Development and Validation of a Nomogram for Assessing Survival in Patients With COVID-19 Pneumonia

Development and validation of a prognostic model for assessing long COVID risk following Omicron wave-a large population-based cohort study

BACKGROUND

Long coronavirus disease (COVID) after COVID-19 infection is continuously threatening the health of people all over the world. Early prediction of the risk of Long COVID in hospitalized patients will help clinical management of COVID-19, but there is still no reliable and effective prediction model.

METHODS

A total of 1905 hospitalized patients with COVID-19 infection were included in this study, and their Long COVID status was followed up 4-8 weeks after discharge. Univariable and multivariable logistic regression analysis were used to determine the risk factors for Long COVID. Patients were randomly divided into a training cohort (70%) and a validation cohort (30%), and factors for constructing the model were screened using Lasso regression in the training cohort. Visualize the Long COVID risk prediction model using nomogram. Evaluate the performance of the model in the training and validation cohort using the area under the curve (AUC), calibration curve, and decision curve analysis (DCA).

RESULTS

A total of 657 patients (34.5%) reported that they had symptoms of long COVID. The most common symptoms were fatigue or muscle weakness (16.8%), followed by sleep difficulties (11.1%) and cough (9.5%). The risk prediction nomogram of age, diabetes, chronic kidney disease, vaccination status, procalcitonin, leukocytes, lymphocytes, interleukin-6 and D-dimer were included for early identification of high-risk patients with Long COVID. AUCs of the model in the training cohort and validation cohort are 0.762 and 0.713, respectively, demonstrating relatively high discrimination of the model. The calibration curve further substantiated the proximity of the nomogram's predicted outcomes to the ideal curve, the consistency between the predicted outcomes and the actual outcomes, and the potential benefits for all patients as indicated by DCA. This observation was further validated in the validation cohort.

CONCLUSIONS

We established a nomogram model to predict the long COVID risk of hospitalized patients with COVID-19, and proved its relatively good predictive performance. This model is helpful for the clinical management of long COVID.

Development and validation of a nomogram for predicting in-hospital survival rates of patients with COVID-19

Objective

Our aim was to develop and validate a nomogram for predicting the in-hospital 14-day (14 d) and 28-day (28 d) survival rates of patients with coronavirus disease 2019 (COVID-19).

Methods

Clinical data of patients with COVID-19 admitted to the Renmin Hospital of Wuhan University from December 2022 to February 2023 and the north campus of Shanghai Ninth People's Hospital from April 2022 to June 2022 were collected. A total of 408 patients from Renmin Hospital of Wuhan University were selected as the training cohort, and 151 patients from Shanghai Ninth People's Hospital were selected as the verification cohort. Independent variables were screened using Cox regression analysis, and a nomogram was constructed using R software. The prediction accuracy of the nomogram was evaluated using the receiver operating characteristic (ROC) curve, C-index, and calibration curve. Decision curve analysis was used to evaluate the clinical application value of the model. The nomogram was externally validated using a validation cohort.

Result

In total, 559 patients with severe/critical COVID-19 were included in this study, of whom 179 (32.02 %) died. Multivariate Cox regression analysis showed that age >80 years [hazard ratio (HR) = 1.539, 95 % confidence interval (CI): 1.027-2.306, P = 0.037], history of diabetes (HR = 1.741, 95 % CI: 1.253-2.420, P = 0.001), high APACHE II score (HR = 1.083, 95 % CI: 1.042-1.126, P < 0.001), sepsis (HR = 2.387, 95 % CI: 1.707-3.338, P < 0.001), high neutrophil-to-lymphocyte ratio (NLR) (HR = 1.010, 95 % CI: 1.003-1.017, P = 0.007), and high D-dimer level (HR = 1.005, 95 % CI: 1.001-1.009, P = 0.028) were independent risk factors for 14 d and 28 d survival rates, whereas COVID-19 vaccination (HR = 0.625, 95 % CI: 0.440-0.886, P = 0.008) was a protective factor affecting prognosis. ROC curve analysis showed that the area under the curve (AUC) of the 14 d and 28 d hospital survival rates in the training cohort was 0.765 (95 % CI: 0.641-0.923) and 0.814 (95 % CI: 0.702-0.938), respectively, and the AUC of the 14 d and 28 d hospital survival rates in the verification cohort was 0.898 (95 % CI: 0.765-0.962) and 0.875 (95 % CI: 0.741-0.945), respectively. The calibration curves of 14 d and 28 d hospital survival showed that the predicted probability of the model agreed well with the actual probability. Decision curve analysis (DCA) showed that the nomogram has high clinical application value.

Conclusion

In-hospital survival rates of patients with COVID-19 were predicted using a nomogram, which will help clinicians in make appropriate clinical decisions.

Development and validation of a predicting nomogram for in-hospital mortality of COVID-19 Omicron variant: A cohort study of 1324 cases in Beijing Anzhen Hospital

Coronavirus disease 2019 (COVID-19) is continuously posing high global public health concerns due to its high morbidity and mortality. This study aimed to construct a convenient risk model for predicting in-hospital mortality of COVID-19 Omicron variant. A total of 1324 hospitalized patients with Omicron variant were enrolled from Beijing Anzhen Hospital. During hospitalization, the Omicron variant mortality rate was found to be 24.4%. Using the datasets of clinical demographics and laboratory tests, three machine learning algorithms, including best subset selection, stepwise selection, and least absolute shrinkage and selection operator regression analyses were employed to identify the potential predictors of in-hospital mortality. The results found that a panel of twenty-four clinical variables (including age, hyperlipemia, stroke, tumor, and several cardiovascular markers) identified by stepwise selection model exhibited significant performances in predicting the in-hospital mortality of COVID-19. The resultant nomogram showed good discrimination, highlighted by the areas under the curve values of 0.88 for 10 days, 0.81 for 20 days, and 0.82 for 30 days, respectively. Furthermore, decision curve analysis showed a significant reliability and precision for the established stepwise selection model. Collectively, this study developed an accurate and convenience risk model for predicting the in-hospital mortality of COVID-19 Omicron.

Predictive modeling for eosinophilic chronic rhinosinusitis: Nomogram and four machine learning approaches

Eosinophilic chronic rhinosinusitis (ECRS) is a distinct subset of chronic rhinosinusitis characterized by heightened eosinophilic infiltration and increased symptom severity, often resisting standard treatments. Traditional diagnosis requires invasive histological evaluation. This study aims to develop predictive models for ECRS based on patient clinical parameters, eliminating the need for invasive biopsy. Utilizing logistic regression with lasso regularization, random forest (RF), gradient-boosted decision tree (GBDT), and deep neural network (DNN), we trained models on common clinical data. The predictive performance was evaluated using metrics such as area under the curve (AUC) for receiver operator characteristics, decision curves, and feature ranking analysis. In a cohort of 437 eligible patients, the models identified peripheral blood eosinophil ratio, absolute peripheral blood eosinophil, and the ethmoidal/maxillary sinus density ratio (E/M) on computed tomography as crucial predictors for ECRS. This predictive model offers a valuable tool for identifying ECRS without resorting to histological biopsy, enhancing clinical decision-making.

Construction and validation of a nomogram for predicting diabetes remission at 3 months after bariatric surgery in patients with obesity combined with type 2 diabetes mellitus

AIM

Bariatric metabolic surgery (BMS) is a proven treatment option for patients with both obesity and type 2 diabetes mellitus (T2DM). However, there is a lack of comprehensive reporting on the short-term remission rates of diabetes, and the existing data are inadequate. Hence, this study aimed to investigate the factors that may contribute to diabetes remission (DR) in patients with obesity and T2DM, 3 months after undergoing BMS. Furthermore, our objective was to develop a risk-predicting model using a nomogram.

METHODS

In total, 389 patients with obesity and T2DM, who had complete preoperative information and underwent either laparoscopic sleeve gastrectomy or laparoscopic gastric bypass surgery between January 2014 and May 2023, were screened in the Chinese Obesity and Metabolic Surgery Database. The patients were randomly divided into a training set (n = 272) and a validation set (n = 117) in a 7:3 ratio. Potential factors for DR were analysed through univariate and multivariate logistic regression analyses and then modelled using a nomogram. The model's performance was evaluated using receiver operating characteristic curves and the area under the curve (AUC). Calibration plots were used to assess prediction accuracy and decision curve analyses were conducted to evaluate the clinical usefulness of the model.

RESULTS

Glycated haemoglobin, triglycerides, duration of diabetes, insulin requirement and hypercholesterolaemia were identified as independent factors influencing DR. We have incorporated these five indicators into a nomogram, which has shown good efficacy in both the training cohort (AUC = 0.930) and validation cohort (AUC = 0.838). The calibration plots indicated that the model fits well in both the training and the validation cohorts, and decision curve analyses showed that the model had good clinical applicability.

CONCLUSION

The prediction model developed in this study holds predictive value for short-term DR following BMS in patients with obesity and T2DM.

A predictive model for disease severity among COVID-19 elderly patients based on IgG subtypes and machine learning

Objective

Due to the increased likelihood of progression of severe pneumonia, the mortality rate of the elderly infected with coronavirus disease 2019 (COVID-19) is high. However, there is a lack of models based on immunoglobulin G (IgG) subtypes to forecast the severity of COVID-19 in elderly individuals. The objective of this study was to create and verify a new algorithm for distinguishing elderly individuals with severe COVID-19.

Methods

In this study, laboratory data were gathered from 103 individuals who had confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection using a retrospective analysis. These individuals were split into training (80%) and testing cohort (20%) by using random allocation. Furthermore, 22 COVID-19 elderly patients from the other two centers were divided into an external validation cohort. Differential indicators were analyzed through univariate analysis, and variable selection was performed using least absolute shrinkage and selection operator (LASSO) regression. The severity of elderly patients with COVID-19 was predicted using a combination of five machine learning algorithms. Area under the curve (AUC) was utilized to evaluate the performance of these models. Calibration curves, decision curves analysis (DCA), and Shapley additive explanations (SHAP) plots were utilized to interpret and evaluate the model.

Results

The logistic regression model was chosen as the best machine learning model with four principal variables that could predict the probability of COVID-19 severity. In the training cohort, the model achieved an AUC of 0.889, while in the testing cohort, it obtained an AUC of 0.824. The calibration curve demonstrated excellent consistency between actual and predicted probabilities. According to the DCA curve, it was evident that the model provided significant clinical advantages. Moreover, the model performed effectively in an external validation group (AUC=0.74).

