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1.
IEEE ACCESS ; 10:62282-62291, 2022.
Article in English | Web of Science | ID: covidwho-1909181

ABSTRACT

In this study, a survival analysis of the time to death caused by coronavirus disease 2019 is presented. The analysis of a dataset from the East Asian region with a focus on data from the Philippines revealed that the hazard of time to death was associated with the symptoms and background variables of patients. Machine learning algorithms, i.e., dimensionality reduction and boosting, were used along with conventional Cox regression. Machine learning algorithms solved the diverging problem observed when using traditional Cox regression and improved performance by maximizing the concordance index (C-index). Logistic principal component analysis for dimensionality reduction was significantly efficient in addressing the collinearity problem. In addition, to address the nonlinear pattern, a higher C-index was achieved using extreme gradient boosting (XGBoost). The results of the analysis showed that the symptoms were statistically significant for the hazard rate. Among the symptoms, respiratory and pneumonia symptoms resulted in the highest hazard level, which can help in the preliminary identification of high-risk patients. Among various background variables, the influence of age, chronic disease, and their interaction were identified as significant. The use of XGBoost revealed that the hazards were minimized during middle age and increased for younger and older people without any chronic diseases, with only the elderly having a higher risk of chronic disease. These results imply that patients with respiratory and pneumonia symptoms or older patients should be given medical attention.

2.
Intensive Crit Care Nurs ; 72: 103265, 2022 Oct.
Article in English | MEDLINE | ID: covidwho-1899758

ABSTRACT

OBJECTIVE: To assess variation in ICU length of stay between countries with varying patient-to-nurse ratios; to compare ICU length of stay of individual countries against an international benchmark. DESIGN: Secondary analysis of the DecubICUs trial (performed on 15 May 2018). SETTING: The study cohort included 12,794 adult ICU patients (57 countries). Only countries with minimally twenty patients discharged (or deceased) within 30 days of ICU admission were included. MAIN OUTCOME MEASURE: Multivariate Cox regression was used to evaluate ICU length of stay, censored at 30 days, across countries and for patient-to-nurse ratio, adjusted for sex, age, admission type and Simplified Acute Physiology Score II. The resulting hazard ratios for countries, indicating longer or shorter length of stay than average, were plotted on a forest plot. Results by country were benchmarked against the overall length of stay using Kaplan-Meier curves. RESULTS: Patients had a median ICU length of stay of 11 days (interquartile range, 4-27). Hazard ratio by country ranged from minimally 0.42 (95% confidence interval 0.35-0.51) for Greece, to maximaly1.94 (1.28-2.93) for Lithuania. The hazard ratio for patient-to-nurse was 0.96 (0.94-0.98), indicating that higher patient-to-nurse ratio results in longer length of stay. CONCLUSIONS: Despite adjustment for case-mix, we observed significant heterogeneity of ICU length of stay in-between countries, and a significantly longer length of stay when patient-to-nurse ratio increases. Future studies determining underlying characteristics of individual ICUs and broader organisation of healthcare infrastructure within countries may further explain the observed heterogeneity in ICU length of stay.


Subject(s)
Intensive Care Units , Patient Discharge , Adult , Cohort Studies , Hospital Mortality , Humans , Length of Stay , Retrospective Studies
3.
J Infect Chemother ; 28(10): 1439-1444, 2022 Oct.
Article in English | MEDLINE | ID: covidwho-1885917

ABSTRACT

INTRODUCTION: In Japan, patients with coronavirus disease 2019 (COVID-19) who do not require medical intervention are provided care in recovery accommodation facilities (RAFs). However, some patients may require hospitalization if their symptoms become more severe during their stay. We conducted an observational study using epidemiological data of patients with COVID-19 admitted to RAFs in Tokyo. METHODS: This was an observational cohort study using data from COVID-19 patients admitted to one of the RAFs in Tokyo from December 2020 to November 2021. Admissions to the facilities were limited to patients with asymptomatic or mild COVID-19 with no underlying disease or at least stable underlying disease at the time of admission. Patients were hospitalized when they required oxygen administration or when they had, or persistent fever, or severe respiratory symptoms. We evaluated the association between hospitalization and the risk factors for hospitalization using a Cox regression model. RESULTS: The number of patients with COVID-19 admitted to the RAF was 6176. The number of hospitalized patients was 393 (6.4%), and the median length of stay was 5.50 days (IQR: 4.50, 6.50). In the Cox regression analysis, the hazard ratio increased with age and was significantly higher among patients aged >60 years (HR = 10.23, 95% CI: 6.72-15.57) than those in other age groups. This trend is similar to that observed in the sensitivity analysis. CONCLUSION: Patients with diabetes, the elderly, obesity, and medications for gout and psychiatric diseases may be at a high risk of hospitalization. In particular, an age over 60 years was strongly associated with hospitalization.


