PeerJ:Infectious Diseaseshttps://peerj.com/articles/index.atom?journal=peerj&subject=5130Infectious Diseases articles published in PeerJImpact of the coronavirus disease 2019 pandemic on the diversity of notifiable infectious diseases: a case study in Shanghai, Chinahttps://peerj.com/articles/171242024-03-122024-03-12Yongfang ZhangWenli Feng
The outbreak of coronavirus disease 2019 (COVID-19) has not only posed significant challenges to public health but has also impacted every aspect of society and the environment. In this study, we propose an index of notifiable disease outbreaks (NDOI) to assess the impact of COVID-19 on other notifiable diseases in Shanghai, China. Additionally, we identify the critical factors influencing these diseases using multivariate statistical analysis. We collected monthly data on 34 notifiable infectious diseases (NIDs) and corresponding environmental and socioeconomic factors (17 indicators) from January 2017 to December 2020. The results revealed that the total number of cases and NDOI of all notifiable diseases decreased by 47.1% and 52.6%, respectively, compared to the period before the COVID-19 pandemic. Moreover, the COVID-19 pandemic has led to improved air quality as well as impacted the social economy and human life. Redundancy analysis (RDA) showed that population mobility, particulate matter (PM2.5), atmospheric pressure, and temperature were the primary factors influencing the spread of notifiable diseases. The NDOI is beneficial in establishing an early warning system for infectious disease epidemics at different scales. Furthermore, our findings also provide insight into the response mechanisms of notifiable diseases influenced by social and environmental factors.
The outbreak of coronavirus disease 2019 (COVID-19) has not only posed significant challenges to public health but has also impacted every aspect of society and the environment. In this study, we propose an index of notifiable disease outbreaks (NDOI) to assess the impact of COVID-19 on other notifiable diseases in Shanghai, China. Additionally, we identify the critical factors influencing these diseases using multivariate statistical analysis. We collected monthly data on 34 notifiable infectious diseases (NIDs) and corresponding environmental and socioeconomic factors (17 indicators) from January 2017 to December 2020. The results revealed that the total number of cases and NDOI of all notifiable diseases decreased by 47.1% and 52.6%, respectively, compared to the period before the COVID-19 pandemic. Moreover, the COVID-19 pandemic has led to improved air quality as well as impacted the social economy and human life. Redundancy analysis (RDA) showed that population mobility, particulate matter (PM2.5), atmospheric pressure, and temperature were the primary factors influencing the spread of notifiable diseases. The NDOI is beneficial in establishing an early warning system for infectious disease epidemics at different scales. Furthermore, our findings also provide insight into the response mechanisms of notifiable diseases influenced by social and environmental factors.Prognostic factors and outcomes of invasive pulmonary aspergillosis, a retrospective hospital-based studyhttps://peerj.com/articles/170662024-02-282024-02-28Wei-Che ChenI-Chieh ChenJun-Peng ChenTsai-Ling LiaoYi-Ming Chen
Objective
Invasive pulmonary aspergillosis (IPA) affects immunocompromised hosts and is associated with higher risks of respiratory failure and mortality. However, the clinical outcomes of different IPA types have not been identified.
Methods
Between September 2002 and May 2021, we retrospectively enrolled patients with IPA in Taichung Veterans General Hospital, Taiwan. Cases were classified as possible IPA, probable IPA, proven IPA, and putative IPA according to EORTC/MSGERC criteria and the AspICU algorithm. Risk factors of respiratory failure, kidney failure, and mortality were analyzed by logistic regression. A total of 3-year survival was assessed by the Kaplan-Meier method with log-rank test for post-hoc comparisons.
Results
We included 125 IPA patients (50: possible IPA, 47: probable IPA, 11: proven IPA, and 17: putative IPA). Comorbidities of liver cirrhosis and solid organ malignancy were risk factors for respiratory failure; diabetes mellitus and post-liver or kidney transplantation were related to kidney failure. Higher galactomannan (GM) test optical density index (ODI) in either serum or bronchoalveolar lavage fluid was associated with dismal outcomes. Probable IPA and putative IPA had lower 3-year respiratory failure-free survival compared to possible IPA. Probable IPA and putative IPA exhibited lower 3-year renal failure-free survival in comparison to possible IPA and proven IPA. Putative IPA had the lowest 3-year overall survival rates among the four IPA groups.
