0.666. The inclusion of BI in the models improved the overall predictive performance, highlighting its positive impact on SI prediction.

Conclusion

The study employed various statistical methodologies to demonstrate a significant negative correlation between ADL and SI, a finding further corroborated by machine learning algorithms. Impairment in ADL increases the likelihood of SI occurrence, underscoring the importance of maintaining ADL in middle-aged and older populations to mitigate the risk of SI.

Association between serum albumin and severe impairment of activities of daily living in patients with stroke: a cross-sectional study

Purpose

The relationship between serum albumin levels and severe limitations in ADLs among stroke patients remains unclear. Specifically, the dose-response relationship between the two needs further exploration. This study aims to provide further results.

Materials and methods

This study examined cross-sectional data from patients aged 18 years or older with a diagnosis of stroke confirmed by cranial CT or MRI within 24 h of admission, gathered from January 2020 to August 2022. Data included serum albumin levels, Barthel Index scores recorded after admission, and other essential variables.

Results

The study comprised 2,393 stroke patients. After adjusting for confounding factors, the multivariate analysis revealed a 7% decrease in severe impairment of ADL after stroke for every unit (g/L) increase in serum albumin levels. Compared with individuals with lower serum albumin levels (Q1: ≤ 37.4 g/L), the adjusted odds ratios (OR) for severe of ADL impairment among stroke patients in Q2 (37.4-40.21 g/L), Q3 (40.21-42.80 g/L), and Q4 (≥42.8 g/L) were 0.68 (95% CI: 0.4-1.15, p = 0.148), 0.55 (95% CI: 0.32-0.97, p = 0.04), and 0.64 (95% CI: 0.37-1.15, p = 0.139), respectively. The relationship between serum albumin and severe impairment of ADLs in stroke patients showed an L-shaped curve (non-linear, p = 0.002), with an inflection point at 38.0 g/L. The OR for significant impairment of ADLs was 0.680 (95% CI: 0.568-0.814, p < 0.001) in participants with serum albumin levels <38.0 g/L. However, when serum albumin levels were greater than or equal to 38.0 g/L, the severe impairment of ADLs no longer decreased with rising serum albumin levels.

Conclusion

In summary, an L-shaped connection with an approximate inflection point of 38.0 g/L was found between blood albumin levels and significant ADL impairment in stroke patients. The results of this study suggest that increasing serum albumin levels can significantly help improve the severity of ADL impairment in stroke patients, particularly those with serum albumin levels below 38.0 g/L.

A New Hospital-At-Home Model for Integrated Geriatric Care: Data from a Preliminary Italian Experience

OBJECTIVE

Hospital-at-home (HaH) has emerged as an alternative to conventional in-hospital care in older adults, possibly reducing hospital admissions and related complications. This study aimed to describe the characteristics and outcomes of patients referred to "Gruppo di Intervento Rapido Ospedale-Territorio" (GIROT), a HaH service based on comprehensive geriatric assessment, developed in Florence, Italy, during the postpandemic period.

DESIGN

Retrospective longitudinal study.

SETTING AND PARTICIPANTS

GIROT provided home-based care to patients with acute or exacerbated chronic diseases and a high risk of hospital-related complications (ie, patients with moderate-to-severe disability and/or dementia), referred from primary care, emergency departments, or in-hospital units.

METHODS

All-cause mortality and hospitalization rates were assessed at 1, 3, and 6 months, and predictors of 6-month mortality were investigated.

RESULTS

Among 391 patients (mean age, 88.4 years; 62.4% female) referred from emergency departments (58.6%), primary care (27.9%), and acute medical units (13.6%), the main diagnoses were respiratory failure (28.4%), acute heart failure (25.3%), and delirium (13.6%). Patients referred from primary care were older and showed a higher prevalence of severe disability and hypomobility. After 1, 3, and 6 months, mortality rates were 34.5%, 45.6%, and 53.8%, and hospitalization rates 7.2%, 21.5%, and 37.9%, respectively. Predictors of 6-month mortality included age [odds ratio (OR), 1.039], severe disability (OR, 3.446), impossible/assisted walking (OR, 4.450) and referral from primary care (OR, 2.066). High global satisfaction with the service was reported.