Subject(s)
COVID-19 , Aged , COVID-19/epidemiology , COVID-19/therapy , Hospitalization , Humans , Retrospective Studies , Risk Factors , SARS-CoV-2 , Tokyo/epidemiology
4.
Stat Med ; 41(16): 3076-3089, 2022 Jul 20.
Article in English | MEDLINE | ID: covidwho-1782695

ABSTRACT

SARS-CoV-2 continues to evolve and the vaccine efficacy against variants is challenging to estimate. It is now common in phase III vaccine trials to provide vaccine to those randomized to placebo once efficacy has been demonstrated, precluding a direct assessment of placebo controlled vaccine efficacy after placebo vaccination. In this work, we extend methods developed for estimating vaccine efficacy post placebo vaccination to allow variant specific time varying vaccine efficacy, where time is measured since vaccination. The key idea is to infer counterfactual strain specific placebo case counts by using surveillance data that provide the proportions of the different strains. This blending of clinical trial and observational data allows estimation of strain-specific time varying vaccine efficacy, or sieve effects, including for strains that emerge after placebo vaccination. The key requirements are that the surveillance strain distribution accurately reflects the strain distribution for a placebo group throughout follow-up after placebo group vaccination, and that at least one strain is present before and after placebo vaccination. For illustration, we develop a Poisson approach for an idealized design under a rare disease assumption and then use a proportional hazards model to address staggered entry, staggered crossover, and smoothly varying strain specific vaccine efficacy. We evaluate these methods by theoretical work and simulations, and demonstrate that useful estimation of the efficacy profile is possible for strains that emerge after vaccination of the placebo group. An important principle is to incorporate sensitivity analyses to guard against misspecification of the strain distribution.


Subject(s)
COVID-19 Vaccines , COVID-19 , Vaccine Efficacy , COVID-19/prevention & control , COVID-19 Vaccines/immunology , Cross-Over Studies , Humans , Observational Studies as Topic , Placebos , Proportional Hazards Models , Randomized Controlled Trials as Topic , SARS-CoV-2 , Vaccination
5.
2021 International Conference on Computing, Computational Modelling and Applications, ICCMA 2021 ; : 130-137, 2021.
Article in English | Scopus | ID: covidwho-1746085

ABSTRACT

There are several established methods for comparing more than two survival curves, namely the scale-rank test or Cox's proportional hazard model. However, when their statistical assumptions are not met, their results' validity is affected. In this study, we address the mentioned issue and propose a new statistical approach on how to compare more than two survival curves using a random forest algorithm, which is practically assumption-free. The repetitive generating of many decision trees covered by one random forest model enables to calculate of a proportion of trees with sufficient complexity classifying into all groups (depicted by their survival curves), which is the p-value estimate as an analogy of the classical Wald's t-test output of the Cox's regression. Furthermore, a level of the pruning of decision trees the random forest model is built with, can modify both the robustness and statistical power of the random forest alternative. The discussed results are confirmed using COVID-19 survival data with varying the tree pruning level. The introduced method for survival curves comparison, based on random forest algorithm, seems to be a valid alternative to Cox's regression;however, it has no statistical assumptions and tends to reach higher statistical power. © 2021 IEEE