Conclusion
Patients with putative IPA had higher mortality rates than the possible, probable, or proven IPA groups. Therefore, a prompt diagnosis and timely treatment are warranted for patients with putative IPA.
Objective
Invasive pulmonary aspergillosis (IPA) affects immunocompromised hosts and is associated with higher risks of respiratory failure and mortality. However, the clinical outcomes of different IPA types have not been identified.
Methods
Between September 2002 and May 2021, we retrospectively enrolled patients with IPA in Taichung Veterans General Hospital, Taiwan. Cases were classified as possible IPA, probable IPA, proven IPA, and putative IPA according to EORTC/MSGERC criteria and the AspICU algorithm. Risk factors of respiratory failure, kidney failure, and mortality were analyzed by logistic regression. A total of 3-year survival was assessed by the Kaplan-Meier method with log-rank test for post-hoc comparisons.
Results
We included 125 IPA patients (50: possible IPA, 47: probable IPA, 11: proven IPA, and 17: putative IPA). Comorbidities of liver cirrhosis and solid organ malignancy were risk factors for respiratory failure; diabetes mellitus and post-liver or kidney transplantation were related to kidney failure. Higher galactomannan (GM) test optical density index (ODI) in either serum or bronchoalveolar lavage fluid was associated with dismal outcomes. Probable IPA and putative IPA had lower 3-year respiratory failure-free survival compared to possible IPA. Probable IPA and putative IPA exhibited lower 3-year renal failure-free survival in comparison to possible IPA and proven IPA. Putative IPA had the lowest 3-year overall survival rates among the four IPA groups.
Conclusion
Patients with putative IPA had higher mortality rates than the possible, probable, or proven IPA groups. Therefore, a prompt diagnosis and timely treatment are warranted for patients with putative IPA.Exploratory study to characterise the individual types of health literacy and beliefs and their associations with infection prevention behaviours amid the COVID-19 pandemic in Japan: a longitudinal studyhttps://peerj.com/articles/169052024-02-222024-02-22Mao YagihashiMichio MurakamiMai KatoAsayo YamamuraAsako MiuraKei Hirai
Background
During a global infectious disease pandemic such as the coronavirus disease 2019 (COVID-19), individuals’ infection prevention/risk-taking behaviours are likely to differ depending on their health literacy and beliefs regarding the disease. To effectively promote infection prevention behaviours, it is necessary to enable information dissemination and risk communication that consider individuals’ health literacy and beliefs. In this study, we exploratorily characterised segments based on individual health literacy and beliefs regarding COVID-19 among the Japanese during the early stage of the COVID-19 pandemic, and investigated whether infection prevention/risk-taking behaviours and fear of COVID-19 differed among these segments.
Methods
In this study, we conducted two web-based longitudinal surveys in Japan (PHASE 1, 1–30 November 2020, 6,000 participants; PHASE 2, 1–31 December 2020, 3,800 participants). We characterised segments of the target population using cluster analysis on health literacy and beliefs regarding COVID-19 obtained in PHASE 1. We further investigated the associations between the clusters and infection prevention/risk-taking behaviours and fear of COVID-19, obtained from PHASE 2.
Results
Five clusters were identified: ‘Calm/hoax denial’, ‘Hoax affinity/threat denial’, ‘Minority/indifference’, ‘Over vigilance’, and ‘Optimism’. There were significant differences in infection prevention/risk-taking behaviours and fear of COVID-19 among the five clusters. The belief in susceptibility to infection, rather than affinity for hoaxes and conspiracy theories, was coherently associated with infection prevention/risk-taking behaviours and fear of infection across clusters. This study provides foundational knowledge for creating segment-specific public messages and developing interactive risk communication to encourage infection prevention behaviours.