CONCLUSIONS AND IMPLICATIONS

The GIROT model may help expanding acute health care capacity for older adults at high risk of hospital-related complications. Customized care plans are needed in patients with severe disability/hypomobility, considering also simultaneous palliative care.

Electronic health records reveal that COVID-19 impacted health resources and survival of Basque population

BACKGROUND

The COVID-19 pandemic impacted worldwide. The Basque Country was one of the regions in Spain most affected by the virus.

METHODS

In this retrospective study, we took advantage of the Basque Health Service electronic health records data lake of over 20,000 deceased individuals, including 5000 positives for COVID-19, between 2020 and 2022 in Gipuzkoa (Basque Country, Spain).

RESULTS

Comparison between COVID-19-positive and negative individuals' showed that the prevalence of infections was higher inside nursing homes and COVID-19 promoted a significant rise in hospitalizations, emergency entrances, and ICU admissions. No differences were observed between genders in terms of infections or survival but were detected in health resources and vaccination showed a strong protective effect against the disease.

CONCLUSIONS

Our results provided a complete characterization of the impact of COVID-19 on the Basque population, which expands the knowledge of the pandemic on older individuals and the health system. Our study also highlights the benefit of the use of Electronic Health Records in studying human diseases.

Prediction of COVID-19 in-hospital mortality in older patients using artificial intelligence: a multicenter study

Background

Once the pandemic ended, SARS-CoV-2 became endemic, with flare-up phases. COVID-19 disease can still have a significant clinical impact, especially in older patients with multimorbidity and frailty.

Objective

This study aims at evaluating the main characteristics associated to in-hospital mortality among data routinely collected upon admission to identify older patients at higher risk of death.

Methods

The present study used data from Gerocovid-acute wards, an observational multicenter retrospective-prospective study conducted in geriatric and internal medicine wards in subjects ≥60 years old during the COVID-19 pandemic. Seventy-one routinely collected variables, including demographic data, living arrangements, smoking habits, pre-COVID-19 mobility, chronic diseases, and clinical and laboratory parameters were integrated into a web-based machine learning platform (Just Add Data Bio) to identify factors with the highest prognostic relevance. The use of artificial intelligence allowed us to avoid variable selection bias, to test a large number of models and to perform an internal validation.

Results

The dataset was split into training and test sets, based on a 70:30 ratio and matching on age, sex, and proportion of events; 3,520 models were set out to train. The three predictive algorithms (optimized for performance, interpretability, or aggressive feature selection) converged on the same model, including 12 variables: pre-COVID-19 mobility, World Health Organization disease severity, age, heart rate, arterial blood gases bicarbonate and oxygen saturation, serum potassium, systolic blood pressure, blood glucose, aspartate aminotransferase, PaO2/FiO2 ratio and derived neutrophil-to-lymphocyte ratio.

Conclusion

Beyond variables reflecting the severity of COVID-19 disease failure, pre-morbid mobility level was the strongest factor associated with in-hospital mortality reflecting the importance of functional status as a synthetic measure of health in older adults, while the association between derived neutrophil-to-lymphocyte ratio and mortality, confirms the fundamental role played by neutrophils in SARS-CoV-2 disease.

Association Between Barthel's Index Change and All-Cause Mortality Among COVID-19 Pneumonia Patients Aged Over 80 Years Old: A Retrospective Cohort Study

Purpose

It has been shown that lower Barthel's index (BI) at admission is associated with a higher in-hospital mortality. There is a lack of evidence regarding the association between the change in BI during hospitalization and mortality after discharge. Our purpose was to determine whether the BI change during hospitalization is associated with all-cause mortality in older adults with COVID-19 pneumonia.

Patients and Methods

We conducted a retrospective cohort study of 330 participants at Peking University Third Hospital during the COVID-19 pandemic period. In order to analyze the time to death data, a Kaplan-Meier survival curve was used. We used restricted cubic splines to analyze the association between BI change and all-cause mortality among COVID-19 pneumonia patients aged over 80 years old. Threshold effect analysis was used to assess the ability of BI change score to predict all-cause mortality.