Conclusion

The present study developed a model that can distinguish between severe and non-severe patients of COVID-19 in the elderly, which might assist clinical doctors in evaluating the severity of COVID-19 and reducing the bad outcomes of elderly patients.

Progress of the application clinical prediction model in polycystic ovary syndrome

Clinical prediction models play an important role in the field of medicine. These can help predict the probability of an individual suffering from disease, complications, and treatment outcomes by applying specific methodologies. Polycystic ovary syndrome (PCOS) is a common disease with a high incidence rate, huge heterogeneity, short- and long-term complications, and complex treatments. In this systematic review study, we reviewed the progress of clinical prediction models in PCOS patients, including diagnosis and prediction models for PCOS complications and treatment outcomes. We aimed to provide ideas for medical researchers and clues for the management of PCOS. In the future, models with poor accuracy can be greatly improved by adding well-known parameters and validations, which will further expand our understanding of PCOS in terms of precision medicine. By developing a series of predictive models, we can make the definition of PCOS more accurate, which can improve the diagnosis of PCOS and reduce the likelihood of false positives and false negatives. It will also help discover complications earlier and treatment outcomes being known earlier, which can result in better outcomes for women with PCOS.

A nomogram proposal for early prediction of intensive care unit admission in patients with acute antipsychotic poisoning

Background

Early identification of antipsychotic poisoned patients who may have a potential risk for intensive care unit (ICU) admission is crucial especially when resources are limited. Nomograms were previously used as a practical tool to predict prognosis and planning the treatment of some diseases including some poisoning conditions. However, they were not previously investigated in antipsychotic poisoning.

Aim

The current study aimed to construct a nomogram to predict the need for ICU admission in acute antipsychotic poisoning. Patients and methods: This 2-year study included 140 patients acutely intoxicated with antipsychotics and admitted at Tanta University Poison Control Centre throughout July 2019 to June 2021. Personal and toxicological data, findings of clinical examination and electrocardiography, as well as, results of laboratory investigations at time of admission were recorded. According to the outcome, patients were divided into ICU-admitted and ICU-not admitted groups.

Results

The results of this study provided a proposed nomogram that included five significant independent predictors for ICU admission in acute antipsychotic intoxications; the presence of seizures (OR: 31132.26[108.97-Inf]), corrected QT interval (OR: 1.04[1.01-1.09]), mean arterial blood pressure (OR: 0.83[0.70-0.92]), oxygen saturation (OR: 0.62[0.40 to 0.83)], and Glasgow Coma Scale (OR: 0.25 [0.06-0.56]).

Conclusion

It could be concluded that the developed nomogram is a promising tool for easy and rapid decision making to predict the need for ICU admission in acute antipsychotic poisoning.

Clinical characteristics and prognostic nomograms of 12555 non-severe COVID-19 cases with Omicron infection in Shanghai

BACKGROUND

Omicron variant of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has rapidly become a global threat to public health. Numerous asymptomatic and mild cases had been admitted in shelter hospitals to quickly win the fight against Omicron pandemic in Shanghai. However, little is known about influencing factors for deterioration and length of stay (LOS) in hospitals among these non-severe cases.

METHODS

This study included 12,555 non-severe cases with COVID-19 in largest shelter hospital of Shanghai, aiming to explore prognostic factors and build effective models for prediction of LOS.

RESULTS

Data showed that 75.0% of participants were initially asymptomatic. In addition, 94.6% were discharged within 10 days, only 0.3% with deterioration in hospitals. The multivariate analysis indicated that less comorbidities (OR = 1.792, P = 0.012) and booster vaccination (OR = 0.255, P = 0.015) was associated with the decreased risk of deterioration. Moreover, age (HR = 0.991, P < 0.001), number of symptoms (HR = 0.969, P = 0.005), time from diagnosis to admission (HR = 1.013, P = 0.001) and Cycle threshold (CT) values of N gene (HR = 1.081, P < 0.001) were significant factors associated with LOS. Based on these factors, a concise nomogram model for predicting patients discharged within 3 days or more than 10 days was built in the development cohort. In validation cohort, 0.75 and 0.73 of Areas under the curve (AUC) in nomograms, similar with AUC in models of simple machine learning, showed good performance in estimating LOS.

CONCLUSION

Collectively, this study not only provides important evidence to deeply understand clinical characteristics and risk factors of short-term prognosis in Shanghai Omicron outbreaks, but also offers a concise and effective nomogram model to predict LOS. Our findings will play critical roles in screening high-risk groups, providing advice on duration of quarantine and helping decision-makers with better preparation in outbreak of COVID-19.

Predictors of immune persistence induced by two-dose BBIBP-CorV vaccine in high-risk occupational population

BACKGROUND

The immune protection from infection may wane over time as neutralizing antibody levels decline. We aimed to develop a nomogram to predict long-term immune persistence induced by two-dose BBIBP-CorV vaccine and calculate the neutralizing antibody decline probability of individuals.

METHODS

In the initial study, a total of 809 participants were recruited and randomly allocated (1:1:1) to vaccination group with three two-dose schedules on days 0 and 14, 0 and 21, or 0 and 28. The participants with neutralizing antibody titers of 16 or above on day 28 after the second dose were followed up at month 3, 6 and 10. Multivariable Cox proportional hazards regression model and nomogram model were used to identify predictors associated with maintaining of neutralizing antibody levels during 10 months after the second dose.

RESULTS

A total of 744 participants followed up at day 28 after the second dose. The participants with age ≥ 50 (aHR = 3.556, 95 %CI: 1.141-4.884, P = 0.028) were associated with a high risk of response loss (titers < 16). The participants who were in 0-28 d group (aHR = 0.403, 95 %CI: 0.177-0.919, P = 0.031), had an influenza vaccination history (aHR = 0.468, 95 %CI: 0.267-0.921, P = 0.033) or were female (aHR = 0.542, 95 %CI: 0.269-0.935, P = 0.035) tended to maintain immune persistence during 10 months after the second dose. The nomogram was constructed and showed moderate discrimination[C-index:0.711 (95 %CI: 0.652-0.770); AUC: 0.731 (95 %CI: 0.663-0.792)] and good calibration.

CONCLUSIONS

From 28 days to 10 months after receipt of the second dose of the BBIBP-CorV vaccine, neutralizing antibody levels were substantially decreased, especially among men, among persons 50 years of age or older, among persons with the 0-14 d group, and among persons without history of influenza vaccination.

TRIAL REGISTRATION

Chinese Clinical Trial Registry, ChiCTR2100041705, ChiCTR2100041706.

m6A methylation: a potential key player in understanding and treating COVID-2019 infection

Since its discovery in 2019, coronavirus disease 2019 (COVID-2019) spans a wide clinical spectrum from the asymptomatic stage, mild infection, to severe pneumonia. In patients with COVID-2019, factors such as advanced age, diabetes, or hypertension are associated with a significantly increased risk of severe diseases and death. Of note, the mechanisms underlying differences in the risk and symptoms of COVID-2019 among different populations are still poorly characterized. Accordingly, it is imperative to elucidate potential pathophysiological mechanisms and develop targeted therapeutic approaches for COVID-2019 infection. N6-methyladenosine (m6A) is one of the most common modifications in mammalian RNA transcripts and is widely found in messenger RNAs and some non-coding RNAs. It has been reported that m6A methylation modifications are present in viral RNA transcripts, which are of great significance for the regulation of the viral life cycle. Furthermore, m6A methylation has recently been found to be strongly associated with COVID-2019 infection. Therefore, this article reviews recent advances in studies related to the role of m6A methylation in COVID-2019 infection.

Risk factors analysis and risk prediction model construction of non-specific low back pain: an ambidirectional cohort study

PURPOSE

Non-specific low back pain (NLBP) is a common clinical condition that affects approximately 60-80% of adults worldwide. However, there is currently a lack of scientific prediction and evaluation systems in clinical practice. The purpose of this study was to analyze the risk factors of NLBP and construct a risk prediction model.

METHODS

We collected baseline data from 707 patients who met the inclusion criteria and were treated at the Sixth Hospital of Ningbo from December 2020 to December 2022. Logistic regression and LASSO regression were used to screen independent risk factors that influence the onset of NLBP and to construct a risk prediction model. The sensitivity and specificity of the model were evaluated by tenfold cross-validation, and internal validation was performed in the validation set.

RESULTS

Age, gender, BMI, education level, marital status, exercise frequency, history of low back pain, labor intensity, working posture, exposure to vibration sources, and psychological status were found to be significantly associated with the onset of NLBP. Using these 11 predictive factors, a nomogram was constructed, and the area under the ROC curve of the training set was 0.835 (95% CI 0.756-0.914), with a sensitivity of 0.771 and a specificity of 0.800. The area under the ROC curve of the validation set was 0.762 (95% CI 0.665-0.858), with a sensitivity of 0.800 and a specificity of 0.600, indicating that the predictive value of the model for the diagnosis of NLBP was high. In addition, the calibration curve showed a high degree of consistency between the predicted and actual survival probabilities.

CONCLUSION

We have developed a preliminary predictive model for NLBP and constructed a nomogram to predict the onset of NLBP. The model demonstrated good performance and may be useful for the prevention and treatment of NLBP in clinical practice.

Risk factors for hypoxaemia following hip fracture surgery in elderly patients who recovered from COVID-19: a multicentre retrospective study

Objectives

To explore the risk factors associated with postoperative hypoxaemia in elderly patients who have recovered from coronavirus disease (COVID-19) and underwent hip fracture surgery in the short term.

Design

Multicentre retrospective study.

Setting

The study was performed in three first 3A-grade hospitals in China.

Participants

A sequential sampling method was applied to select study participants. Medical records of 392 patients aged ≥65 years who had recovered from COVID-19 and underwent hip fracture surgery at three hospitals in China between 1 November, 2022, and 15 February, 2023, were reviewed.

Interventions

Patients were assigned to hypoxaemia or non-hypoxaemia groups, according to whether hypoxaemia occurred after surgery. Univariate and multivariate logistic regression analyses were used to identify independent risk factors for postoperative hypoxaemia.