Background
During a global infectious disease pandemic such as the coronavirus disease 2019 (COVID-19), individuals’ infection prevention/risk-taking behaviours are likely to differ depending on their health literacy and beliefs regarding the disease. To effectively promote infection prevention behaviours, it is necessary to enable information dissemination and risk communication that consider individuals’ health literacy and beliefs. In this study, we exploratorily characterised segments based on individual health literacy and beliefs regarding COVID-19 among the Japanese during the early stage of the COVID-19 pandemic, and investigated whether infection prevention/risk-taking behaviours and fear of COVID-19 differed among these segments.
Methods
In this study, we conducted two web-based longitudinal surveys in Japan (PHASE 1, 1–30 November 2020, 6,000 participants; PHASE 2, 1–31 December 2020, 3,800 participants). We characterised segments of the target population using cluster analysis on health literacy and beliefs regarding COVID-19 obtained in PHASE 1. We further investigated the associations between the clusters and infection prevention/risk-taking behaviours and fear of COVID-19, obtained from PHASE 2.
Results
Five clusters were identified: ‘Calm/hoax denial’, ‘Hoax affinity/threat denial’, ‘Minority/indifference’, ‘Over vigilance’, and ‘Optimism’. There were significant differences in infection prevention/risk-taking behaviours and fear of COVID-19 among the five clusters. The belief in susceptibility to infection, rather than affinity for hoaxes and conspiracy theories, was coherently associated with infection prevention/risk-taking behaviours and fear of infection across clusters. This study provides foundational knowledge for creating segment-specific public messages and developing interactive risk communication to encourage infection prevention behaviours.Transmission potential of mpox in mainland China, June-July 2023: estimating reproduction number during the initial phase of the epidemichttps://peerj.com/articles/169082024-02-082024-02-08Andrei R. AkhmetzhanovPei-Hsuan Wu
Despite reporting very few mpox cases in early 2023, mainland China observed a surge of over 500 cases during the summer. Amid ambiguous prevention strategies and stigma surrounding mpox transmission, the epidemic silently escalated. This study aims to quantify the scale of the mpox epidemic and assess the transmission dynamics of the virus by estimating the effective reproduction number (Re) during its early phase. Publicly available data were aggregated to obtain daily mpox case counts in mainland China, and the Re value was estimated using an exponential growth model. The mean Re value was found to be 1.57 (95% credible interval [1.38–1.78]), suggesting a case doubling time of approximately 2 weeks. This estimate was compared with Re values from 16 other countries’ national outbreaks in 2022 that had cumulative case count exceeding 700 symptomatic cases by the end of that year. The Re estimates for these outbreaks ranged from 1.13 for Portugal to 2.31 for Colombia. The pooled mean Re was 1.49 (95% credible interval [1.32–1.67]), which aligns closely with the Re for mainland China. These findings underscore the need for immediate and effective control measures including targeted vaccination campaigns to mitigate the further spread and impact of the epidemic.
Despite reporting very few mpox cases in early 2023, mainland China observed a surge of over 500 cases during the summer. Amid ambiguous prevention strategies and stigma surrounding mpox transmission, the epidemic silently escalated. This study aims to quantify the scale of the mpox epidemic and assess the transmission dynamics of the virus by estimating the effective reproduction number (Re) during its early phase. Publicly available data were aggregated to obtain daily mpox case counts in mainland China, and the Re value was estimated using an exponential growth model. The mean Re value was found to be 1.57 (95% credible interval [1.38–1.78]), suggesting a case doubling time of approximately 2 weeks. This estimate was compared with Re values from 16 other countries’ national outbreaks in 2022 that had cumulative case count exceeding 700 symptomatic cases by the end of that year. The Re estimates for these outbreaks ranged from 1.13 for Portugal to 2.31 for Colombia. The pooled mean Re was 1.49 (95% credible interval [1.32–1.67]), which aligns closely with the Re for mainland China. These findings underscore the need for immediate and effective control measures including targeted vaccination campaigns to mitigate the further spread and impact of the epidemic.Mathematical model of voluntary vaccination against schistosomiasishttps://peerj.com/articles/168692024-02-072024-02-07Santiago LopezSamiya MajidRida SyedJan RychtarDewey Taylor
Human schistosomiasis is a chronic and debilitating neglected tropical disease caused by parasitic worms of the genus Schistosoma. It is endemic in many countries in sub-Saharan Africa. Although there is currently no vaccine available, vaccines are in development. In this paper, we extend a simple compartmental model of schistosomiasis transmission by incorporating the vaccination option. Unlike previous models of schistosomiasis transmission that focus on control and treatment at the population level, our model focuses on incorporating human behavior and voluntary individual vaccination. We identify vaccination rates needed to achieve herd immunity as well as optimal voluntary vaccination rates. We demonstrate that the prevalence remains too high (higher than 1%) unless the vaccination costs are sufficiently low. Thus, we can conclude that voluntary vaccination (with or without mass drug administration) may not be sufficient to eliminate schistosomiasis as a public health concern. The cost of the vaccine (relative to the cost of schistosomiasis infection) is the most important factor determining whether voluntary vaccination can yield elimination of schistosomiasis. When the cost is low, the optimal voluntary vaccination rate is high enough that the prevalence of schistosomiasis declines under 1%. Once the vaccine becomes available for public use, it will be crucial to ensure that the individuals have as cheap an access to the vaccine as possible.
