Multimorbidity and SARS-CoV-2-Related Outcomes: Analysis of a Cohort of Italian Patients

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.

A Multimorbidity Analysis of Hospitalized Patients With COVID-19 in Northwest Italy: Longitudinal Study Using Evolutionary Machine Learning and Health Administrative Data

BACKGROUND

Multimorbidity is a significant public health concern, characterized by the coexistence and interaction of multiple preexisting medical conditions. This complex condition has been associated with an increased risk of COVID-19. Individuals with multimorbidity who contract COVID-19 often face a significant reduction in life expectancy. The postpandemic period has also highlighted an increase in frailty, emphasizing the importance of integrating existing multimorbidity details into epidemiological risk assessments. Managing clinical data that include medical histories presents significant challenges, particularly due to the sparsity of data arising from the rarity of multimorbidity conditions. Also, the complex enumeration of combinatorial multimorbidity features introduces challenges associated with combinatorial explosions.

OBJECTIVE

This study aims to assess the severity of COVID-19 in individuals with multiple medical conditions, considering their demographic characteristics such as age and sex. We propose an evolutionary machine learning model designed to handle sparsity, analyzing preexisting multimorbidity profiles of patients hospitalized with COVID-19 based on their medical history. Our objective is to identify the optimal set of multimorbidity feature combinations strongly associated with COVID-19 severity. We also apply the Apriori algorithm to these evolutionarily derived predictive feature combinations to identify those with high support.

METHODS

We used data from 3 administrative sources in Piedmont, Italy, involving 12,793 individuals aged 45-74 years who tested positive for COVID-19 between February and May 2020. From their 5-year pre-COVID-19 medical histories, we extracted multimorbidity features, including drug prescriptions, disease diagnoses, sex, and age. Focusing on COVID-19 hospitalization, we segmented the data into 4 cohorts based on age and sex. Addressing data imbalance through random resampling, we compared various machine learning algorithms to identify the optimal classification model for our evolutionary approach. Using 5-fold cross-validation, we evaluated each model's performance. Our evolutionary algorithm, utilizing a deep learning classifier, generated prediction-based fitness scores to pinpoint multimorbidity combinations associated with COVID-19 hospitalization risk. Eventually, the Apriori algorithm was applied to identify frequent combinations with high support.

RESULTS

We identified multimorbidity predictors associated with COVID-19 hospitalization, indicating more severe COVID-19 outcomes. Frequently occurring morbidity features in the final evolved combinations were age>53, R03BA (glucocorticoid inhalants), and N03AX (other antiepileptics) in cohort 1; A10BA (biguanide or metformin) and N02BE (anilides) in cohort 2; N02AX (other opioids) and M04AA (preparations inhibiting uric acid production) in cohort 3; and G04CA (Alpha-adrenoreceptor antagonists) in cohort 4.

CONCLUSIONS

When combined with other multimorbidity features, even less prevalent medical conditions show associations with the outcome. This study provides insights beyond COVID-19, demonstrating how repurposed administrative data can be adapted and contribute to enhanced risk assessment for vulnerable populations.

Relationship of oral bacterial number with medical hospitalization costs in analysis of Diagnosis Procedure Combination database from single institution in Japan

Oral bacteria are known to be associated with perioperative complications during hospitalization. However, no presented reports have clarified the relationship of oral bacterial number with medical costs for inpatients. The Diagnosis Procedure Combination (DPC) database system used in Japan provides clinical information regarding acute hospital patients. The present study was conducted to determine the association of oral bacterial numbers in individual patients treated at a single institution with length of hospital stay and medical costs using DPC data. A total of 2369 patients referred by the medical department to the dental department at Hiroshima University Hospital were divided into the low (n = 2060) and high (n = 309) oral bacterial number groups. Length of hospital stay and medical costs were compared between the groups, as well as the associations of number of oral bacteria with Charlson comorbidity index (CCI)-related diseases in regard to mortality and disease severity. There was no significant difference in hospital stay length between the low (24.3 ± 24.2 days) and high (22.8 ± 20.1 days) oral bacterial number groups. On the other hand, the daily hospital medical cost in the high group was significantly greater (US$1456.2 ± 1505.7 vs. US$1185.7 ± 1128.6, P < 0.001). Additionally, there was no significant difference in CCI score between the groups, whereas the daily hospital medical costs for patients in the high group treated for cardiovascular disease or malignant tumors were greater than in the low number group (P < 0.05). Multivariate regression analysis was also performed, which showed that oral bacterial number, age, gender, BMI, cardiovascular disease, diabetes, malignant tumor, and hospital stay length were independently associated with daily hospitalization costs. Monitoring and oral care treatment to lower the number of oral bacteria in patients affected by cardiovascular disease or cancer may contribute to reduce hospitalization costs.