Results

The incidence of postoperative hypoxaemia was 38.01%. Statistically significant differences were found between the two groups in terms of age, body mass index (BMI), American Society of Anesthesiologists (ASA) classification, presence of expectoration symptoms, preoperative hypoxaemia, chronic obstructive pulmonary disease, pulmonary inflammation, time between recovery from COVID-19 and surgery, anaesthetic mode, surgical procedure, intraoperative blood loss, intraoperative infusion, duration of surgery, and length of hospital stay (p < 0.05). Furthermore, patients with BMI ≥28.0 kg/m², expectoration symptoms, presence of preoperative hypoxaemia, ASA classification III, time between recovery from COVID-19 and surgery ≤2 weeks, and general anaesthesia were potential risk factors for postoperative hypoxaemia.

Conclusion

Obesity, expectoration symptoms, preoperative hypoxaemia, ASA classification III, time between recovery from COVID-19 and surgery ≤2 weeks, and general anaesthesia were potential risk factors for postoperative hypoxaemia in elderly patients who recovered from COVID-19 and underwent hip fracture surgery in the short term.

Nomogram for predicting the risk of preterm delivery after IVF/ICSI treatment: an analysis of 11513 singleton births

Background

There is a higher risk of preterm delivery (PTD) in singleton live births conceived after in vitro fertilization (IVF)/intracytoplasmic sperm injection (ICSI) compared with spontaneously conceived pregnancies. The objective of our study was to build a predictive nomogram model to suggest the possibility of PTD in singleton pregnancies after IVF/ICSI treatment.

Method

11513 IVF/ICSI cycles with singleton live births were enrolled retrospectively. These cycles were randomly allocated into a training group (80%) and a validation group (20%). We used the multivariate logistics regression analysis to determine prognostic factors for PTD in the training group. A nomogram based on the above factors was further established for predicting PTD. Receiver operating characteristic curves (ROC), areas under the ROC curves (AUC), concordance index (C-index), and calibration plots were analyzed for assessing the performance of this nomogram in the training and validation group.

Results

There were fourteen risk factors significantly related to PTD in IVF/ICSI singleton live births, including maternal body mass index (BMI) > 24 kg/m2, smoking, uterine factors, cervical factors, ovulatory factors, double embryo transferred (DET), blastocyst transfer, FET, vanishing twin syndrome (VTS), obstetric complications (placenta previa, placenta abruption, hypertensive of pregnancies, and premature rupture of membrane), and a male fetus. These factors were further incorporated to construct a nomogram prediction model. The AUC, C-index, and calibration curves indicated that this nomogram exhibited fair performance and good calibration.

Conclusions

We found that the occurrence of PTD increased when women with obesity, smoking, uterine factors, cervical factors, ovulatory factors, DET, VTS, and obstetric complications, and a male fetus. Furthermore, a nomogram was constructed based on the above factors and it might have great value for clinic use.

Pulmonary embolism or COVID-19 pneumonia? A case report

Pulmonary embolism (PE) is the most severe clinical presentation of venous thromboembolism (VTE), which can be challenging to diagnose due to its non-specific symptoms. The overlapping clinical symptoms of Coronavirus disease 2019 (COVID-19) and PE may make distinguishing between the two difficult. Thus, the diagnosis of PE may be delayed or missed, with grave consequences for the patient's outcome and safety. We herein present the case of a 63-year-old Iranian female admitted to our hospital showing symptoms of delirium superimposed on dementia. Soon after her admission, she developed a fever and respiratory symptoms. However, overestimating the likelihood of COVID-19 pneumonia and attributing the patient's symptoms to this disease led to a delayed diagnosis and treatment of pulmonary embolism, resulting in the patient's death. During the COVID-19 pandemic, a high index of suspicion is required for the timely diagnosis of PE, especially in patients with identifiable risk factors. This is specifically true for older patients who cannot express their symptoms due to neurocognitive disorders.

Predicting and Analyzing Restenosis Risk after Endovascular Treatment in Lower Extremity Arterial Disease: Development and Assessment of a Predictive Nomogram

PURPOSE

This study aimed to develop and internally validate nomograms for predicting restenosis after endovascular treatment of lower extremity arterial diseases.

MATERIALS AND METHODS

A total of 181 hospitalized patients with lower extremity arterial disease diagnosed for the first time between 2018 and 2019 were retrospectively collected. Patients were randomly divided into a primary cohort (n=127) and a validation cohort (n=54) at a ratio of 7:3. The least absolute shrinkage and selection operator (LASSO) regression was used to optimize the feature selection of the prediction model. Combined with the best characteristics of LASSO regression, the prediction model was established by multivariate Cox regression analysis. The predictive models' identification, calibration, and clinical practicability were evaluated by the C index, calibration curve, and decision curve. The prognosis of patients with different grades was compared by survival analysis. Internal validation of the model used data from the validation cohort.

RESULTS

The predictive factors included in the nomogram were lesion site, use of antiplatelet drugs, application of drug coating technology, calibration, coronary heart disease, and international normalized ratio (INR). The prediction model demonstrated good calibration ability, and the C index was 0.762 (95% confidence interval: 0.691-0.823). The C index of the validation cohort was 0.864 (95% confidence interval: 0.801-0.927), which also showed good calibration ability. The decision curve shows that when the threshold probability of the prediction model is more significant than 2.5%, the patients benefit significantly from our prediction model, and the maximum net benefit rate is 30.9%. Patients were graded according to the nomogram. Survival analysis found that there was a significant difference in the postoperative primary patency rate between patients of different classifications (log-rank p<0.001) in both the primary cohort and the validation cohort.

CONCLUSION

We developed a nomogram to predict the risk of target vessel restenosis after endovascular treatment by considering information on lesion site, postoperative antiplatelet drugs, calcification, coronary heart disease, drug coating technology, and INR.

CLINICAL IMPACT

Clinicians can grade patients after endovascular procedure according to the scores of the nomograms and apply intervention measures of different intensities for people at different risk levels. During the follow-up process, an individualized follow-up plan can be further formulated according to the risk classification. Identifying and analyzing risk factors is essential for making appropriate clinical decisions to prevent restenosis.

Construction and Validation of a Nomogram Clinical Prediction Model for Predicting Osteoporosis in an Asymptomatic Elderly Population in Beijing

BACKGROUND

Based on the high prevalence and occult-onset of osteoporosis, the development of novel early screening tools was imminent. Therefore, this study attempted to construct a nomogram clinical prediction model for predicting osteoporosis.

METHODS

Asymptomatic elderly residents in the training (n = 438) and validation groups (n = 146) were recruited. BMD examinations were performed and clinical data were collected for the participants. Logistic regression analyses were performed. A logistic nomogram clinical prediction model and an online dynamic nomogram clinical prediction model were constructed. The nomogram model was validated by means of ROC curves, calibration curves, DCA curves, and clinical impact curves.

RESULTS

The nomogram clinical prediction model constructed based on gender, education level, and body weight was well generalized and had moderate predictive value (AUC > 0.7), better calibration, and better clinical benefit. An online dynamic nomogram was constructed.

CONCLUSIONS

The nomogram clinical prediction model was easy to generalize, and could help family physicians and primary community healthcare institutions to better screen for osteoporosis in the general elderly population and achieve early detection and diagnosis of the disease.

Validation of the RIM Score-COVID in the Spanish SEMI-COVID-19 Registry

The significant impact of COVID-19 worldwide has made it necessary to develop tools to identify patients at high risk of severe disease and death. This work aims to validate the RIM Score-COVID in the SEMI-COVID-19 Registry. The RIM Score-COVID is a simple nomogram with high predictive capacity for in-hospital death due to COVID-19 designed using clinical and analytical parameters of patients diagnosed in the first wave of the pandemic. The nomogram uses five variables measured on arrival to the emergency department (ED): age, sex, oxygen saturation, C-reactive protein level, and neutrophil-to-platelet ratio. Validation was performed in the Spanish SEMI-COVID-19 Registry, which included consecutive patients hospitalized with confirmed COVID-19 in Spain. The cohort was divided into three time periods: T1 from February 1 to June 10, 2020 (first wave), T2 from June 11 to December 31, 2020 (second wave, pre-vaccination period), and T3 from January 1 to December 5, 2021 (vaccination period). The model's accuracy in predicting in-hospital COVID-19 mortality was assessed using the area under the receiver operating characteristics curve (AUROC). Clinical and laboratory data from 22,566 patients were analyzed: 15,976 (70.7%) from T1, 4,233 (18.7%) from T2, and 2,357 from T3 (10.4%). AUROC of the RIM Score-COVID in the entire SEMI-COVID-19 Registry was 0.823 (95%CI 0.819-0.827) and was 0.834 (95%CI 0.830-0.839) in T1, 0.792 (95%CI 0.781-0.803) in T2, and 0.799 (95%CI 0.785-0.813) in T3. The RIM Score-COVID is a simple, easy-to-use method for predicting in-hospital COVID-19 mortality that uses parameters measured in most EDs. This tool showed good predictive ability in successive disease waves.

Correlates of Coronavirus Disease 2019 Inpatient Mortality at a Southern California Community Hospital With a Predominantly Hispanic/Latino Adult Population

Background

Studies of inpatient coronavirus disease 2019 (COVID-19) mortality risk factors have mainly used data from academic medical centers or large multihospital databases and have not examined populations with large proportions of Hispanic/Latino patients. In a retrospective cohort study of 4881 consecutive adult COVID-19 hospitalizations at a single community hospital in Los Angeles County with a majority Hispanic/Latino population, we evaluated factors associated with mortality.

Methods

Data on demographic characteristics, comorbidities, laboratory and clinical results, and COVID-19 therapeutics were abstracted from the electronic medical record. Cox proportional hazards regression modeled statistically significant, independently associated predictors of hospital mortality.

Results

Age ≥65 years (hazard ratio [HR] = 2.66; 95% confidence interval [CI] = 1.90-3.72), male sex (HR = 1.31; 95% CI = 1.07-1.60), renal disease (HR = 1.52; 95% CI = 1.18-1.95), cardiovascular disease (HR = 1.45; 95% CI = 1.18-1.78), neurological disease (HR = 1.84; 95% CI = 1.41-2.39), D-dimer ≥500 ng/mL (HR = 2.07; 95% CI = 1.43-3.0), and pulse oxygen level <88% (HR = 1.39; 95% CI = 1.13-1.71) were independently associated with increased mortality. Patient household with (1) multiple COVID-19 cases and (2) Asian, Black, or Hispanic compared with White non-Hispanic race/ethnicity were associated with reduced mortality. In hypoxic COVID-19 inpatients, remdesivir, tocilizumab, and convalescent plasma were associated with reduced mortality, and corticosteroid use was associated with increased mortality.