Human schistosomiasis is a chronic and debilitating neglected tropical disease caused by parasitic worms of the genus Schistosoma. It is endemic in many countries in sub-Saharan Africa. Although there is currently no vaccine available, vaccines are in development. In this paper, we extend a simple compartmental model of schistosomiasis transmission by incorporating the vaccination option. Unlike previous models of schistosomiasis transmission that focus on control and treatment at the population level, our model focuses on incorporating human behavior and voluntary individual vaccination. We identify vaccination rates needed to achieve herd immunity as well as optimal voluntary vaccination rates. We demonstrate that the prevalence remains too high (higher than 1%) unless the vaccination costs are sufficiently low. Thus, we can conclude that voluntary vaccination (with or without mass drug administration) may not be sufficient to eliminate schistosomiasis as a public health concern. The cost of the vaccine (relative to the cost of schistosomiasis infection) is the most important factor determining whether voluntary vaccination can yield elimination of schistosomiasis. When the cost is low, the optimal voluntary vaccination rate is high enough that the prevalence of schistosomiasis declines under 1%. Once the vaccine becomes available for public use, it will be crucial to ensure that the individuals have as cheap an access to the vaccine as possible.Automatic detection of Opisthorchis viverrini egg in stool examination using convolutional-based neural networkshttps://peerj.com/articles/167732024-01-302024-01-30Tongjit ThanchomnangNatthanai ChaibutrWanchai MaleewongPenchom Janwan
Background
Human opisthorchiasis is a dangerous infectious chronic disease distributed in many Asian areas in the water-basins of large rivers, Siberia, and Europe. The gold standard for human opisthorchiasis laboratory diagnosis is the routine examination of Opisthorchis spp. eggs under a microscope. Manual detection is laborious, time-consuming, and dependent on the microscopist’s abilities and expertise. Automatic screening of Opisthorchis spp. eggs with deep learning techniques is a useful diagnostic aid.
Methods
Herein, we propose a convolutional neural network (CNN) for classifying and automatically detecting O. viverrini eggs from digitized images. The image data acquisition was acquired from infected human feces and was processed using the gold standard formalin ethyl acetate concentration technique, and then captured under the microscope digital camera at 400x. Microscopic images containing artifacts and O.viverrini egg were augmented using image rotation, filtering, noising, and sharpening techniques. This augmentation increased the image dataset from 1 time to 36 times in preparation for the training and validation step. Furthermore, the overall dataset was subdivided into a training-validation and test set at an 80:20 ratio, trained with a five-fold cross-validation to test model stability. For model training, we customized a CNN for image classification. An object detection method was proposed using a patch search algorithm to detect eggs and their locations. A performance matrix was used to evaluate model efficiency after training and IoU analysis for object detection.
Results
The proposed model, initially trained on non-augmented data of artifacts (class 0) and O. viverrini eggs (class 1), showed limited performance with 50.0% accuracy, 25.0% precision, 50.0% recall, and a 33.0% F1-score. After implementing data augmentation, the model significantly improved, reaching 100% accuracy, precision, recall, and F1-score. Stability assessments using 5-fold cross-validation indicated better stability with augmented data, evidenced by an ROC-AUC metric improvement from 0.5 to 1.00. Compared to other models such as ResNet50, InceptionV3, VGG16, DenseNet121, and Xception, the proposed model, with a smaller file size of 2.7 MB, showed comparable perfect performance. In object detection, the augmented data-trained model achieved an IoU score over 0.5 in 139 out of 148 images, with an average IoU of 0.6947.
