Development and validation of a prognostic nomogram for HIV/AIDS patients who underwent antiretroviral therapy: Data from a China population-based cohort

The predictive accuracy of machine learning for the risk of death in HIV patients: a systematic review and meta-analysis

BACKGROUND

Early prediction of mortality in individuals with HIV (PWH) has perpetually posed a formidable challenge. With the widespread integration of machine learning into clinical practice, some researchers endeavor to formulate models predicting the mortality risk for PWH. Nevertheless, the diverse timeframes of mortality among PWH and the potential multitude of modeling variables have cast doubt on the efficacy of the current predictive model for HIV-related deaths. To address this, we undertook a systematic review and meta-analysis, aiming to comprehensively assess the utilization of machine learning in the early prediction of HIV-related deaths and furnish evidence-based support for the advancement of artificial intelligence in this domain.

METHODS

We systematically combed through the PubMed, Cochrane, Embase, and Web of Science databases on November 25, 2023. To evaluate the bias risk in the original studies included, we employed the Predictive Model Bias Risk Assessment Tool (PROBAST). During the meta-analysis, we conducted subgroup analysis based on survival and non-survival models. Additionally, we utilized meta-regression to explore the influence of death time on the predictive value of the model for HIV-related deaths.

RESULTS

After our comprehensive review, we analyzed a total of 24 pieces of literature, encompassing data from 401,389 individuals diagnosed with HIV. Within this dataset, 23 articles specifically delved into deaths during long-term follow-ups outside hospital settings. The machine learning models applied for predicting these deaths comprised survival models (COX regression) and other non-survival models. The outcomes of the meta-analysis unveiled that within the training set, the c-index for predicting deaths among people with HIV (PWH) using predictive models stands at 0.83 (95% CI: 0.75-0.91). In the validation set, the c-index is slightly lower at 0.81 (95% CI: 0.78-0.85). Notably, the meta-regression analysis demonstrated that neither follow-up time nor the occurrence of death events significantly impacted the performance of the machine learning models.

CONCLUSIONS

The study suggests that machine learning is a viable approach for developing non-time-based predictions regarding HIV deaths. Nevertheless, the limited inclusion of original studies necessitates additional multicenter studies for thorough validation.

Mortality risk of people living with HIV under hypothetical intervention scenarios of PM2.5 and HIV severity: a prospective cohort study

This study estimated and compared mortality risks among people living with HIV (PLWH) under the real-world and hypothetical scenarios of PM2.5 concentrations and HIV severity. An open cohort from all PLWH receiving antiretroviral therapy in Sichuan during 2010-2019 was constructed, resulting in 541,515 person-years. Annual mean concentrations of PM2.5 were estimated and linked to PLWH by their residential address. The parametric g-formula were used to assess 3- and 5-year mortality risks under the real-world and hypothetical scenarios of PM2.5 (10-35, 35-50, 50-75 μg/m3) and CD4 concentrations (0-200, 200-500, 500-800, 800-1100 counts/μl). The estimated 3- and 5-year mortality risks among the PLWH were 14.43 % and 19.38 %, respectively, which would decrease substantially when annual PM2.5 concentration were reduced to between 10 and 35 μg/m3 (risk difference [RD] = -3.23 % and - 4.06 %) and would increase when PM2.5 concentration were elevated to between 50 and 75 μg/m3 (RD = 3.59 % and 5.04 %). The mortality risk would increase when CD4 concentration were reduced to <200 counts/μl (RD = 15.90 % and 20.27 %) and would decrease when CD4 concentration were ≥ 200 counts/μl, especially to between 800 and 1100 counts/μl (RD = -9.01 % and - 11.75 %). The elevated concentration of PM2.5 may disproportionately affect individuals with immune deficiency, especially those with more severity. The findings would serve as justifications for future intervention design and policy making to alleviate air pollution and improve environmental justice and health equity.