6.
BMJ Open ; 12(3): e050877, 2022 03 09.
Article in English | MEDLINE | ID: covidwho-1736065

ABSTRACT

OBJECTIVE: To identify patients at risk of mid-late term revision of hip replacement to inform targeted follow-up. DESIGN: Analysis of linked national data sets from primary and secondary care (Clinical Practice Research Datalink (CPRD-GOLD); National Joint Registry (NJR); English Hospital Episode Statistics (HES); Patient-Reported Outcome Measures (PROMs)). PARTICIPANTS: Primary elective total hip replacement (THR) aged≥18. EVENT OF INTEREST: Revision surgery≥5 years (mid-late term) after primary THR. STATISTICAL METHODS: Cox regression modelling to ascertain risk factors of mid-late term revision. HR and 95% CI assessed association of sociodemographic factors, comorbidities, medication, surgical variables and PROMs with mid-late term revision. RESULTS: NJR-HES-PROMs data were available from 2008 to 2011 on 142 275 THR; mean age 70.0 years and 61.9% female. CPRD GOLD-HES data covered 1995-2011 on 17 047 THR; mean age 68.4 years, 61.8% female. Patients had minimum 5 years postprimary surgery to end 2016. In NJR-HES-PROMS data, there were 3582 (2.5%) revisions, median time-to-revision after primary surgery 1.9 years (range 0.01-8.7), with 598 (0.4%) mid-late term revisions; in CPRD GOLD, 982 (5.8%) revisions, median time-to-revision 5.3 years (range 0-20), with 520 (3.1%) mid-late term revisions.Reduced risk of mid-late term revision was associated with older age at primary surgery (HR: 0.96; 95% CI: 0.95 to 0.96); better 6-month postoperative pain/function scores (HR: 0.35; 95% CI: 0.27 to 0.46); use of ceramic-on-ceramic (HR: 0.73; 95% CI: 0.56 to 0.95) or ceramic-on-polyethylene (HR: 0.76; 95% CI: 0.58 to 1.00) bearing surfaces.Increased risk of mid-late term revision was associated with the use of antidepressants (HR: 1.32; 95% CI: 1.09 to 1.59), glucocorticoid injections (HR: 1.33; 95% CI: 1.06 to 1.67) and femoral head size≥44 mm (HR: 2.56; 95% CI: 1.09 to 6.02)No association of gender, obesity or Index of Multiple Deprivation was observed. CONCLUSION: The risk of mid-late term THR is associated with age at primary surgery, 6-month postoperative pain and function and implant factors. Further work is needed to explore the associations with prescription medications observed in our data.


Subject(s)
Arthroplasty, Replacement, Hip , Hip Prosthesis , Aged , Arthroplasty, Replacement, Hip/adverse effects , Female , Follow-Up Studies , Humans , Male , Pain, Postoperative/etiology , Prosthesis Design , Prosthesis Failure , Registries , Reoperation , Retrospective Studies , Risk Factors , United Kingdom/epidemiology
7.
J Diabetes Metab Disord ; 20(2): 1675-1683, 2021 Dec.
Article in English | MEDLINE | ID: covidwho-1694195

ABSTRACT

PURPOSE: Coronavirus increases mortality rate in people with underlying disease. The purpose of the present research was to compare the clinical outcomes in Covid-19 patients with and without underlying diabetes disease using propensity score matching. METHODS: A matched case-control study was conducted on 459 diabetic patients with Covid-19 (case group) and 459 non-diabetic patients with Covid-19 (control group). Matching in two groups was performed using propensity score matching method. The effect of covariates on the clinical outcome of the patients (recovery-death) was assessed using logistic regression and the associations of factors with the patients' survival were determined using Cox proportional hazards regression model. Data were analyzed using R software. RESULTS: The mean (standard deviation) age of patients in the case and control groups were 65.77 (12.2) and 65.8 (12.24), respectively. 196 patients (43%) in the case group, and 249 patients (54%) in the control group were male (with P-value < 0.05). The logistic regression model showed that the variables of age, level of blood oxygen (SpO2), ICU admission, length of hospitalization, cancer and diabetes affected patients' death. Furthermore, the resuts of the Cox regression showed that the variables of age, level of blood oxygen (SpO2), ICU admission,cancer and diabetes were related to survival of the patients. It was found that diabetes was significantly associated with mortality from COVID-19 with odds ratio of 2.88 (95% CI: 1.80-4.69; P < 0.01) and hazard ratio of 1.45 (95% CI: 1.01-2.03; P = 0.05). CONCLUSION: The underlying diabetes significantly increases the mortality among patients with Covid-19, so special care should be taken for this high risk group if they develop Covid-19.