Conclusion
This study demonstrated the successful application of CNN in classifying and automating the detection of O. viverrini eggs in human stool samples. Our CNN model’s performance metrics and true positive detection rates were outstanding. This innovative application of deep learning can automate and improve diagnostic precision, speed, and efficiency, particularly in regions where O. viverrini infections are prevalent, thereby possibly improving infection sustainable control and treatment program.
Background
Human opisthorchiasis is a dangerous infectious chronic disease distributed in many Asian areas in the water-basins of large rivers, Siberia, and Europe. The gold standard for human opisthorchiasis laboratory diagnosis is the routine examination of Opisthorchis spp. eggs under a microscope. Manual detection is laborious, time-consuming, and dependent on the microscopist’s abilities and expertise. Automatic screening of Opisthorchis spp. eggs with deep learning techniques is a useful diagnostic aid.
Methods
Herein, we propose a convolutional neural network (CNN) for classifying and automatically detecting O. viverrini eggs from digitized images. The image data acquisition was acquired from infected human feces and was processed using the gold standard formalin ethyl acetate concentration technique, and then captured under the microscope digital camera at 400x. Microscopic images containing artifacts and O.viverrini egg were augmented using image rotation, filtering, noising, and sharpening techniques. This augmentation increased the image dataset from 1 time to 36 times in preparation for the training and validation step. Furthermore, the overall dataset was subdivided into a training-validation and test set at an 80:20 ratio, trained with a five-fold cross-validation to test model stability. For model training, we customized a CNN for image classification. An object detection method was proposed using a patch search algorithm to detect eggs and their locations. A performance matrix was used to evaluate model efficiency after training and IoU analysis for object detection.
Results
The proposed model, initially trained on non-augmented data of artifacts (class 0) and O. viverrini eggs (class 1), showed limited performance with 50.0% accuracy, 25.0% precision, 50.0% recall, and a 33.0% F1-score. After implementing data augmentation, the model significantly improved, reaching 100% accuracy, precision, recall, and F1-score. Stability assessments using 5-fold cross-validation indicated better stability with augmented data, evidenced by an ROC-AUC metric improvement from 0.5 to 1.00. Compared to other models such as ResNet50, InceptionV3, VGG16, DenseNet121, and Xception, the proposed model, with a smaller file size of 2.7 MB, showed comparable perfect performance. In object detection, the augmented data-trained model achieved an IoU score over 0.5 in 139 out of 148 images, with an average IoU of 0.6947.
Conclusion
This study demonstrated the successful application of CNN in classifying and automating the detection of O. viverrini eggs in human stool samples. Our CNN model’s performance metrics and true positive detection rates were outstanding. This innovative application of deep learning can automate and improve diagnostic precision, speed, and efficiency, particularly in regions where O. viverrini infections are prevalent, thereby possibly improving infection sustainable control and treatment program.Prevalence of intestinal parasites and comparison of detection techniques for soil-transmitted helminths among newly arrived expatriate labors in Jeddah, Saudi Arabiahttps://peerj.com/articles/168202024-01-262024-01-26Mohammad F. Al-RefaiMajed H. Wakid
Background
Diversity in clinical signs and symptoms are associated with soil transmitted diseases (STD), which are spread to humans by intestinal worms and transmitted in a variety of ways. There is a need for the present study, which aimed to investigate the prevalence of intestinal parasites and to compare between the common detection techniques for soil-transmitted helminths (STHs) among newly arrived expatriate labors in Jeddah, Saudi Arabia.
Methods
A total of 188 stool samples were analyzed by macroscopic examination, and microscopic examination using direct iodine smear and the formal ether sedimentation technique. Trichrome and modified Kinyoun’s stains were used to confirm the morphology of any detected protozoa stages and oocyst of Cryptosporidium, respectively. A chromatographic immunoassay kit was used for Entamoeba histolytica, Giardia lamblia and Cryptosporidium. In addition, real-time PCR was employed only to identify various STHs.