Results

Our study included 330 patients aged over 80 years with COVID-19 pneumonia. The Kaplan-Meier curve for mortality showed significantly worst survival with reduced BI among three groups (χ2= 6.896, P < 0.05). There was a non-linear association between the BI change and all-cause mortality (P for all over <0.001). The effect sizes on the left and right sides of the inflection point were 0.958 (HR: 0.958, 95% CI 0.932-0.958, P < 0.05) and 1.013 (HR: 1.013, 95% CI 0.967-1.062, P > 0.05), respectively.

Conclusion

Reduced BI during hospitalization was associated with the highest mortality risk. It is crucial to monitor BI change among COVID-19 pneumonia patients aged over 80 years old.

The influence of nationwide COVID-19 lockdown on the functional impairment and long-term survival of dependent people for carrying out basic activities of daily living in a neighborhood of the city of Madrid, Spain: Orcasitas Cohort Longitudinal Study

Background

Prolonged confinement can lead to personal deterioration at various levels. We studied this phenomenon during the nationwide COVID-19 lockdown in a functionally dependent population of the Orcasitas neighborhood of Madrid, Spain, by measuring their ability to perform basic activities of daily living and their mortality rate.

Methods

A total of 127 patients were included in the Orcasitas cohort. Of this cohort, 78.7% were female, 21.3% were male, and their mean age was 86 years. All participants had a Barthel index of ≤ 60. Changes from pre- to post-confinement and 3 years afterward were analyzed, and the effect of these changes on survival was assessed (2020-2023).

Results

The post-confinement functional assessment showed significant improvement in independence over pre-confinement for both the Barthel score (t = -5.823; p < 0.001) and the classification level (z = -2.988; p < 0.003). This improvement progressively disappeared in the following 3 years, and 40.9% of the patients in this cohort died during this period. These outcomes were associated with the Barthel index (z = -3.646; p < 0.001) and the level of dependence (hazard ratio 2.227; CI 1.514-3.276). Higher mortality was observed among men (HR 1.745; CI 1.045-2.915) and those with severe dependence (HR 2.169; CI 1.469-3.201). Setting the cutoff point of the Barthel index at 40 provided the best detection of the risk of death associated with dependence.

Conclusions

Home confinement and the risk of death due to the COVID-19 pandemic awakened a form of resilience in the face of adversity among the population of functionally dependent adults. The Barthel index is a good predictor of medium- and long-term mortality and is a useful method for detecting populations at risk in health planning. A cutoff score of 40 is useful for this purpose. To a certain extent, the non-institutionalized dependent population is an invisible population. Future studies should analyze the causes of the high mortality observed.

Description of Post-discharge Outcomes of Patients with COVID-19 in a Tertiary Referral Center in the Philippines

Background and Objectives

The immediate post-discharge period of COVID-19 patients is a vulnerable state due to several complications that may arise during this time. Some patients get readmitted shortly after being discharged while others report persistence of symptoms, develop specialized needs, or observe a decline from their baseline functional capacity. Information on the outcomes of post-COVID discharge patients in our institution is currently lacking. This study described the outcomes of patients with COVID-19 after their discharge from the service areas of Philippine General Hospital.

Methods

This study is a retrospective chart review involving charts of all adult patients discharged from the PGH COVID service areas last August 2021 to October 2021. Data from their follow up consults at 1 week, 1 month, and 3 months post-discharge were reviewed. Baseline characteristics and post-discharge outcomes including post-COVID symptoms, special care needs, mortality, rehospitalization, emergency consult, level of dependence, and ability to return to work were assessed.

Results

A total of 171 patient charts were included. The mean age of patients was 53.7 years. Most were male (60.2%), unemployed (59.7%), non-smoker (55%), hypertensive (57.9%), diabetic (50.2%), and obese (50.2%). Most of them were oxygen requiring (80%) and with severe to critical COVID infection (72.5%) during admission. At 3 months post-discharge, 113 (66%) were stable and able to complete the follow up, 8 (4.6%) died, 9 (5.2%) got readmitted, and 41 (23.9%) were lost to follow up. Among those who were able to follow up after 3 months, 84 (74%) were asymptomatic. Among those who remained symptomatic, the most common symptoms were dyspnea, fatigue, and cough. After 3 months, 100 (88%) did not require special care needs, 100 (88%) were fully independent, and 45 (39.8%) were able to return to baseline work.