Multimorbidity in middle-aged women and COVID-19: binary data clustering for unsupervised binning of rare multimorbidity features and predictive modeling

BACKGROUND

Multimorbidity is typically associated with deficient health-related quality of life in mid-life, and the likelihood of developing multimorbidity in women is elevated. We address the issue of data sparsity in non-prevalent features by clustering the binary data of various rare medical conditions in a cohort of middle-aged women. This study aims to enhance understanding of how multimorbidity affects COVID-19 severity by clustering rare medical conditions and combining them with prevalent features for predictive modeling. The insights gained can guide the development of targeted interventions and improved management strategies for individuals with multiple health conditions.

METHODS

The study focuses on a cohort of 4477 female patients, (aged 45-60) in Piedmont, Italy, and utilizes their multimorbidity data prior to the COVID-19 pandemic from their medical history from 2015 to 2019. The COVID-19 severity is determined by the hospitalization status of the patients from February to May 2020. Each patient profile in the dataset is depicted as a binary vector, where each feature denotes the presence or absence of a specific multimorbidity condition. By clustering the sparse medical data, newly engineered features are generated as a bin of features, and they are combined with the prevalent features for COVID-19 severity predictive modeling.

RESULTS

From sparse data consisting of 174 input features, we have created a low-dimensional feature matrix of 17 features. Machine Learning algorithms are applied to the reduced sparsity-free data to predict the Covid-19 hospital admission outcome. The performance obtained for the corresponding models are as follows: Logistic Regression (accuracy 0.72, AUC 0.77, F1-score 0.69), Linear Discriminant Analysis (accuracy 0.7, AUC 0.77, F1-score 0.67), and Ada Boost (accuracy 0.7, AUC 0.77, F1-score 0.68).

CONCLUSION

Mapping higher-dimensional data to a low-dimensional space can result in information loss, but reducing sparsity can be beneficial for Machine Learning modeling due to improved predictive ability. In this study, we addressed the issue of data sparsity in electronic health records and created a model that incorporates both prevalent and rare medical conditions, leading to more accurate and effective predictive modeling. The identification of complex associations between multimorbidity and the severity of COVID-19 highlights potential areas of focus for future research, including long COVID and intervention efforts.

Multimorbidity and frailty are associated with poorer SARS-CoV-2-related outcomes: systematic review of population-based studies

BACKGROUND

Estimating the risks and impacts of COVID-19 for different health groups at the population level is essential for orienting public health measures. Adopting a population-based approach, we conducted a systematic review to explore: (1) the etiological role of multimorbidity and frailty in developing SARS-CoV-2 infection and COVID-19-related short-term outcomes; and (2) the prognostic role of multimorbidity and frailty in developing short- and long-term outcomes. This review presents the state of the evidence in the early years of the pandemic. It was conducted within the European Union Horizon 2020 program (No: 101018317); Prospero registration: CRD42021249444.

METHODS

PubMed, Embase, World Health Organisation COVID-19 Global literature on coronavirus disease, and PsycINFO were searched between January 2020 and 7 April 2021 for multimorbidity and 1 February 2022 for frailty. Quantitative peer-reviewed studies published in English with population-representative samples and validated multimorbidity and frailty tools were considered.

RESULTS

Overall, 9,701 records were screened by title/abstract and 267 with full text. Finally, 14 studies were retained for multimorbidity (etiological role, n = 2; prognostic, n = 13) and 5 for frailty (etiological role, n = 2; prognostic, n = 4). Only short-term outcomes, mainly mortality, were identified. An elevated likelihood of poorer outcomes was associated with an increasing number of diseases, a higher Charlson Comorbidity Index, different disease combinations, and an increasing frailty level.

DISCUSSION

Future studies, which include the effects of recent virus variants, repeated exposure and vaccination, will be useful for comparing the possible evolution of the associations observed in the earlier waves.