Results
Out of 188, several types of parasites were detected in 35 samples (18.62%), of which some with multiple infections. Nine samples (4.79%) were positive for Entamoeba coli, seven samples (3.72%) for Trichuris trichiura, six samples (3.19%) for Necator americanus, four samples (2.13%) for Strongyloides stercoralis, four samples (2.13%) for Ascaris lumbricoides, four samples (2.13%) for E. histolytica, three samples (1.60%) for Blastocystis hominis and two samples (1.06%) for Ancylostoma duodenale. In comparison between laboratory techniques for STHs, real-time PCR was able to detect the DNA of 19 samples (10.1%) followed by Ritchie sedimentation technique (18, 9.6%), and direct smear (7, 3.7%) (p > 0.05).
Conclusion
The high rate of newly arrived foreign workers infected with intestinal parasites could lead to a risk to society. Continuous and regular surveys are needed to deal with the occurrence of intestinal parasitic infections including STHs. To improve the identification of these infections, we recommend a supporting infrastructure for the application of concentration methods and molecular assays.
Background
Diversity in clinical signs and symptoms are associated with soil transmitted diseases (STD), which are spread to humans by intestinal worms and transmitted in a variety of ways. There is a need for the present study, which aimed to investigate the prevalence of intestinal parasites and to compare between the common detection techniques for soil-transmitted helminths (STHs) among newly arrived expatriate labors in Jeddah, Saudi Arabia.
Methods
A total of 188 stool samples were analyzed by macroscopic examination, and microscopic examination using direct iodine smear and the formal ether sedimentation technique. Trichrome and modified Kinyoun’s stains were used to confirm the morphology of any detected protozoa stages and oocyst of Cryptosporidium, respectively. A chromatographic immunoassay kit was used for Entamoeba histolytica, Giardia lamblia and Cryptosporidium. In addition, real-time PCR was employed only to identify various STHs.
Results
Out of 188, several types of parasites were detected in 35 samples (18.62%), of which some with multiple infections. Nine samples (4.79%) were positive for Entamoeba coli, seven samples (3.72%) for Trichuris trichiura, six samples (3.19%) for Necator americanus, four samples (2.13%) for Strongyloides stercoralis, four samples (2.13%) for Ascaris lumbricoides, four samples (2.13%) for E. histolytica, three samples (1.60%) for Blastocystis hominis and two samples (1.06%) for Ancylostoma duodenale. In comparison between laboratory techniques for STHs, real-time PCR was able to detect the DNA of 19 samples (10.1%) followed by Ritchie sedimentation technique (18, 9.6%), and direct smear (7, 3.7%) (p > 0.05).
Conclusion
The high rate of newly arrived foreign workers infected with intestinal parasites could lead to a risk to society. Continuous and regular surveys are needed to deal with the occurrence of intestinal parasitic infections including STHs. To improve the identification of these infections, we recommend a supporting infrastructure for the application of concentration methods and molecular assays.A dynamic nomogram for predicting 28-day mortality in septic shock: a Chinese retrospective cohort studyhttps://peerj.com/articles/167232024-01-232024-01-23Zhijun XuMan Huang
Background
Septic shock is a severe life-threatening disease, and the mortality of septic shock in China was approximately 37.3% that lacks prognostic prediction model. This study aimed to develop and validate a prediction model to predict 28-day mortality for Chinese patients with septic shock.
Methods
This retrospective cohort study enrolled patients from Intensive Care Unit (ICU) of the Second Affiliated Hospital, School of Medicine, Zhejiang University between December 2020 and September 2021. We collected patients’ clinical data: demographic data and physical condition data on admission, laboratory data on admission and treatment method. Patients were randomly divided into training and testing sets in a ratio of 7:3. Univariate logistic regression was adopted to screen for potential predictors, and stepwise regression was further used to screen for predictors in the training set. Prediction model was constructed based on these predictors. A dynamic nomogram was performed based on the results of prediction model. Using receiver operator characteristic (ROC) curve to assess predicting performance of dynamic nomogram, which were compared with Sepsis Organ Failure Assessment (SOFA) and Acute Physiology and Chronic Health Evaluation II (APACHE II) systems.