Conclusions

Despite the majority of patients having severe to critical COVID infection during admission, most were asymptomatic within 3 months post-discharge. In those who developed persistent symptoms, dyspnea, cough, and fatigue were the most common symptoms identified regardless of COVID severity. Majority did not require special care needs.

Decoding emotional resilience in aging: unveiling the interplay between daily functioning and emotional health

Background

EPs pose significant challenges to individual health and quality of life, attracting attention in public health as a risk factor for diminished quality of life and healthy life expectancy in middle-aged and older adult populations. Therefore, in the context of global aging, meticulous exploration of the factors behind emotional issues becomes paramount. Whether ADL can serve as a potential marker for EPs remains unclear. This study aims to provide new evidence for ADL as an early predictor of EPs through statistical analysis and validation using machine learning algorithms.

Methods

Data from the 2018 China Health and Retirement Longitudinal Study (CHARLS) national baseline survey, comprising 9,766 samples aged 45 and above, were utilized. ADL was assessed using the BI, while the presence of EPs was evaluated based on the record of "Diagnosed with Emotional Problems by a Doctor" in CHARLS data. Statistical analyses including independent samples t-test, chi-square test, Pearson correlation analysis, and multiple linear regression were conducted using SPSS 25.0. Machine learning algorithms, including Support Vector Machine (SVM), Decision Tree (DT), and Logistic Regression (LR), were implemented using Python 3.10.2.

Results

Population demographic analysis revealed a significantly lower average BI score of 65.044 in the "Diagnosed with Emotional Problems by a Doctor" group compared to 85.128 in the "Not diagnosed with Emotional Problems by a Doctor" group. Pearson correlation analysis indicated a significant negative correlation between ADL and EPs (r = -0.165, p < 0.001). Iterative analysis using stratified multiple linear regression across three different models demonstrated the persistent statistical significance of the negative correlation between ADL and EPs (B = -0.002, β = -0.186, t = -16.476, 95% CI = -0.002, -0.001, p = 0.000), confirming its stability. Machine learning algorithms validated our findings from statistical analysis, confirming the predictive accuracy of ADL for EPs. The area under the curve (AUC) for the three models were SVM-AUC = 0.700, DT-AUC = 0.742, and LR-AUC = 0.711. In experiments using other covariates and other covariates + BI, the overall prediction level of machine learning algorithms improved after adding BI, emphasizing the positive effect of ADL on EPs prediction.

Conclusion

This study, employing various statistical methods, identified a negative correlation between ADL and EPs, with machine learning algorithms confirming this finding. Impaired ADL increases susceptibility to EPs.

Factors Associated with SARS-CoV-2 Infection in Fully Vaccinated Nursing Home Residents and Workers

Persons living or working in nursing homes faced a higher risk of SARS-CoV-2 infections during the pandemic, resulting in heightened morbidity and mortality among older adults despite robust vaccination efforts. This prospective study evaluated the humoral and cellular immunity in fully vaccinated residents and workers from two nursing homes in Madrid, Spain, from 2020 to 2021. Measurements of IgG levels were conducted in August 2020 (pre-vaccination) and June and September 2021 (post-vaccination), alongside assessments of neutralizing antibodies and cellular responses in September 2021 among the most vulnerable individuals. Follow-up extended until February 2022 to identify risk factors for SARS-CoV-2 infection or mortality, involving 267 residents (mean age 87.6 years, 81.3% women) and 302 workers (mean age 50.7 years, 82.1% women). Residents exhibited a significantly higher likelihood of experiencing COVID-19 before June 2021 compared with nursing staff (OR [95% CI], 7.2 [3.0 to 17.2], p < 0.01). Participants with a history of previous COVID-19 infection showed more significant increases in IgG levels in August 2020, June 2021 and September 2021, alongside an increased proportion of neutralizing antibodies in the most vulnerable individuals. However, IgG decay remained the same between June and September 2021 based on the previous COVID-19 status. During the Omicron variant wave, residents and staff showed a similar rate of SARS-CoV-2 infection. Notably, preceding clinical or immunological factors before receiving three vaccination doses did not demonstrate associations with COVID-19 infection or overall mortality in our participant cohort.