Conclusions

We corroborate several previously identified mortality risk factors and find evidence that the combination of factors associated with mortality differ between populations.

Development and validation of nomograms for predicting the risk probability of carbapenem resistance and 28-day all-cause mortality in gram-negative bacteremia among patients with hematological diseases

Objectives

Gram-negative bacteria (GNB) bloodstream infections (BSIs) are the most widespread and serious complications in hospitalized patients with hematological diseases. The emergence and prevalence of carbapenem-resistant (CR) pathogens has developed into a considerable challenge in clinical practice. Currently, nomograms have been extensively applied in the field of medicine to facilitate clinical diagnosis and treatment. The purpose of this study was to explore risk indicators predicting mortality and carbapenem resistance in hematological (HM) patients with GNB BSI and to construct two nomograms to achieve personalized prediction.

Methods

A single-center retrospective case-control study enrolled 244 hospitalized HM patients with GNB-BSI from January 2015 to December 2019. The least absolute shrinkage and selection operator (LASSO) regression analysis and multivariate logistic regression analysis were conducted to select potential characteristic predictors of plotting nomograms. Subsequently, to evaluate the prediction performance of the models, the prediction models were internally validated using the bootstrap approach (resampling = 1000) and 10-fold cross validation.

Results

Of all 244 eligible patients with BSI attributed to GNB in this study, 77 (31.6%) were resistant to carbapenems. The rate of carbapenem resistance exhibited a growing tendency year by year, from 20.4% in 2015 to 42.6% in 2019 (p = 0.004). The carbapenem resistance nomogram constructed with the parameters of hypoproteinemia, duration of neutropenia ≥ 6 days, previous exposure to carbapenems, and previous exposure to cephalosporin/β-lactamase inhibitors indicated a favorable discrimination ability with a modified concordance index (C-index) of 0.788 and 0.781 in both the bootstrapping and 10-fold cross validation procedures. The 28-day all-cause mortality was 28.3% (68/240). The prognosis nomogram plotted with the variables of hypoproteinemia, septic shock, isolation of CR-GNB, and the incomplete remission status of underlying diseases showed a superior discriminative ability of poorer clinical prognosis. The modified C-index of the prognosis nomogram was 0.873 with bootstrapping and 0.887 with 10-fold cross validation. The decision curve analysis (DCA) for two nomogram models both demonstrated better clinical practicality.

Conclusions

For clinicians, nomogram models were effective individualized risk prediction tools to facilitate the early identification of HM patients with GNB BSI at high risk of mortality and carbapenem resistance.

Development and validation of a prognostic model based on immune variables to early predict severe cases of SARS-CoV-2 Omicron variant infection

Background

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant has prevailed globally since November 2021. The extremely high transmissibility and occult manifestations were notable, but the severity and mortality associated with the Omicron variant and subvariants cannot be ignored, especially for immunocompromised populations. However, no prognostic model for specially predicting the severity of the Omicron variant infection is available yet. In this study, we aim to develop and validate a prognostic model based on immune variables to early recognize potentially severe cases of Omicron variant-infected patients.

Methods

This was a single-center prognostic study involving patients with SARS-CoV-2 Omicron variant infection. Eligible patients were randomly divided into the training and validation cohorts. Variables were collected immediately after admission. Candidate variables were selected by three variable-selecting methods and were used to construct Cox regression as the prognostic model. Discrimination, calibration, and net benefit of the model were evaluated in both training and validation cohorts.

Results

Six hundred eighty-nine of the involved 2,645 patients were eligible, consisting of 630 non-ICU cases and 59 ICU cases. Six predictors were finally selected to establish the prognostic model: age, neutrophils, lymphocytes, procalcitonin, IL-2, and IL-10. For discrimination, concordance indexes in the training and validation cohorts were 0.822 (95% CI: 0.748-0.896) and 0.853 (95% CI: 0.769-0.942). For calibration, predicted probabilities and observed proportions displayed high agreements. In the 21-day decision curve analysis, the threshold probability ranges with positive net benefit were 0~1 and nearly 0~0.75 in the training and validation cohorts, correspondingly.

Conclusions

This model had satisfactory high discrimination, calibration, and net benefit. It can be used to early recognize potentially severe cases of Omicron variant-infected patients so that they can be treated timely and rationally to reduce the severity and mortality of Omicron variant infection.

A Nomogram to Predict the Overall Survival of Patients With Resected T1-2N0-1M0 Non-Small Cell Lung Cancer and to Identify the Optimal Candidates for Adjuvant Chemotherapy in Stage IB or IIA Non-Small Cell Lung Cancer Patients

BACKGROUND

The benefit of adjuvant chemotherapy for IB/IIA non-small cell lung cancer (NSCLC) patients remains uncertain. This study aimed to develop a prognostic model to predict overall survival in resected NSCLC patients with T1-2N0-1M0 stage and identify optimal candidates for postoperative chemotherapy among those with stage IB or IIA disease.

METHODS

We conducted a retrospective study using the SEER 18 database (2000-2018, November 2020 submission) of patients who underwent radical surgery for T1-2N0-1M0 NSCLC. The patients not receiving adjuvant chemotherapy were randomly divided into training and validation cohorts. A prognostic nomogram was established and evaluated using calibration and receiver operating characteristic curves. Based on the nomogram, stage IB and IIA patients were categorized into two prognostic groups, each further divided into cohorts based on adjuvant chemotherapy status. Kaplan-Meier analysis and log-rank tests were used to compare overall survival between these groups.

RESULTS

A total of 14 789 patients were enrolled and randomly assigned to the training cohort (n = 10 352) and validation cohort (n = 4437). Ten independent prognostic factors were identified and integrated into the prognostic model. The area under the receiver operating characteristic curve was .706, .699, and .705 in the training cohort, and .700, .698, and .695 in the validation cohort at 1, 3, and 5 years, respectively. Among stage IB and IIA patients, only those in the high-risk group showed a significant benefit from adjuvant chemotherapy, with a 16.4% absolute increase in 5-year overall survival.

CONCLUSIONS

The nomogram developed in the study may help physicians choose the most appropriate management strategy for each patient.

The risk of COVID-19 can be predicted by a nomogram based on m6A-related genes

BACKGROUND

The expression of m6A-related genes and their significance in COVID-19 patients are still unknown.

METHODS

The GSE177477 and GSE157103 datasets of the Gene Expression Omnibus were used to extract RNA-seq data. The expression of 26 m6A-related genes and immune cell infiltration in COVID-19 patients were analyzed. Finally, we built and validated a nomogram model to predict the risk of COVID-19 infection.

RESULTS

There were significant differences in 11 m6A regulatory factors between patients with COVID-19 and healthy individuals. The classification of disease subtypes based on m6A-related gene levels can be distinguished. COVID-19 patients in GSE177477 were classified into two categories based on m6A-related genes. The patients in cluster A were all symptomatic, while those in cluster B were asymptomatic. A significant correlation was also found between immune cells and m6A-related genes. Finally, seven m6A-related disease-characteristic genes, HNRNPA2B1, ELAVL1, RBM15, RBM15B, YTHDC1, HNRNPC, and WTAP, were screened to construct a nomogram model for predicting risk. The calibration curve, decision curve analysis, and clinical impact curve analysis were used to show that the nomogram model was effective and had a high net efficacy for risk prediction.

CONCLUSIONS

m6A-related genes were correlated with immune cells. The nomogram model effectively predicted COVID-19 risk. Moreover, m6A-related genes may be associated with the presence or absence of symptoms in COVID-19 patients.

Prognostic risk assessment model and drug sensitivity analysis of colon adenocarcinoma (COAD) based on immune-related lncRNA pairs

PURPOSE

The aim of this study was to identify and screen long non-coding RNA (lncRNA) associated with immune genes in colon cancer, construct immune-related lncRNA pairs, establish a prognostic risk assessment model for colon adenocarcinoma (COAD), and explore prognostic factors and drug sensitivity.

METHOD

Our method was based on data from The Cancer Genome Atlas (TCGA). To begin, we obtained all pertinent demographic and clinical information on 385 patients with COAD. All lncRNAs significantly related to immune genes and with differential expression were identified to construct immune lncRNA pairs. Subsequently, least absolute shrinkage and selection operator and Cox models were used to screen out prognostic-related immune lncRNAs for the establishment of a prognostic risk scoring formula. Finally, We analysed the functional differences between subgroups and screened the drugs, and establish an individual prediction nomogram model.

RESULTS

Our final analysis confirmed eight lncRNA pairs to construct prognostic risk assessment model. Results showed that the high-risk and low-risk groups had significant differences (training (n = 249): p < 0.001, validation (n = 114): p = 0.022). The prognostic model was certified as an independent prognosis model. Compared with the common clinicopathological indicators, the prognostic model had better predictive efficiency (area under the curve (AUC) = 0.805). Finally, We have analysed highly differentiated cellular pathways such as mucosal immune response, identified 9 differential immune cells, 10 sensitive drugs, and establish an individual prediction nomogram model (C-index = 0.820).

CONCLUSION

Our study verified that the eight lncRNA pairs mentioned can be used as biomarkers to predict the prognosis of COAD patients. Identified cells, drugs may have an positive effect on colon cancer prognosis.