8.
2nd South American Conference on Industrial Engineering and Operations Management, IEOM 2021 ; : 2425-2431, 2021.
Article in English | Scopus | ID: covidwho-1589813

ABSTRACT

The SARS-CoV-2 vaccination plan development in Colombia, set to begin in February 2021, included a comprehensive assessment of the spread to set population priorities in rank-ordered phases. In Phase 3 of the plan, populations between 16 and 59 years with a set of specific comorbidities will be vaccinated. Our study aims to evaluate the comorbidities incidence in the survival probability to assess the population at most risk if infected and assist in the assignation on this phase. In this study, multivariate Cox regression and Kaplan-Meier curves were performed to determine risk predictors of mortality for 610 reports of up to 15-day decay non-survivor SARS-CoV-2 infected in Colombia. After implementation, higher hazard ratios were associated with diabetes. Kaplan-Meier curves indicate that patients with diabetes that have an older age and hypertension are at a higher risk of earlier death. © IEOM Society International.

9.
Sensors (Basel) ; 21(24)2021 Dec 20.
Article in English | MEDLINE | ID: covidwho-1580509

ABSTRACT

The coronavirus disease 2019 (COVID-19) pandemic has affected hundreds of millions of individuals and caused millions of deaths worldwide. Predicting the clinical course of the disease is of pivotal importance to manage patients. Several studies have found hematochemical alterations in COVID-19 patients, such as inflammatory markers. We retrospectively analyzed the anamnestic data and laboratory parameters of 303 patients diagnosed with COVID-19 who were admitted to the Polyclinic Hospital of Bari during the first phase of the COVID-19 global pandemic. After the pre-processing phase, we performed a survival analysis with Kaplan-Meier curves and Cox Regression, with the aim to discover the most unfavorable predictors. The target outcomes were mortality or admission to the intensive care unit (ICU). Different machine learning models were also compared to realize a robust classifier relying on a low number of strongly significant factors to estimate the risk of death or admission to ICU. From the survival analysis, it emerged that the most significant laboratory parameters for both outcomes was C-reactive protein min; HR=17.963 (95% CI 6.548-49.277, p < 0.001) for death, HR=1.789 (95% CI 1.000-3.200, p = 0.050) for admission to ICU. The second most important parameter was Erythrocytes max; HR=1.765 (95% CI 1.141-2.729, p < 0.05) for death, HR=1.481 (95% CI 0.895-2.452, p = 0.127) for admission to ICU. The best model for predicting the risk of death was the decision tree, which resulted in ROC-AUC of 89.66%, whereas the best model for predicting the admission to ICU was support vector machine, which had ROC-AUC of 95.07%. The hematochemical predictors identified in this study can be utilized as a strong prognostic signature to characterize the severity of the disease in COVID-19 patients.


Subject(s)
COVID-19 , Hospital Mortality , Humans , Machine Learning , Prognosis , Retrospective Studies , SARS-CoV-2 , Survival Analysis
10.
Stat Methods Med Res ; : 9622802211023955, 2021 Dec 21.
Article in English | MEDLINE | ID: covidwho-1582665

ABSTRACT

Time-to-event data are right-truncated if only individuals who have experienced the event by a certain time can be included in the sample. For example, we may be interested in estimating the distribution of time from onset of disease symptoms to death and only have data on individuals who have died. This may be the case, for example, at the beginning of an epidemic. Right truncation causes the distribution of times to event in the sample to be biased towards shorter times compared to the population distribution, and appropriate statistical methods should be used to account for this bias. This article is a review of such methods, particularly in the context of an infectious disease epidemic, like COVID-19. We consider methods for estimating the marginal time-to-event distribution, and compare their efficiencies. (Non-)identifiability of the distribution is an important issue with right-truncated data, particularly at the beginning of an epidemic, and this is discussed in detail. We also review methods for estimating the effects of covariates on the time to event. An illustration of the application of many of these methods is provided, using data on individuals who had died with coronavirus disease by 5 April 2020.