Development and Evaluation of a Nomogram for Predicting the Outcome of Immune Reconstitution Among HIV/AIDS Patients Receiving Antiretroviral Therapy in China

This study aims to develop and evaluate a model to predict the immune reconstitution among HIV/AIDS patients after antiretroviral therapy (ART). A total of 502 HIV/AIDS patients are randomized to the training cohort and evaluation cohort. Least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression analysis are performed to identify the indicators and establish the nomogram for predicting the immune reconstitution. Decision curve analysis (DCA) and clinical impact curve (CIC) are used to evaluate the clinical effectiveness of the nomogram. Predictive factors included white blood cells (WBC), baseline CD4+ T-cell counts (baseline CD4), ratio of effector regulatory T cells to resting regulatory T cells (eTreg/rTreg) and low-density lipoprotein cholesterol (LDL-C) and are incorporated into the nomogram. The area under the curve (AUC) is 0.812 (95% CI, 0.767∼0.851) and 0.794 (95%CI, 0.719∼0.857) in the training cohort and evaluation cohort, respectively. The calibration curve shows a high consistency between the predicted and actual observations. Moreover, DCA and CIC indicate that the nomogram has a superior net benefit in predicting poor immune reconstitution. A simple-to-use nomogram containing four routinely collected variables is developed and internally evaluated and can be used to predict the poor immune reconstitution in HIV/AIDS patients after ART.

Toward a Prognostic Model for Mortality Risk in Older People Living With HIV: A Prospective Cohort Study From Southwestern China

OBJECTIVE

The existing prognostic models for mortality risk in people living with HIV (PLWH) may not be applicable for older PLWH because the risk factors were confined to biomarkers and clinical variables. We developed and validated a nomogram for the prognosis of all-cause mortality in older PLWH based on comprehensive predictors.

DESIGN

Prospective cohort study.

SETTING AND PARTICIPANTS

We included 824 participants aged ≥50 years (mean age, 64.0 ± 7.6 years) from 30 study sites in Sichuan, China, and followed up from Nov 2018 to Mar 2021.

METHODS

Data on demographics, biomarkers, and clinical indicators were extracted from the registry; mental and social factors were assessed by a survey. Elastic net was used to select predictors. A nomogram was developed based on Cox proportional hazards regression model to visualize the relative effect size (points) of the selected predictors. The prognostic index (PI) was calculated by summing points of all predictors to quantify mortality risk.

RESULTS

Predictive performance of PI from the nomogram was good, with area under the curve of 0.76 for the training set, and 0.77 for the validation set. Change in CD4 count, virological failure in antiretroviral therapy, and living with comorbidities were robust predictors. Depressive symptoms were an important predictor in men, those aged ≥65 years, and those with time of diagnosis <1 year; low social capital was an additional predictor in people aged <65. Mortality risk increased approximately 10-fold among participants whose PI was in the fourth quartile compared with those in the first quartile (hazard ratio, 9.5; 95% CI, 2.9-31.5).

CONCLUSION AND IMPLICATIONS

Although biological and clinical factors are crucial predictors, mental and social predictors are essential for specific groups. The developed nomogram is useful for identifying risk factors and groups at risk of mortality in older PLWH.

Developing and validating clinical models to identify candidates for allergic rhinitis pre-exposure prophylaxis

PURPOSE

Few risk-forecasting models of allergic rhinitis (AR) exist that may aid AR pre-exposure prophylaxis (PrEP) in clinical practice. Therefore, this study aimed to develop and validate an effective clinical model for identifying candidates for AR PrEP using a routine medical questionnaire.

METHODS

This study was conducted in 10 Chinese provinces with 13 medical centers (n = 877) between 2019 and 2021. Clinical characteristics and exposure history were collected via face-to-face interviews. Well-trained physicians diagnosed patients with AR based on skin prick test results and clinical performance. The least absolute shrinkage and selection operator model was used to identify potential risk factors for AR, and the logistic regression model was used to construct the risk-forecasting model. Predictive power and model reliability were assessed using area under the receiver operating characteristic curve and calibration curves, respectively.

RESULTS

This study diagnosed 625 patients with AR who had positive responses to at least one indoor or outdoor allergen and 460 to at least one outdoor pollen allergen. Two nomograms were established to identify two types of AR with various sensitization patterns. Both models had an area under curve of approximately 0.7 in the development and internal validation datasets. Additionally, our findings found good agreement for the calibration curves of both models.

CONCLUSION

Early identification of candidates for AR PrEP using routine medical information may improve the deployment of limited resources and effective health management. Our models showed good performance in predicting AR; therefore, they can serve as potential automatic screening tools to identify AR PrEP candidates.

Incomplete immune reconstitution and its predictors in people living with HIV in Wuhan, China

OBJECTIVE

This study aimed to build and validate a nomogram model to predict the risk of incomplete immune reconstitution in people living with HIV (PLWH).