Results
A total of 304 patients with septic shock were included, with a 28-day mortality of 25.66%. Systolic blood pressure, cerebrovascular disease, Na, oxygenation index (PaO2/FiO2), prothrombin time, glucocorticoids, and hemodialysis were identified as predictors for 28-day mortality in septic shock patients, which were combined to construct the predictive model. A dynamic nomogram (https://zhijunxu.shinyapps.io/DynNomapp/) was developed. The dynamic nomogram model showed a good discrimination with area under the ROC curve of 0.829 in the training set and 0.825 in the testing set. Additionally, the study suggested that the dynamic nomogram has a good predictive value than SOFA and APACHE II.
Conclusion
The dynamic nomogram for predicting 28-day mortality in Chinese patients with septic shock may help physicians to assess patient survival and optimize personalized treatment strategies for septic shock.
Background
Septic shock is a severe life-threatening disease, and the mortality of septic shock in China was approximately 37.3% that lacks prognostic prediction model. This study aimed to develop and validate a prediction model to predict 28-day mortality for Chinese patients with septic shock.
Methods
This retrospective cohort study enrolled patients from Intensive Care Unit (ICU) of the Second Affiliated Hospital, School of Medicine, Zhejiang University between December 2020 and September 2021. We collected patients’ clinical data: demographic data and physical condition data on admission, laboratory data on admission and treatment method. Patients were randomly divided into training and testing sets in a ratio of 7:3. Univariate logistic regression was adopted to screen for potential predictors, and stepwise regression was further used to screen for predictors in the training set. Prediction model was constructed based on these predictors. A dynamic nomogram was performed based on the results of prediction model. Using receiver operator characteristic (ROC) curve to assess predicting performance of dynamic nomogram, which were compared with Sepsis Organ Failure Assessment (SOFA) and Acute Physiology and Chronic Health Evaluation II (APACHE II) systems.
Results
A total of 304 patients with septic shock were included, with a 28-day mortality of 25.66%. Systolic blood pressure, cerebrovascular disease, Na, oxygenation index (PaO2/FiO2), prothrombin time, glucocorticoids, and hemodialysis were identified as predictors for 28-day mortality in septic shock patients, which were combined to construct the predictive model. A dynamic nomogram (https://zhijunxu.shinyapps.io/DynNomapp/) was developed. The dynamic nomogram model showed a good discrimination with area under the ROC curve of 0.829 in the training set and 0.825 in the testing set. Additionally, the study suggested that the dynamic nomogram has a good predictive value than SOFA and APACHE II.
Conclusion
The dynamic nomogram for predicting 28-day mortality in Chinese patients with septic shock may help physicians to assess patient survival and optimize personalized treatment strategies for septic shock.Comparison design and evaluation power in cohort and self-controlled case series designs for post-authorization vaccine safety studieshttps://peerj.com/articles/167802024-01-232024-01-23Shuntaro SatoYurika KawazoeTomohiro KatsutaHaruhisa Fukuda
Background
Post-authorization safety studies (PASSs) of vaccines are important. PASSs enable the evaluation of association between vaccination and adverse events following immunization through common study designs. Clinical trials during vaccine development typically include a few thousand to 10,000 participants while a PASS might aim to detect a few adverse events per 100,000 vaccine recipients. While all available data may be utilized, prior consideration of power analyses are nonetheless crucial for interpretation in cases where statistically significant differences are not found.
Methods
This research primarily examined cohort study design and self-controlled case series (SCCS) design, estimating the power of a PASS under plausible conditions.
Results
Both the cohort study and SCCS designs necessitated large sample sizes or high event counts to guarantee adequate power. The SCCS design is particularly suited to evaluating rare adverse events. However, extremely rare events may not yield sufficient occurrences, thereby resulting in low power. Although the SCCS design can more efficiently control for time-invariant confounding in principle, it solely estimates relative measures. A cohort study design might be preferred if confounding can be adequately managed as it also estimates absolute measures. It may be an easy decision to use all the data at hand for either design. We found it necessary to estimate the sample size and number of events to be used in the study based on a priori information and anticipated results.