Predicting In-Hospital Mortality in Severe COVID-19: A Systematic Review and External Validation of Clinical Prediction Rules

Multiple prediction models for risk of in-hospital mortality from COVID-19 have been developed, but not applied, to patient cohorts different to those from which they were derived. The MEDLINE, EMBASE, Scopus, and Web of Science (WOS) databases were searched. Risk of bias and applicability were assessed with PROBAST. Nomograms, whose variables were available in a well-defined cohort of 444 patients from our site, were externally validated. Overall, 71 studies, which derived a clinical prediction rule for mortality outcome from COVID-19, were identified. Predictive variables consisted of combinations of patients′ age, chronic conditions, dyspnea/taquipnea, radiographic chest alteration, and analytical values (LDH, CRP, lymphocytes, D-dimer); and markers of respiratory, renal, liver, and myocardial damage, which were mayor predictors in several nomograms. Twenty-five models could be externally validated. Areas under receiver operator curve (AUROC) in predicting mortality ranged from 0.71 to 1 in derivation cohorts; C-index values ranged from 0.823 to 0.970. Overall, 37/71 models provided very-good-to-outstanding test performance. Externally validated nomograms provided lower predictive performances for mortality in their respective derivation cohorts, with the AUROC being 0.654 to 0.806 (poor to acceptable performance). We can conclude that available nomograms were limited in predicting mortality when applied to different populations from which they were derived.

Nomogram for predicting the prognosis of tumor patients with sepsis after gastrointestinal surgery

BACKGROUND

There were few studies on the prognosis of tumor patients with sepsis after gastrointestinal surgery and there was no relevant nomogram for predicting the prognosis of these patients.

AIM

To establish a nomogram for predicting the prognosis of tumor patients with sepsis after gastrointestinal surgery in the intensive care unit (ICU).

METHODS

A total of 303 septic patients after gastrointestinal tumor surgery admitted to the ICU at Peking University Cancer Hospital from January 1, 2013 to December 31, 2020 were analysed retrospectively. The model for predicting the prognosis of septic patients was established by the R software package.

RESULTS

The most common infection site of sepsis after gastrointestinal surgery in the ICU was abdominal infection. The 90-d all-cause mortality rate was 10.2% in our study group. In multiple analyses, we found that there were statistically significant differences in tumor type, septic shock, the number of lymphocytes after ICU admission, serum creatinine and total operation times among tumor patients with sepsis after gastrointestinal surgery (P < 0.05). These five variables could be used to establish a nomogram for predicting the prognosis of these septic patients. The nomogram was verified, and the initial C-index was 0.861. After 1000 internal validations of the model, the C-index was 0.876, and the discrimination was good. The correction curve indicated that the actual value was in good agreement with the predicted value.

CONCLUSION

The nomogram based on these five factors (tumor type, septic shock, number of lymphocytes, serum creatinine, and total operation times) could accurately predict the prognosis of tumor patients with sepsis after gastrointestinal surgery.

A systematic review and meta-analysis of the association between the neutrophil, lymphocyte, and platelet count, neutrophil-to-lymphocyte ratio, and platelet-to-lymphocyte ratio and COVID-19 progression and mortality

BACKGROUND AND AIMS

Severe manifestations of coronavirus disease 2019 (COVID-19) are associated with alterations in blood cells that regulate immunity, inflammation, and hemostasis. We conducted an updated systematic review and meta-analysis of the association between the neutrophil, lymphocyte, and platelet count, neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR), and COVID-19 progression and mortality.

METHODS

A systematic literature search was conducted in PubMed, Web of Science, and Scopus for studies published between January 2020 and June 2022.

RESULTS

In 71 studies reporting the investigated parameters within 48 hours of admission, higher NLR (HR 1.21, 95% CI 1.16 to 1.27, p < 0.0001), relative neutrophilia (HR 1.62, 95% CI 1.46 to 1.80, p < 0.0001), relative lymphopenia (HR 1.62, 95% CI 1.27 to 2.08, p < 0.001), and relative thrombocytopenia (HR 1.74, 95% CI 1.36 to 2.22, p < 0.001), but not PLR (p = 0.11), were significantly associated with disease progression and mortality. Between-study heterogeneity was large-to-extreme. The magnitude and direction of the effect size were not modified in sensitivity analysis.

CONCLUSIONS

NLR and neutrophil, lymphocyte, and platelet count significantly discriminate COVID-19 patients with different progression and survival outcomes. (PROSPERO registration number: CRD42021267875).

Prognostic nomograms for lung neuroendocrine carcinomas based on lymph node ratio: a SEER database analysis

Objective

The current study aimed to explore the prognostic value of the lymph node ratio (LNR) in patients with lung neuroendocrine carcinomas (LNECs).

Methods

Data for 1564 elderly patients with LNECs between 1998 and 2016 were obtained from the Surveillance, Epidemiology, and End Results database. The cases were assigned randomly to training (n = 1086) and internal validation (n = 478) sets. The association between LNR and survival was investigated by Cox regression.

Results

Multivariate analyses identified age, tumor grade, summary stage, M stage, surgery, and LNR as independent prognostic factors for both overall survival (OS) and lung cancer-specific survival (LCSS). Tumor size was also a prognostic determinant for LCSS. Prognostic nomograms combining LNR with other informative variables showed good discrimination and calibration abilities in both the training and validation sets. In addition, the C-index of the nomograms was statistically superior to the American Joint Committee on Cancer (AJCC) staging system in both the training and validation cohorts.

Conclusions

These nomograms, based on LNR, showed superior prognostic predictive accuracy compared with the AJCC staging system for predicting OS and LCSS in patients with LNECs.

Sustainable solutions for indoor pollution abatement during COVID phase: A critical study on current technologies & challenges

The appearance of the contagious virus COVID-19, several revelations and environmental health experts punctually predicted the possibly disastrous public health complications of coexisting catching and airborne contamination-arbitrated disease. But much attention has been given on the outdoor-mediated interactions. Almost 3.8 million premature deaths occur every year globally due to the illness from indoor air pollution. Considering the human staying longer span indoors due to restricted human activities or work from home, the indoor air quality (IAQ) might show prominent role for individual health life. Currently, the Environmental Protection Agency (EPA) ensures no regulation of indoor airborne pollution. Herein, the paper underlines the common bases of indoor air pollution, poor IAQ, and impacts of the aerosolized airborne particles on the human health. In order to address these challenges and collective contagion events in indoor environment, several emerging control techniques and preventive sustainable solutions are suggested. By this, more innovations need to be investigated in future to measure the impact of indoor air pollution on individual health.

Constructing prediction models for excessive daytime sleepiness by nomogram and machine learning: A large Chinese multicenter cohort study

Objective

Although risk factors for excessive daytime sleepiness (EDS) have been reported, there are still few cohort-based predictive models for EDS in Parkinson’s disease (PD). This 1-year longitudinal study aimed to develop a predictive model of EDS in patients with PD using a nomogram and machine learning (ML).

Materials and methods

A total of 995 patients with PD without EDS were included, and clinical data during the baseline period were recorded, which included basic information as well as motor and non-motor symptoms. One year later, the presence of EDS in this population was re-evaluated. First, the baseline characteristics of patients with PD with or without EDS were analyzed. Furthermore, a Cox proportional risk regression model and XGBoost ML were used to construct a prediction model of EDS in PD.

Results

At the 1-year follow-up, EDS occurred in 260 of 995 patients with PD (26.13%). Baseline features analysis showed that EDS correlated significantly with age, age of onset (AOO), hypertension, freezing of gait (FOG). In the Cox proportional risk regression model, we included high body mass index (BMI), late AOO, low motor score on the 39-item Parkinson’s Disease Questionnaire (PDQ-39), low orientation score on the Mini-Mental State Examination (MMSE), and absence of FOG. Kaplan–Meier survival curves showed that the survival prognosis of patients with PD in the high-risk group was significantly worse than that in the low-risk group. XGBoost demonstrated that BMI, AOO, PDQ-39 motor score, MMSE orientation score, and FOG contributed to the model to different degrees, in decreasing order of importance, and the overall accuracy of the model was 71.86% after testing.

Conclusion

In this study, we showed that risk factors for EDS in patients with PD include high BMI, late AOO, a low motor score of PDQ-39, low orientation score of MMSE, and lack of FOG, and their importance decreased in turn. Our model can predict EDS in PD with relative effectivity and accuracy.

Low LINC02147 expression promotes the malignant progression of oral submucous fibrosis

BACKGROUND

Key lncRNAs associated with the malignant progression of oral submucous fibrosis (OSF) to oral squamous cell carcinoma (OSCC) were identified.

METHODS

Key lncRNAs with sequential changes from normal oral mucosa (NOM) to OSF to OSCC were identified based on the GEO database. Kaplan-Meier analysis was used to screen lncRNAs related to OSCC prognosis. Cox regression analysis was used to validate the independent prognostic value. qPCR was used to confirm the expression of the candidate lncRNAs. Gene set enrichment analysis (GSEA), nucleocytoplasmic separation assay, fluorescence in situ hybridization, RNA knockdown, western blot, and cell viability assay were performed to investigate the biological functions of the candidate lncRNA. A nomogram was constructed to quantitatively predict OSCC prognosis based on TCGA.

RESULTS

Bioinformatics methods indicated that LINC02147 was sequentially downregulated from NOM to OSF to OSCC, as confirmed by clinical tissues and cells. Meanwhile, low LINC02147 expression, as an independent prognostic factor, predicted a poor prognosis for OSCC. GSEA and in vitro studies suggested that low LINC02147 expression promoted OSF malignant progression by promoting cell proliferation and differentiation. A LINC02147 signature-based nomogram successfully quantified each indicator's contribution to the overall survival of OSCC.

CONCLUSIONS

Low LINC02147 expression promoted OSF malignant progression and predicted poor OSCC prognosis.

An updated review of SARS-CoV-2 detection methods in the context of a novel coronavirus pandemic

The World Health Organization has reported approximately 430 million confirmed cases of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), worldwide, including nearly 6 million deaths, since its initial appearance in China in 2019. While the number of diagnosed cases continues to increase, the need for technologies that can accurately and rapidly detect SARS-CoV-2 virus infection at early phases continues to grow, and the Federal Drug Administration (FDA) has licensed emergency use authorizations (EUAs) for virtually hundreds of diagnostic tests based on nucleic acid molecules and antigen-antibody serology assays. Among them, the quantitative real-time reverse transcription PCR (qRT-PCR) assay is considered the gold standard for early phase virus detection. Unfortunately, qRT-PCR still suffers from disadvantages such as the complex test process and the occurrence of false negatives; therefore, new nucleic acid detection devices and serological testing technologies are being developed. However, because of the emergence of strongly infectious mutants of the new coronavirus, such as Alpha (B.1.1.7), Delta (B.1.617.2), and Omicron (B.1.1.529), the need for the specific detection of mutant strains is also increasing. Therefore, this article reviews nucleic acid- and antigen-antibody-based serological assays, and compares the performance of some of the most recent FDA-approved and literature-reported assays and associated kits for the specific testing of new coronavirus variants.