METHODS

Totally 3783 individuals with a confirmed diagnosis of HIV/AIDS were included. A predictive model was developed based on a retrospective set (N = 2678) and was validated using the remaining cases (N = 1105). Univariate and multivariate logistic regression analyses were performed to determine valuable predictors among the collected clinical and laboratory variables. The predictive model is presented in the form of a nomogram, which is internally and externally validated with two independent datasets. The discrimination of nomograms was assessed by calculating the area under the curve (AUC). Besides, calibration curve and decision curve (DCA) analyses were performed in the training and validation sets.

RESULTS

The final model comprised 5 predictors, including baseline CD4, age at ART initiation, BMI, HZ and TBIL. The AUC of the nomogram model was 0.902, 0.926, 0.851 in the training cohort, internal validation and external cohorts. The calibration accuracy and diagnostic performance were satisfactory in both the training and validation sets.

CONCLUSIONS

This predictive model based on a retrospective study was externally validated using 5 readily available clinical indicators. It showed high performance in predicting the risk of incomplete immune reconstitution in people living with HIV.

Nomogram for Early Prediction of Parkinson's Disease Based on microRNA Profiles and Clinical Variables

BACKGROUND

Few efficient and simple models for the early prediction of Parkinson's disease (PD) exists.

OBJECTIVE

To develop and validate a novel nomogram for early identification of PD by incorporating microRNA (miRNA) expression profiles and clinical indicators.

METHODS

Expression levels of blood-based miRNAs and clinical variables from 1,284 individuals were downloaded from the Parkinson's Progression Marker Initiative database on June 1, 2022. Initially, the generalized estimating equation was used to screen candidate biomarkers of PD progression in the discovery phase. Then, the elastic net model was utilized for variable selection and a logistics regression model was constructed to establish a nomogram. Additionally, the receiver operating characteristic (ROC) curves, decision curve analysis (DCA), and calibration curves were utilized to evaluate the performance of the nomogram.

RESULTS

An accurate and externally validated nomogram was constructed for predicting prodromal and early PD. The nomogram is easy to utilize in a clinical setting since it consists of age, gender, education level, and transcriptional score (calculated by 10 miRNA profiles). Compared with the independent clinical model or 10 miRNA panel separately, the nomogram was reliable and satisfactory because the area under the ROC curve achieved 0.72 (95% confidence interval, 0.68-0.77) and obtained a superior clinical net benefit in DCA based on external datasets. Moreover, calibration curves also revealed its excellent prediction power.

CONCLUSION

The constructed nomogram has potential for large-scale early screening of PD based upon its utility and precision.

Comparison of the prognostic value of a comprehensive set of predictors in identifying risk of metabolic-associated fatty liver disease among employed adults

Objective

Metabolic-associated fatty liver disease (MAFLD) is of concern in employed adults, while the crucial indicators in predicting MAFLD are understudied in this population. We aimed to investigate and compare the prediction performance of a set of indicators for MAFLD in employed adults.

Methods

A cross-sectional study recruiting 7968 employed adults was conducted in southwest China. MAFLD was assessed by abdominal ultrasonography and physical examination. Comprehensive indicators of demographics, anthropometric, lifestyle, psychological, and biochemical indicators were collected by questionnaire or physical examination. All indicators were evaluated for importance in predicting MAFLD by random forest. A prognostic model based on multivariate regression model was constructed to obtain a prognostic index. All indicators and prognostic index were compared to evaluate their prediction performance in predicting MAFLD by the receiver operating characteristic (ROC) curve, calibration plot, and Decision curve analysis (DCA).

Results

Triglyceride Glucose-Body Mass Index (TyG-BMI), BMI, TyG, triglyceride (TG)/high-density lipoprotein-cholesterol (HDL-C), and TG ranked the top five important indicators, and TyG-BMI performed the most accurate prediction of MAFLD according to the ROC curve, calibration plot and DCA. The area under the ROC curves (AUCs) of the five indicators were all over 0.7, with TyG-BMI (cut-off value: 218.284, sensitivity: 81.7%, specificity: 78.3%) suggesting the most sensitive and specific indicator. All five indicators showed higher prediction performance and net benefit than the prognostic model.

Conclusion

This epidemiological study firstly compared a set of indicators to evaluate their prediction performance in predicting MAFLD risk among employed adults. Intervention targeting powerful predictors can be helpful to reduce the MAFLD risk among employed adults.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-023-15365-9.