Background
Post-authorization safety studies (PASSs) of vaccines are important. PASSs enable the evaluation of association between vaccination and adverse events following immunization through common study designs. Clinical trials during vaccine development typically include a few thousand to 10,000 participants while a PASS might aim to detect a few adverse events per 100,000 vaccine recipients. While all available data may be utilized, prior consideration of power analyses are nonetheless crucial for interpretation in cases where statistically significant differences are not found.
Methods
This research primarily examined cohort study design and self-controlled case series (SCCS) design, estimating the power of a PASS under plausible conditions.
Results
Both the cohort study and SCCS designs necessitated large sample sizes or high event counts to guarantee adequate power. The SCCS design is particularly suited to evaluating rare adverse events. However, extremely rare events may not yield sufficient occurrences, thereby resulting in low power. Although the SCCS design can more efficiently control for time-invariant confounding in principle, it solely estimates relative measures. A cohort study design might be preferred if confounding can be adequately managed as it also estimates absolute measures. It may be an easy decision to use all the data at hand for either design. We found it necessary to estimate the sample size and number of events to be used in the study based on a priori information and anticipated results.Microparticle-associated tissue factor activity correlates with the inflammatory response in septic disseminated intravascular coagulation patientshttps://peerj.com/articles/166362024-01-082024-01-08Shishuai MengBin XuWei YangMingyan Zhao
Background
Sepsis is often accompanied by the formation of disseminated intravascular coagulation (DIC). Microparticles can exert their procoagulant and proinflammatory properties in a variety of ways. The purpose of this study was to investigate the relationship between microparticle-associated tissue factor activity (TF+-MP activity) and the inflammatory response.
Methods
Data from a total of 31 DIC patients with sepsis and 31 non-DIC patients with sepsis admitted to the ICU of the First Affiliated Hospital of Harbin Medical University from December 2017 to March 2019 were collected. Blood samples were collected and DIC scores were calculated on the day of enrollment. The hospital’s clinical laboratory completed routine blood, procalcitonin, and C-reactive protein tests. TF+-MP activity was measured using a tissue factor-dependent FXa generation assay. Interleukin-1β (IL-1β) and tumor necrosis factor-α (TNF-α) levels were determined using ELISA kits.
Results
Compared with the non-DIC group, the DIC group had higher levels of leukocytes, neutrophils, procalcitonin, C-reactive protein, IL-1β, and TNF-α, and more severe inflammatory reactions. TF+-MP activity in the DIC group was higher than that in the non-DIC group. In sepsis patients, TF+-MP activity was strongly correlated with inflammatory response indices and DIC scores.
Conclusion
TF+-MP activity may play a major role in promoting inflammatory response in septic DIC.
Background
Sepsis is often accompanied by the formation of disseminated intravascular coagulation (DIC). Microparticles can exert their procoagulant and proinflammatory properties in a variety of ways. The purpose of this study was to investigate the relationship between microparticle-associated tissue factor activity (TF+-MP activity) and the inflammatory response.
Methods
Data from a total of 31 DIC patients with sepsis and 31 non-DIC patients with sepsis admitted to the ICU of the First Affiliated Hospital of Harbin Medical University from December 2017 to March 2019 were collected. Blood samples were collected and DIC scores were calculated on the day of enrollment. The hospital’s clinical laboratory completed routine blood, procalcitonin, and C-reactive protein tests. TF+-MP activity was measured using a tissue factor-dependent FXa generation assay. Interleukin-1β (IL-1β) and tumor necrosis factor-α (TNF-α) levels were determined using ELISA kits.
Results
Compared with the non-DIC group, the DIC group had higher levels of leukocytes, neutrophils, procalcitonin, C-reactive protein, IL-1β, and TNF-α, and more severe inflammatory reactions. TF+-MP activity in the DIC group was higher than that in the non-DIC group. In sepsis patients, TF+-MP activity was strongly correlated with inflammatory response indices and DIC scores.
Conclusion
TF+-MP activity may play a major role in promoting inflammatory response in septic DIC.