11.
Open Med (Wars) ; 16(1): 692-695, 2021.
Article in English | MEDLINE | ID: covidwho-1207665

ABSTRACT

OBJECTIVE: Over 90% of the COVID-19 patients with computed tomographic (CT) manifestations showed radiological improvement on dissipating stage. Cases with refractory pulmonary infiltration were discussed in this study. METHODS: During hospitalization, chest CT scan and reverse transcriptase polymerase chain reaction (RT-PCR) test were repeatedly performed. While drawing parallels to RT-PCR, the impact of delayed absorption of lung lesions on length of hospital stay (LOS) and medical expense was investigated. Features for delayed absorption of lung lesions were identified using cox proportional hazard regression model. RESULTS: Cases with delayed absorption of lung lesions had a prolonged LOS (18.00 ± 4.90 vs 9.25 ± 4.20, p < 0.01) and increased medical expense (9124.55 ± 2421.31 vs 4923.88 ± 2218.56, p < 0.01). Time interval from admission to a negative RT-PCR (ATN) was also prolonged (13.29 ± 4.72 vs 9.25 ± 4.20, p = 0.03). The cox proportional hazard regression model indicated that imported cases bore high risk of delayed absorption of lung lesions (hazard ratio = 2.54, 95% confidence interval 1.05, 6.11, p = 0.04). Sensitivity analysis revealed similar pattern (hazard ratio = 6.64, 95% confidence interval 1.62, 27.18, p = 0.01). CONCLUSION: Imported cases of COVID-19 were more likely to have refractory pulmonary infiltration, which subsequently prolongs LOS and increases medical expense.

12.
Pattern Anal Appl ; 24(3): 993-1005, 2021.
Article in English | MEDLINE | ID: covidwho-1092688

ABSTRACT

Coronavirus (COVID-19) is one of the most serious problems that has caused stopping the wheel of life all over the world. It is widely spread to the extent that hospital places are not available for all patients. Therefore, most hospitals accept patients whose recovery rate is high. Machine learning techniques and artificial intelligence have been deployed for computing infection risks, performing survival analysis and classification. Survival analysis (time-to-event analysis) is widely used in many areas such as engineering and medicine. This paper presents two systems, Cox_COVID_19 and Deep_ Cox_COVID_19 that are based on Cox regression to study the survival analysis for COVID-19 and help hospitals to choose patients with better chances of survival and predict the most important symptoms (features) affecting survival probability. Cox_COVID_19 is based on Cox regression and Deep_Cox_COVID_19 is a combination of autoencoder deep neural network and Cox regression to enhance prediction accuracy. A clinical dataset for COVID-19 patients is used. This dataset consists of 1085 patients. The results show that applying an autoencoder on the data to reconstruct features, before applying Cox regression algorithm, would improve the results by increasing concordance, accuracy and precision. For Deep_ Cox_COVID_19 system, it has a concordance of 0.983 for training and 0.999 for testing, but for Cox_COVID_19 system, it has a concordance of 0.923 for training and 0.896 for testing. The most important features affecting mortality are, age, muscle pain, pneumonia and throat pain. Both Cox_COVID_19 and Deep_ Cox_COVID_19 prediction systems can predict the survival probability and present significant symptoms (features) that differentiate severe cases and death cases. But the accuracy of Deep_Cox_Covid_19 outperforms that of Cox_Covid_19. Both systems can provide definite information for doctors about detection and intervention to be taken, which can reduce mortality.

13.
Influenza Other Respir Viruses ; 15(3): 371-380, 2021 05.
Article in English | MEDLINE | ID: covidwho-1066700

ABSTRACT

BACKGROUND: The population of adult residential care homes has been shown to have high morbidity and mortality in relation to COVID-19. METHODS: We examined 3115 hospital discharges to a national cohort of 1068 adult care homes and subsequent outbreaks of COVID-19 occurring between 22 February and 27 June 2020. A Cox proportional hazards regression model was used to assess the impact of time-dependent exposure to hospital discharge on incidence of the first known outbreak, over a window of 7-21 days after discharge, and adjusted for care home characteristics, including size and type of provision. RESULTS: A total of 330 homes experienced an outbreak, and 544 homes received a discharge over the study period. Exposure to hospital discharge was not associated with a significant increase in the risk of a new outbreak (hazard ratio 1.15, 95% CI 0.89, 1.47, P = .29) after adjusting for care home characteristics. Care home size was the most significant predictor. Hazard ratios (95% CI) in comparison with homes of <10 residents were as follows: 3.40 (1.99, 5.80) for 10-24 residents; 8.25 (4.93, 13.81) for 25-49 residents; and 17.35 (9.65, 31.19) for 50+ residents. When stratified for care home size, the outbreak rates were similar for periods when homes were exposed to a hospital discharge, in comparison with periods when homes were unexposed. CONCLUSION: Our analyses showed that large homes were at considerably greater risk of outbreaks throughout the epidemic, and after adjusting for care home size, a discharge from hospital was not associated with a significant increase in risk.