Learnings from COVID-19 for managing humanitarian supply chains: systematic literature review and future research directions

The COVID-19 pandemic has been experienced as the most significant global disaster after the Spanish flue in 1918. Millions of people lost their life due to a lack of preparedness and ineffective strategies for managing humanitarian supply chains (HSC). Based on the learnings from this pandemic outbreak, different strategies for managing the effective HSC have been explored in the present context of pandemics through a systematic literature review. The findings highlight some of the major challenges faced during the COVID-19 pandemic, such as lack of planning and preparedness, extended shortages of essential lifesaving items, inadequate lab capacity, lack of transparency and visibility, inefficient distribution network, high response time, dependencies on single sourcing for the medical equipment and medicines, lack of the right information on time, and lack of awareness about the protocol for the treatment of the viral disease. Some of the significant learnings observed from this analysis are the use of multiple sourcing of essential items, joint procurement, improving collaboration among all stakeholders, applications of IoT and blockchain technologies for improving tracking and traceability of essential commodities, application of data analytics tools for accurate prediction of next possible COVID wave/disruptions and optimization of distribution network. Limited studies are focused on finding solutions to these problems in managing HSC. Therefore, as a future scope, researchers could find solutions to optimizing the distribution network in context to pandemics, improving tracing and tracking of items during sudden demand, improving trust and collaborations among different agencies involved in HSC.

Two Novel Nomograms Predicting the Risk and Prognosis of Pancreatic Cancer Patients With Lung Metastases: A Population-Based Study

Background

Pancreatic cancer (PC) is one of the most common malignant types of cancer, with the lung being the frequent distant metastatic site. Currently, no population-based studies have been done on the risk and prognosis of pancreatic cancer with lung metastases (PCLM). As a result, we intend to create two novel nomograms to predict the risk and prognosis of PCLM.

Methods

PC patients were selected from the Surveillance, Epidemiology, and End Results Program (SEER) database from 2010 to 2016. A multivariable logistic regression analysis was used to identify risk factors for PCLM at the time of diagnosis. The multivariate Cox regression analysis was carried out to assess PCLM patient's prognostic factors for overall survival (OS). Following that, we used area under curve (AUC), time-dependent receiver operating characteristics (ROC) curves, calibration plots, consistency index (C-index), time-dependent C-index, and decision curve analysis (DCA) to evaluate the effectiveness and accuracy of the two nomograms. Finally, we compared differences in survival outcomes using Kaplan-Meier curves.

Results

A total of 803 (4.22%) out of 19,067 pathologically diagnosed PC patients with complete baseline information screened from SEER database had pulmonary metastasis at diagnosis. A multivariable logistic regression analysis revealed that age, histological subtype, primary site, N staging, surgery, radiotherapy, tumor size, bone metastasis, brain metastasis, and liver metastasis were risk factors for the occurrence of PCLM. According to multivariate Cox regression analysis, age, grade, tumor size, histological subtype, surgery, chemotherapy, liver metastasis, and bone metastasis were independent prognostic factors for PCLM patients' OS. Nomograms were constructed based on these factors to predict 6-, 12-, and 18-months OS of patients with PCLM. AUC, C-index, calibration curves, and DCA revealed that the two novel nomograms had good predictive power.

Conclusion

We developed two reliable predictive models for clinical practice to assist clinicians in developing individualized treatment plans for patients.

A novel reliability-based regression model to analyze and forecast the severity of COVID-19 patients

Background

Coronavirus outbreak (SARS-CoV-2) has become a serious threat to human society all around the world. Due to the rapid rate of disease outbreaks and the severe shortages of medical resources, predicting COVID-19 disease severity continues to be a challenge for healthcare systems. Accurate prediction of severe patients plays a vital role in determining treatment priorities, effective management of medical facilities, and reducing the number of deaths. Various methods have been used in the literature to predict the severity prognosis of COVID-19 patients. Despite the different appearance of the methods, they all aim to achieve generalizable results by increasing the accuracy and reducing the errors of predictions. In other words, accuracy is considered the only effective factor in the generalizability of models. In addition to accuracy, reliability and consistency of results are other critical factors that must be considered to yield generalizable medical predictions. Since the role of reliability in medical decisions is significant, upgrading reliable medical data-driven models requires more attention.

Methods

This paper presents a new modeling technique to specify and maximize the reliability of results in predicting the severity prognosis of COVID-19 patients. We use the well-known classic regression as the basic model to implement our proposed procedure on it. To assess the performance of the proposed model, it has been applied to predict the severity prognosis of COVID-19 by using a dataset including clinical information of 46 COVID-19 patients. The dataset consists of two types of patients’ outcomes including mild (discharge) and severe (ICU or death). To measure the efficiency of the proposed model, we compare the accuracy of the proposed model to the classic regression model.

Results

The proposed reliability-based regression model, by achieving 98.6% sensitivity, 88.2% specificity, and 93.10% accuracy, has better performance than classic accuracy-based regression model with 95.7% sensitivity, 85.5% specificity, and 90.3% accuracy. Also, graphical analysis of ROC curve showed AUC 0.93 (95% CI 0.88–0.98) and AUC 0.90 (95% CI 0.85–0.96) for classic regression models, respectively.

Conclusions

Maximizing reliability in the medical forecasting models can lead to more generalizable and accurate results. The competitive results indicate that the proposed reliability-based regression model has higher performance in predicting the deterioration of COVID-19 patients compared to the classic accuracy-based regression model. The proposed framework can be used as a suitable alternative for the traditional regression method to improve the decision-making and triage processes of COVID-19 patients.

Establishment and Verification of Prognostic Nomograms for Young Women With Breast Cancer Bone Metastasis

Purpose

The prognosis of patients with metastatic breast cancer usually varies greatly among individuals. At present, the application of nomogram is very popular in metastatic tumors. The present study was conducted to identify independent survival predictors and construct nomograms among young women with breast cancer bone metastasis (BCBM).

Patients and Methods

We searched the Surveillance, Epidemiology, and End Results (SEER) database to identify young women diagnosed with BCBM between 2010 and 2016. We first analyzed the potential risk factors of overall survival (OS) and cancer-specific survival (CSS) by applying univariate Cox regression analysis. Then we conducted multivariate Cox analysis to identify independent survival predictors. Based on significant independent predictors, we developed and validated novel prognostic nomograms by using the R version 4.1.0 software.

Results

We finally identified 715 eligible young women with BCBM for survival analysis, of which 358 patients were in the training set, and 357 patients in the validation set. Approximately four-fifths of patients are between 31 and 40 years old. The 5-year OS and CSS rates of this research population were 41.9 and 43.3%, respectively. Multivariate analysis revealed seven independent predictors of both OS and CSS, including race, tumor subtype, tumor size, surgical treatment, brain metastasis, liver metastasis, and lung metastasis. Based on these predictors, we developed and validated OS and CSS nomograms. The C-index of the OS nomogram reached 0.728 and 0.73 in the training and validation sets, respectively. The C-index of the CSS nomogram reached 0.743 and 0.695 in the training and validation sets, respectively. Meanwhile, high quality calibration plots were revealed in both OS and CSS nomograms.

Conclusion

The current novel nomograms can provide an individualized survival evaluation of young women with BCBM and instruct clinicians to treat them appropriately.

Nomogram and Machine Learning Models Predict 1-Year Mortality Risk in Patients With Sepsis-Induced Cardiorenal Syndrome

Objective

Early prediction of long-term outcomes in patients with sepsis-induced cardiorenal syndrome (CRS) remains a great challenge in clinical practice. Herein, we aimed to construct a nomogram and machine learning model for predicting the 1-year mortality risk in patients with sepsis-induced CRS.

Methods

This retrospective study enrolled 340 patients diagnosed with sepsis-induced CRS in Shanghai Tongji Hospital between January 2015 and May 2019, as a discovery cohort. Two predictive models, the nomogram and machine learning model, were used to predict 1-year mortality. The prognostic variables used to develop the nomogram were identified based on a forward stepwise binary logistic regression, and the predictive ability of the nomogram was evaluated by the areas under the receiver operating characteristic curve (AUC) and the calibration curve. Meanwhile, machine learning (ML) techniques, such as support vector machine, random forest (RF), and gradient boosted decision tree, were assessed mainly by accuracy and AUC. Feature ranking analysis was performed using the ML algorithm. Both nomogram and ML models were externally validated by an independent cohort of 103 patients diagnosed with sepsis-induced CRS between June 2019 and December 2020.

Results

Age, sequential sepsis-related organ failure score (SOFA), serum myoglobin (MYO), vasopressor use, and mechanical ventilation were identified as independent risk factors for 1-year mortality in the nomogram predictive model. In the discovery cohort, the nomogram yielded higher AUC for predicting mortality than did the SOFA score (0.855 [95% CI: 0.815–0.895] vs. 0.756 [95% CI: 0.705–0.808]). For ML, the model developed by RF showed the highest accuracy (0.765) and AUC (0.854). In feature ranking analysis, factors such as age, MYO, SOFA score, vasopressor use, and baseline serum creatinine were identified as important features affecting 1-year prognosis. Moreover, the nomogram and RF model both performed well in external validation, with an AUC of 0.877 and 0.863, respectively.

Conclusion

Our nomogram and ML models showed that age, SOFA score, serum MYO levels, and the use of vasopressors during hospitalization were the main factors influencing the risk of long-term mortality. Our models may serve as useful tools for assessing long-term prognosis in patients with sepsis-induced CRS.

A nomogram for predicting paradoxical immune reconstitution inflammatory syndrome associated with cryptococcal meningitis among HIV-infected individuals in China

Background

Cryptococcal meningitis (CM) associated immune reconstitution inflammatory syndrome (CM-IRIS) is the second most common complication in HIV-infected individuals with cryptococcal meningitis, with a reported mortality rate ranging from 8 to 30%. Given the devastating consequences of CM-IRIS related intracranial neuroinflammation and its challenging in diagnosis, we conducted a study to explore the risk factors and the occurrence of paradoxical CM-IRIS in HIV-infected patients, which is of great value for prevention and clinical management.