Effects of the pre-existing coronary heart disease on the prognosis of COVID-19 patients: A systematic review and meta-analysis

Although studies have shown severe Coronavirus disease 2019 (COVID-19) outcomes in patients with pre-existing coronary heart disease (CHD), the prognosis of COVID-19 patients with pre-existing CHD remains uncertain primarily due to the limited number of patients in existing studies. This study aimed to investigate the impacts of pre-existing CHD on the prognosis of COVID-19 patients. Five electronic databases were searched for eligible studies. This article focused on cohort and case-control studies involving the prognosis of COVID-19 patients with pre-existing CHD. The meta-analysis was performed using a random effects model. The odds ratios (ORs) and 95% confidence intervals (CIs) were used as valid indicators. The study was registered in PROSPERO with the identifier: CRD42022352853. A total of 81 studies, involving 157,439 COVID-19 patients, were included. The results showed that COVID-19 patients with pre-existing CHD exhibited an elevated risk of mortality (OR = 2.45; 95%CI: [2.04, 2.94], P < 0.001), severe/critical COVID-19 (OR = 2.57; 95%CI: [1.98, 3.33], P < 0.001), Intensive Care Unit or Coronary Care Unit (ICU/CCU) admission: (OR = 2.75, 95%CI: [1.61, 4.72], P = 0.002), and reduced odds of discharge/recovery (OR = 0.43, 95%CI: [0.28, 0.66], P < 0.001) compared to COVID-19 patients without pre-existing CHD. Subgroup analyses indicated that the prognosis of COVID-19 patients with pre-existing CHD was influenced by publication year, follow-up duration, gender, and hypertension. In conclusion, pre-existing CHD significantly increases the risk of poor prognosis in patients with COVID-19, particularly in those male or hypertensive patients.

Head and neck cancer patients show poor oral health as compared to those with other types of cancer

PURPOSE

Several studies have found associations between periodontitis and various types of cancer. Since the site of head and neck cancer (HNC) has contiguity or proximity to the oral cavity, it may be particularly influenced by oral inflammation. This study aimed to determine whether HNC patients have poor oral health as compared to those with other types of cancer.

METHODS

This study retrospectively examined oral environmental factors including periodontal inflamed surface area (PISA), a new periodontal inflammatory parameter. A total of 1030 cancer patients were divided into the HNC (n = 142) and other cancer (n = 888) groups. Furthermore, the HNC group was divided into high (n = 71) and low (n = 71) PISA subgroups, and independent risk factors affecting a high PISA value were investigated.

RESULTS

Multivariate logistic regression analysis showed that number of missing teeth (odds ratio 1.72, 95% CI 1.15-2.56, P < 0.01), PISA (odds ratio 1.06, 95% CI 1.03-1.06, P < 0.05), and oral bacterial count (odds ratio 1.02, 95% CI 1.01-1.03, P < 0.01) were independent factors related to HNC. In addition, multivariate logistic regression analysis indicated that current smoker (odds ratio 7.51, 95% CI 1.63-34.71, P < 0.01) and presence of untreated dental caries (odds ratio 3.33, 95% CI 1.23-9.00, P < 0.05) were independent risk factors affecting high PISA values in HNC patients.

CONCLUSION

HNC patients have higher levels of gingival inflammation and poor oral health as compared to patients with other types of cancer, indicating that prompt oral assessment and an effective oral hygiene management plan are needed at the time of HNC diagnosis.

Multimorbidity and Serological Response to SARS-CoV-2 Nine Months after 1st Vaccine Dose: European Cohort of Healthcare Workers-Orchestra Project

Understanding antibody persistence concerning multimorbidity is crucial for vaccination policies. Our goal is to assess the link between multimorbidity and serological response to SARS-CoV-2 nine months post-first vaccine. We analyzed Healthcare Workers (HCWs) from three cohorts from Italy, and one each from Germany, Romania, Slovakia, and Spain. Seven groups of chronic diseases were analyzed. We included 2941 HCWs (78.5% female, 73.4% ≥ 40 years old). Multimorbidity was present in 6.9% of HCWs. The prevalence of each chronic condition ranged between 1.9% (cancer) to 10.3% (allergies). Two regression models were fitted, one considering the chronic conditions groups and the other considering whether HCWs had diseases from ≥2 groups. Multimorbidity was present in 6.9% of HCWs, and higher 9-months post-vaccine anti-S levels were significantly associated with having received three doses of the vaccine (RR = 2.45, CI = 1.92-3.13) and with having a prior COVID-19 infection (RR = 2.30, CI = 2.15-2.46). Conversely, lower levels were associated with higher age (RR = 0.94, CI = 0.91-0.96), more time since the last vaccine dose (RR = 0.95, CI = 0.94-0.96), and multimorbidity (RR = 0.89, CI = 0.80-1.00). Hypertension is significantly associated with lower anti-S levels (RR = 0.87, CI = 0.80-0.95). The serological response to vaccines is more inadequate in individuals with multimorbidity.