Taking precautions in advance: a lower level of activities of daily living may be associated with a higher likelihood of memory-related diseases

Introduction

Memory-related diseases (MDs) pose a significant healthcare challenge globally, and early detection is essential for effective intervention. This study investigates the potential of Activities of Daily Living (ADL) as a clinical diagnostic indicator for MDs. Utilizing data from the 2018 national baseline survey of the China Health and Retirement Longitudinal Study (CHARLS), encompassing 10,062 Chinese individuals aged 45 or older, we assessed ADL using the Barthel Index (BI) and correlated it with the presence of MDs. Statistical analysis, supplemented by machine learning algorithms (Support Vector Machine, Decision Tree, and Logistic Regression), was employed to elucidate the relationship between ADL and MDs.

Background

MDs represent a significant public health concern, necessitating early detection and intervention to mitigate their impact on individuals and society. Identifying reliable clinical diagnostic signs for MDs is imperative. ADL have garnered attention as a potential marker. This study aims to rigorously analyze clinical data and validate machine learning algorithms to ascertain if ADL can serve as an indicator of MDs.

Methods

Data from the 2018 national baseline survey of the China Health and Retirement Longitudinal Study (CHARLS) were employed, encompassing responses from 10,062 Chinese individuals aged 45 or older. ADL was assessed using the BI, while the presence of MDs was determined through health report questions. Statistical analysis was executed using SPSS 25.0, and machine learning algorithms, including Support Vector Machine (SVM), Decision Tree Learning (DT), and Logistic Regression (LR), were implemented using Python 3.10.2.

Results

Population characteristics analysis revealed that the average BI score for individuals with MDs was 70.88, significantly lower than the average score of 87.77 in the control group. Pearson's correlation analysis demonstrated a robust negative association (r = -0.188, p < 0.001) between ADL and MDs. After adjusting for covariates such as gender, age, smoking status, drinking status, hypertension, diabetes, and dyslipidemia, the negative relationship between ADL and MDs remained statistically significant (B = -0.002, β = -0.142, t = -14.393, 95% CI = -0.002, -0.001, p = 0.000). The application of machine learning models further confirmed the predictive accuracy of ADL for MDs, with area under the curve (AUC) values as follows: SVM-AUC = 0.69, DT-AUC = 0.715, LR-AUC = 0.7. Comparative analysis of machine learning outcomes with and without the BI underscored the BI's role in enhancing predictive abilities, with the DT model demonstrating superior performance.

Conclusion

This study establishes a robust negative correlation between ADL and MDs through comprehensive statistical analysis and machine learning algorithms. The results validate ADL as a promising diagnostic indicator for MDs, with enhanced predictive accuracy when coupled with the Barthel Index. Lower levels of ADL are associated with an increased likelihood of developing memory-related diseases, underscoring the clinical relevance of ADL assessment in early disease detection.

Validation of the Barthel Index as a Predictor of In-Hospital Mortality among COVID-19 Patients

In order to predict the high mortality due to COVID-19, simple, useful and remote instruments are required. To assess the validity of the baseline Barthel Index score as a predictor of in-hospital mortality among COVID-19 patients, a validation study of a clinical prediction tool in a cohort of patients with COVID-19 was conducted. The primary variable was mortality and the Barthel Index was the main explanatory variable. Demographic, clinical and laboratory variables were collected. Other mortality predictor scores were also assessed: Pneumonia Severity Index, CURB-65 and A-DROP. The Receiver Operating Characteristic Area under the Curve (ROC AUC), sensitivity and specificity were calculated for both the Barthel Index and the other predictor scores. An analysis of the association between the main variables was conducted, adjusting by means of three multivariate models. Three hundred and twelve patients were studied. Mortality was 16.4%. A mortality Odds Ratio (OR) of 5.95 was associated with patients with a Barthel Index ≤ 90. The model number 3 was developed to predict in-hospital mortality before COVID-19 infection occurs. It exhibits an OR of 3.44, a ROC AUC of 0.792, a sensitivity of 74.5% and a specificity of 73.9%. The Baseline Barthel Index proved useful in our population as a predictor of in-hospital mortality due to COVID-19.