Methods

We conducted a retrospective cohort study to identify the indicators associated with paradoxical CM-IRIS among 86 HIV-infected patients with CM using univariate and multivariate cox analysis. A nomogram was constructed using selected variables to evaluate the occurrence of paradoxical CM-IRIS at 6 months and 12 months after ART initiation. The discrimination and calibration of the nomogram were assessed by concordance index (C-index) and calibration plots. Decision curves analysis (DCA) were used to evaluate clinical effectiveness of the nomogram. Subsequently, to help clinicians recognize patients at high risk faster, patients were divided into high-risk and low-risk groups according to the best cutoff point identified by X-tile.

Results

Of 86 AIDS patients with CM, 22.1% experienced paradoxical CM-IRIS at a median of 32 days after antiretroviral therapy (ART) initiation. The occurrence of paradoxical CM-IRIS was associated with age, ART initiation within 4 weeks of antifungal treatment, a four-fold increase in CD4 T cell counts, C-reactive protein levels, and hemoglobin levels independently. These five variables were further used to construct a predictive nomogram. The C-index (0.876) showed the favorable discriminative ability of the nomogram. The calibration plot revealed a high consistency between the predicted and actual observations. DCA showed that the nomogram was clinically useful. Risk stratification based on the total score of the nomogram showed well-differentiated in the high-risk and low-risk groups. Clinicians should pay attention to patients with total points high than 273.

Conclusions

We identified the predictive factors of paradoxical CM-IRIS and constructed a nomogram to evaluate the occurrence of paradoxical CM-IRIS in 6 months and 12 months. The nomogram represents satisfactory performance and might be applied clinically to the screening and management of high-risk patients.

Alveolar Arterial Gradient and Respiratory Index in Predicting the Outcome of COVID-19 Patients; a Retrospective Cross-Sectional Study

Introduction

Alveolar arterial (A-a) oxygen gradient and respiratory index can be of immense help for the critical care physician in clinical decision making. This study aimed to evaluate the potential application of A-a oxygen gradient and respiratory index in predicting the survival of COVID-19 patients in intensive care unit (ICU).

Method

This is a retrospective cross-sectional study involving 215 adult patients with COVID-19 disease, admitted to the ICU between 1^st April 2020 and 30 June 2021. Details regarding demographic variables, comorbidities, laboratory and arterial blood gas (ABG) findings were recorded. Alveolar-arterial gradient and respiratory index were calculated and tested as predictors of survival.

Result

The mean age of the patients was 51.92 years (65.6 % male). Hypertension was the most common comorbidity and oxygen via non-rebreathing mask was the most common modality used at the time of ICU admission. Mortality was 28.37% and average length of stay was 12.84 days. Patients who died were older (p=0.02), mostly male (p=0.017), had at least one comorbidity (p<0.001), and higher heart rate and respiratory rate (<0.001 and p=0.03, respectively), lower pH on arterial blood gas (ABG) (p=0.002), higher FiO2 requirement (p<0.001), and increased A-a oxygen gradient on admission compared to survivors. According to receiver operating characteristic (ROC) curve analysis, A-a oxygen gradient and respiratory index were not sensitive or specific in predicting mortality in the studied patient subset.

Conclusion

A-a oxygen gradient and respiratory index calculated at time of admission to ICU in patients with COVID-19 were poor predictors of survival.

Targeting TNF-α for COVID-19: Recent Advanced and Controversies

Recent advances in the pathophysiologic understanding of coronavirus disease 2019 (COVID-19) suggests that cytokine release syndrome (CRS) has an association with the severity of disease, which is characterized by increased tumor necrosis factor α (TNF-α), interleukin (IL)-6, IL-2, IL-7, and IL-10. Hence, managing CRS has been recommended for rescuing severe COVID-19 patients. TNF-α, one of the pro-inflammatory cytokines commonly upregulated in acute lung injury, triggers CRS and facilitates SARS-CoV-2 interaction with angiotensin-converting enzyme 2 (ACE2). TNF-α inhibitors, therefore, may serve as an effective therapeutic strategy for attenuating disease progression in severe SARS-CoV-2 infection. Below, we review the possibilities and challenges of targeting the TNF-α pathway in COVID-19 treatment.

Study on the Collection Efficiency of Bioaerosol Nanoparticles by Andersen-Type Impactors

Airborne transmission is much more common than previously thought. Based on our knowledge about SARS-COV-2 (COVID-19) infection, the aerosol transmission routes for all respiratory infections must be reassessed. Thus far, the COVID-19 outbreak has caused catastrophic public health and economic crises, posing a serious threat to the lives and health of people around the world and directing public attention toward the airborne transmission of pathogens. The novel coronavirus transmission in the form of nanoaerosols in a wider range hinders prevention and early warning efforts. As a classical bioaerosol sampler, the Andersen six-stage sampler is widely used in the collection and research of aerosol particles. In this study, the physical and biological collection efficiency of the six-stage sampler was explored by qPCR and colony counting method. Results showed that the physical collection efficiency reached more than 50% when the particle size was larger than 0.75 μm. However, the overall biological collection efficiency was only 0.25%. In addition, fluorescence microscopy and flow cytometry were used to detect the microbial state after sampling, and the results showed that the proportion of the collected live bacteria was less than 15% of the total. This result is of great significance not only for the application of the Andersen six-stage sampler in collecting nanosized bioaerosols, but also provides reference for the selection of subsequent detection technologies for effective collection.

Prediction and prognosis of adverse maternal and foetal/neonatal outcomes in pulmonary hypertension: an observational study and nomogram construction

BACKGROUND

Pregnant women with pulmonary hypertension (PH) have higher mortality rates and poor foetal/neonatal outcomes. Tools to assess these risk factors are not well established.

METHODS

Predictive and prognostic nomograms were constructed using data from a "Development" cohort of 420 pregnant patients with PH, recorded between January 2009 and December 2018. Logistic regression analysis established models to predict the probability of adverse maternal and foetal/neonatal events and overall survival by Cox analysis. An independent "Validation" cohort comprised data of 273 consecutive patients assessed from January 2019 until May 2022. Nomogram performance was evaluated internally and implemented with online software to increase the ease of use.

RESULTS

Type I respiratory failure, New York Heart Association functional class, N-terminal pro-brain natriuretic peptide [Formula: see text] 1400 ng/L, arrhythmia, and eclampsia with pre-existing hypertension were independent risk factors for maternal mortality or heart failure. Type I respiratory failure, arrhythmia, general anaesthesia for caesarean section, New York Heart Association functional class, and N-terminal pro-brain natriuretic peptide [Formula: see text] 1400 ng/L were independent predictors of pulmonary hypertension survival during pregnancy. For foetal/neonatal adverse clinical events, type I respiratory failure, arrhythmia, general anaesthesia for caesarean section, parity, platelet count, fibrinogen, and left ventricular systolic diameter were important predictors. Nomogram application for the Development and Validation cohorts showed good discrimination and calibration; decision curve analysis demonstrated their clinical utility.

CONCLUSIONS

The nomogram and its online software can be used to analyse individual mortality, heart failure risk, overall survival prediction, and adverse foetal/neonatal clinical events, which may be useful to facilitate early intervention and better survival rates.

Risk factors of falls in elderly patients with visual impairment

OBJECTIVE

To examine the risk factors for falls in elderly patients with visual impairment (VI) and assess the predictive performance of these factors.

METHODS

Between January 2019 and March 2021, a total of 251 elderly patients aged 65-92 years with VI were enrolled and then prospectively followed up for 12 months to evaluate outcomes of accidental falls via telephone interviews. Information of demographics and lifestyle, gait and balance deficits, and ophthalmic and systemic conditions were collected during baseline visits. Forward stepwise multivariable logistic regression analysis was performed to identify independent risk factors of falls in elderly patients with VI, and a derived nomogram was constructed.

RESULTS

A total of 143 falls were reported in 251 elderly patients during follow-up, with an incidence of 56.97%. The risk factors for falls in elderly patients with VI identified by multivariable logistic regression were women [odds ratio (OR), 95% confidence interval (CI): 2.71, 1.40-5.27], smoking (3.57, 1.34-9.48), outdoor activities/3 months (1.31, 1.08-1.59), waking up frequently during the night (2.08, 1.15-3.79), disorders of balance and gait (2.60, 1.29-5.24), glaucoma (3.12, 1.15-8.44), other retinal degenerations (3.31, 1.16-9.43) and best-corrected visual acuity (BCVA) of the better eye (1.79, 1.10-2.91). A nomogram was developed based on the abovementioned multivariate analysis results. The area under receiver operating characteristic curve of the predictive model was 0.779.

CONCLUSIONS

Gender, smoking, outdoor activities, waking up at night, disorders of balance and gait, glaucoma, other retinal degeneration and BCVA of the better eye were independent risk factors for falls in elderly patients with VI. The predictive model and derived nomogram achieved a satisfying prediction of fall risk in these individuals.

A Comparison of XGBoost, Random Forest, and Nomograph for the Prediction of Disease Severity in Patients With COVID-19 Pneumonia: Implications of Cytokine and Immune Cell Profile

BACKGROUND AND AIMS

The aim of this study was to apply machine learning models and a nomogram to differentiate critically ill from non-critically ill COVID-19 pneumonia patients.

METHODS

Clinical symptoms and signs, laboratory parameters, cytokine profile, and immune cellular data of 63 COVID-19 pneumonia patients were retrospectively reviewed. Outcomes were followed up until Mar 12, 2020. A logistic regression function (LR model), Random Forest, and XGBoost models were developed. The performance of these models was measured by area under receiver operating characteristic curve (AUC) analysis.

RESULTS

Univariate analysis revealed that there was a difference between critically and non-critically ill patients with respect to levels of interleukin-6, interleukin-10, T cells, CD4⁺ T, and CD8⁺ T cells. Interleukin-10 with an AUC of 0.86 was most useful predictor of critically ill patients with COVID-19 pneumonia. Ten variables (respiratory rate, neutrophil counts, aspartate transaminase, albumin, serum procalcitonin, D-dimer and B-type natriuretic peptide, CD4⁺ T cells, interleukin-6 and interleukin-10) were used as candidate predictors for LR model, Random Forest (RF) and XGBoost model application. The coefficients from LR model were utilized to build a nomogram. RF and XGBoost methods suggested that Interleukin-10 and interleukin-6 were the most important variables for severity of illness prediction. The mean AUC for LR, RF, and XGBoost model were 0.91, 0.89, and 0.93 respectively (in two-fold cross-validation). Individualized prediction by XGBoost model was explained by local interpretable model-agnostic explanations (LIME) plot.