Subject(s)
COVID-19/epidemiology , Disease Outbreaks , Nursing Homes , SARS-CoV-2 , Cohort Studies , Humans , Patient Discharge , Proportional Hazards Models
14.
Respir Res ; 21(1): 169, 2020 Jul 03.
Article in English | MEDLINE | ID: covidwho-630307

ABSTRACT

BACKGROUND: Since December 2019, the outbreak of COVID-19 caused a large number of hospital admissions in China. Many patients with COVID-19 have symptoms of acute respiratory distress syndrome, even are in danger of death. This is the first study to evaluate dynamic changes of D-Dimer and Neutrophil-Lymphocyte Count Ratio (NLR) as a prognostic utility in patients with COVID-19 for clinical use. METHODS: In a retrospective study, we collected data from 349 hospitalized patients who diagnosed as the infection of the COVID-19 in Wuhan Pulmonary Hospital. We used ROC curves and Cox regression analysis to explore critical value (optimal cut-off point associated with Youden index) and prognostic role of dynamic changes of D-Dimer and NLR. RESULTS: Three hundred forty-nine participants were enrolled in this study and the mortality rate of the patients with laboratory diagnosed COVID-19 was 14.9%. The initial and peak value of D-Dimer and NLR in deceased patients were higher statistically compared with survivors (P < 0.001). There was a more significant upward trend of D-Dimer and NLR during hospitalization in the deceased patients, initial D-Dimer and NLR were lower than the peak tests (MD) -25.23, 95% CI: - 31.81- -18.64, P < 0.001; (MD) -43.73, 95% CI:-59.28- -31.17, P < 0.001. The test showed a stronger correlation between hospitalization days, PCT and peak D-Dimer than initial D-Dimer. The areas under the ROC curves of peak D-Dimer and peak NLR tests were higher than the initial tests (0.94(95%CI: 0.90-0.98) vs. 0.80 (95% CI: 0.73-0.87); 0.93 (95%CI:0.90-0.96) vs. 0.86 (95%CI:0.82-0.91). The critical value of initial D-Dimer, peak D-Dimer, initial NLR and peak NLR was 0.73 mg/L, 3.78 mg/L,7.13 and 14.31 respectively. 35 (10.03%) patients were intubated. In the intubated patients, initial and peak D-Dimer and NLR were much higher than non-intubated patients (P < 0.001). The critical value of initial D-Dimer, peak D-Dimer, initial NLR and peak NLR in prognosticate of intubation was 0.73 mg/L, 12.75 mg/L,7.28 and 27.55. The multivariable Cox regression analysis showed that age (HR 1.04, 95% CI 1.00-1.07, P = 0.01), the peak D-Dimer (HR 1.03, 95% CI 1.01-1.04, P < 0.001) were prognostic factors for COVID-19 patients' death. CONCLUSIONS: To dynamically observe the ratio of D-Dimer and NLR was more valuable during the prognosis of COVID-19. The rising trend in D-Dimer and NLR, or the test results higher than the critical values may indicate a risk of death for participants with COVID-19.


Subject(s)
Coronavirus Infections/blood , Coronavirus Infections/epidemiology , Fibrin Fibrinogen Degradation Products/analysis , Lymphocyte Count , Neutrophils , Pneumonia, Viral/blood , Pneumonia, Viral/epidemiology , Adult , Aged , Biomarkers/blood , COVID-19 , Cohort Studies , Coronavirus Infections/diagnosis , Female , Hospitals, Special , Humans , Leukocyte Count , Male , Middle Aged , Pandemics , Pneumonia, Viral/diagnosis , Prognosis , Proportional Hazards Models , ROC Curve , Retrospective Studies , Severity of Illness Index , Survival Rate
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