CONCLUSIONS

XGBoost exhibited the highest discriminatory performance for prediction of critically ill patients with COVID-19 pneumonia. It is inferred that the nomogram and visualized interpretation with LIME plot could be useful in the clinical setting. Additionally, interleukin-10 could serve as a useful predictor of critically ill patients with COVID-19 pneumonia.

An Integrated Deep Learning and Belief Rule Base Intelligent System to Predict Survival of COVID-19 Patient under Uncertainty

The novel Coronavirus-induced disease COVID-19 is the biggest threat to human health at the present time, and due to the transmission ability of this virus via its conveyor, it is spreading rapidly in almost every corner of the globe. The unification of medical and IT experts is required to bring this outbreak under control. In this research, an integration of both data and knowledge-driven approaches in a single framework is proposed to assess the survival probability of a COVID-19 patient. Several neural networks pre-trained models: Xception, InceptionResNetV2, and VGG Net, are trained on X-ray images of COVID-19 patients to distinguish between critical and non-critical patients. This prediction result, along with eight other significant risk factors associated with COVID-19 patients, is analyzed with a knowledge-driven belief rule-based expert system which forms a probability of survival for that particular patient. The reliability of the proposed integrated system has been tested by using real patient data and compared with expert opinion, where the performance of the system is found promising.

Heart Failure Probability and Early Outcomes of Critically Ill Patients With COVID-19: A Prospective, Multicenter Study

Background: The relationship between cardiac functions and the fatal outcome of coronavirus disease 2019 (COVID-19) is still largely underestimated. We aim to explore the role of heart failure (HF) and NT-proBNP in the prognosis of critically ill patients with COVID-19 and construct an easy-to-use predictive model using machine learning.

Methods: In this multicenter and prospective study, a total of 1,050 patients with clinical suspicion of COVID-19 were consecutively screened. Finally, 402 laboratory-confirmed critically ill patients with COVID-19 were enrolled. A “triple cut-point” strategy of NT-proBNP was applied to assess the probability of HF. The primary outcome was 30-day all-cause in-hospital death. Prognostic risk factors were analyzed using the least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression, further formulating a nomogram to predict mortality.

Results: Within a 30-day follow-up, 27.4% of the 402 patients died. The mortality rate of patients with HF likely was significantly higher than that of the patient with gray zone and HF unlikely (40.8% vs. 25 and 16.5%, respectively, P < 0.001). HF likely [Odds ratio (OR) 1.97, 95% CI 1.13–3.42], age (OR 1.04, 95% CI 1.02–1.06), lymphocyte (OR 0.36, 95% CI 0.19–0.68), albumin (OR 0.92, 95% CI 0.87–0.96), and total bilirubin (OR 1.02, 95% CI 1–1.04) were independently associated with the prognosis of critically ill patients with COVID-19. Moreover, a nomogram was developed by bootstrap validation, and C-index was 0.8 (95% CI 0.74–0.86).

Conclusions: This study established a novel nomogram to predict the 30-day all-cause mortality of critically ill patients with COVID-19, highlighting the predominant role of the “triple cut-point” strategy of NT-proBNP, which could assist in risk stratification and improve clinical sequelae.

ISARIC-4C Mortality Score overestimates risk of death due to COVID-19 in Australian ICU patients: a validation cohort study

Objective: To assess the performance of the UK International Severe Acute Respiratory and Emerging Infections Consortium (ISARIC) Coronavirus Clinical Characterisation Consortium (4C) Mortality Score for predicting mortality in Australian patients with coronavirus disease 2019 (COVID-19) requiring intensive care unit (ICU) admission. Design: Multicentre, prospective, observational cohort study. Setting: 78 Australian ICUs participating in the SPRINT-SARI (Short Period Incidence Study of Severe Acute Respiratory Infection) Australia study of COVID-19. Participants: Patients aged 16 years or older admitted to participating Australian ICUs with polymerase chain reaction (PCR)-confirmed COVID-19 between 27 February and 10 October 2020. Main outcome measures: ISARIC-4C Mortality Score, calculated at the time of ICU admission. The primary outcome was observed versus predicted in-hospital mortality (by 4C Mortality and APACHE II). Results: 461 patients admitted to a participating ICU were included. 149 (32%) had complete data to calculate a 4C Mortality Score without imputation. Overall, 61/461 patients (13.2%) died, 16.9% lower than the comparable ISARIC-4C cohort in the United Kingdom. In patients with complete data, the median (interquartile range [IQR]) 4C Mortality Score was 10.0 (IQR, 8.0-13.0) and the observed mortality was 16.1% (24/149) versus 22.9% median predicted risk of death. The 4C Mortality Score discriminatory performance measured by the area under the receiver operating characteristic curve (AUROC) was 0.79 (95% CI, 0.68-0.90), similar to its performance in the original ISARIC-4C UK cohort (0.77) and not superior to APACHE II (AUROC, 0.81; 95% CI, 0.75-0.87). Conclusions: When calculated at the time of ICU admission, the 4C Mortality Score consistently overestimated the risk of death for Australian ICU patients with COVID-19. The 4C Mortality Score may need to be individually recalibrated for use outside the UK and in different hospital settings.

Establishment of Routine Clinical Indicators-Based Nomograms for Predicting the Mortality in Patients With COVID-19

This study aimed to establish and validate the nomograms to predict the mortality risk of patients with coronavirus disease 2019 (COVID-19) using routine clinical indicators. This retrospective study included a development cohort enrolled 2,119 hospitalized patients with COVID-19 and a validation cohort included 1,504 patients with COVID-19. The demographics, clinical manifestations, vital signs, and laboratory tests of the patients at admission and outcome of in-hospital death were recorded. The independent factors associated with death were identified by a forward stepwise multivariate logistic regression analysis and used to construct the two prognostic nomograms. The nomogram 1 was a full model to include nine factors identified in the multivariate logistic regression and nomogram 2 was built by selecting four factors from nine to perform as a reduced model. The nomogram 1 and nomogram 2 showed better performance in discrimination and calibration than the Multilobular infiltration, hypo-Lymphocytosis, Bacterial coinfection, Smoking history, hyper-Tension and Age (MuLBSTA) score in training. In validation, nomogram 1 performed better than nomogram 2 for calibration. We recommend the application of nomogram 1 in general hospitals which provide robust prognostic performance though more cumbersome; nomogram 2 in the out-patient, emergency department, and mobile cabin hospitals, which depend on less laboratory examinations to make the assessment more convenient. Both the nomograms can help the clinicians to identify the patients at risk of death with routine clinical indicators at admission, which may reduce the overall mortality of COVID-19.

Redefining COVID-19 Severity and Prognosis: The Role of Clinical and Immunobiotypes

Background

Most of the explanatory and prognostic models of COVID-19 lack of a comprehensive assessment of the wide COVID-19 spectrum of abnormalities. The aim of this study was to unveil novel biological features to explain COVID-19 severity and prognosis (death and disease progression).

Methods

A predictive model for COVID-19 severity in 121 patients was constructed by ordinal logistic regression calculating odds ratio (OR) with 95% confidence intervals (95% CI) for a set of clinical, immunological, metabolomic, and other biological traits. The accuracy and calibration of the model was tested with the area under the curve (AUC), Somer's D, and calibration plot. Hazard ratios with 95% CI for adverse outcomes were calculated with a Cox proportional-hazards model.

Results

The explanatory variables for COVID-19 severity were the body mass index (BMI), hemoglobin, albumin, 3-Hydroxyisovaleric acid, CD8+ effector memory T cells, Th1 cells, low-density granulocytes, monocyte chemoattractant protein-1, plasma TRIM63, and circulating neutrophil extracellular traps. The model showed an outstanding performance with an optimism-adjusted AUC of 0.999, and Somer's D of 0.999. The predictive variables for adverse outcomes in COVID-19 were severe and critical disease diagnosis, BMI, lactate dehydrogenase, Troponin I, neutrophil/lymphocyte ratio, serum levels of IP-10, malic acid, 3, 4 di-hydroxybutanoic acid, citric acid, myoinositol, and cystine.

Conclusions

Herein, we unveil novel immunological and metabolomic features associated with COVID-19 severity and prognosis. Our models encompass the interplay among innate and adaptive immunity, inflammation-induced muscle atrophy and hypoxia as the main drivers of COVID-19 severity.

Development and validation of nomogram to predict severe illness requiring intensive care follow up in hospitalized COVID-19 cases

BACKGROUND

Early identification of severe COVID-19 patients who will need intensive care unit (ICU) follow-up and providing rapid, aggressive supportive care may reduce mortality and provide optimal use of medical resources. We aimed to develop and validate a nomogram to predict severe COVID-19 cases that would need ICU follow-up based on available and accessible patient values.

METHODS

Patients hospitalized with laboratory-confirmed COVID-19 between March 15, 2020, and June 15, 2020, were enrolled in this retrospective study with 35 variables obtained upon admission considered. Univariate and multivariable logistic regression models were constructed to select potential predictive parameters using 1000 bootstrap samples. Afterward, a nomogram was developed with 5 variables selected from multivariable analysis. The nomogram model was evaluated by Area Under the Curve (AUC) and bias-corrected Harrell's C-index with 95% confidence interval, Hosmer-Lemeshow Goodness-of-fit test, and calibration curve analysis.

RESULTS

Out of a total of 1022 patients, 686 cases without missing data were used to construct the nomogram. Of the 686, 104 needed ICU follow-up. The final model includes oxygen saturation, CRP, PCT, LDH, troponin as independent factors for the prediction of need for ICU admission. The model has good predictive power with an AUC of 0.93 (0.902-0.950) and a bias-corrected Harrell's C-index of 0.91 (0.899-0.947). Hosmer-Lemeshow test p-value was 0.826 and the model is well-calibrated (p = 0.1703).

CONCLUSION

We developed a simple, accessible, easy-to-use nomogram with good distinctive power for severe illness requiring ICU follow-up. Clinicians can easily predict the course of COVID-19 and decide the procedure and facility of further follow-up by using clinical and laboratory values of patients available upon admission.