Evaluation of antiretroviral therapy effect and prognosis between HIV-1 recent and long-term infection based on a rapid recent infection testing algorithm

Early diagnosis of HIV-1 infection and immediate initiation of combination antiretroviral therapy (cART) are important for achieving better virological suppression and quicker immune reconstitution. However, no serological HIV-1 recency testing assay has been approved for clinical use, and the real-world clinical outcomes remain to be explored for the subjects with HIV-1 recent infection (RI) or long-term infection (LI) when antiretroviral therapy is initiated. In this study, a HIV-1 rapid recent-infection testing strip (RRITS) was developed and incorporated into the recent infection testing algorithms (RITAs) to distinguish HIV-1 RI and LI and to assess their clinical outcomes including virological response, the recovery of CD4+ T-cell count and CD4/CD8 ratio and the probability of survival. We found that the concordance between our RRITS and the commercially available LAg-Avidity EIA was 97.13% and 90.63% when detecting the longitudinal and cross-sectional HIV-1 positive samples, respectively. Among the 200 HIV-1 patients analyzed, 22.5% (45/200) of them were RI patients and 77.5% (155/200) were chronically infected and 30% (60/200) of them were AIDS patients. After cART, 4.1% (5/155) of the LI patients showed virological rebound, but none in the RI group. The proportion of CD4+ T-cell count >500 cells/mm3 was significantly higher in RI patients than in LI after 2 years of cART with a hazard ratio (HR) of 2.6 (95% CI: 1.9, 3.6, p < 0.0001) while the probability of CD4/CD8 = 1 was higher in RI than in LI group with a HR of 3.6 (95% CI: 2.2, 5.7, p < 0.0001). Furthermore, the immunological recovery speed was 16 cells/mm3/month for CD4+ T-cell and 0.043/month for the ratio of CD4/CD8 in the RI group, and was bigger in the RI group than in the LI patients (p < 0.05) during the 1st year of cART. The survival probability for LI patients was significantly lower than that for RI patients (p < 0.001). Our results indicated that RRITS combined with RITAs could successfully distinguish HIV-1 RI and LI patients whose clinical outcomes were significantly different after cART. The rapid HIV-1 recency test provides a feasible assay for diagnosing HIV-1 recent infection and a useful tool for predicting the outcomes of HIV-1 patients.

Development and validation of a prognostic nomogram for patients with stage II colon mucinous adenocarcinoma

PURPOSE

Mucinous histology is generally considered as a risk factor of prognosis in stage II colon cancer, but there is no appropriate model for prognostic evaluation and treatment decision in patients with stage II colon mucinous adenocarcinoma (C-MAC) Thus, it is urgent to develop a comprehensive, individualized evaluation tool to reflect the heterogeneity of stage II C-MAC.

METHODS

Patients with stage II C-MAC who underwent surgical treatment in the Surveillance, Epidemiology, and End Results Program were enrolled and randomly divided into training cohort (70%) and internal validation cohort (30%). Prognostic predictors which were determined by univariate and multivariate analysis in the training cohort were included in the nomogram. The calibration curves, decision curve analysis, X-tile analysis, and Kaplan-Meier curve of the nomogram were validated in the internal validation cohort.

RESULTS

Three thousand seven hundred sixty-two patients of stage II C-MAC were enrolled. The age, pathological T (pT) stage, tumor number, serum carcinoembryonic antigen (CEA), and perineural invasion (PNI) were independent predictors of overall survival (OS), which were used to establish a nomogram. Calibration curves of the nomogram indicated good consistency between nomogram prediction and actual survival for 1-, 3- and 5-year OS. Besides, patients with stage II C-MAC could be divided into high-, middle-, and low-risk subgroups by the nomogram. Further subgroup analysis indicated that patients in the high-risk group could have a survival benefit from chemotherapy after surgical treatment.

CONCLUSIONS

We established the first nomogram to accurately predict the survival of stage II C-MAC patients who underwent surgical treatment. In addition, the nomogram identified low-, middle-, and high-risk subgroups of patients and found chemotherapy might improve survival in the high-risk subgroup of stage II C-MAC patients.

Development and validation of a novel nomogram for predicting the occurrence of myopia in schoolchildren: A prospective cohort study

PURPOSE

Myopia is a major public health issue and occurs at young ages. Apart from its high prevalence, myopia results in high costs and irreversible blinding diseases. Accurate prediction of the risk of myopia onset is crucial for its precise prevention. We aimed to develop and validate an effective nomogram for predicting myopia onset in schoolchildren.

DESIGN

School-based prospective cohort study.

METHODS

A total of 1073 schoolchildren were enrolled from November 2014 to May 2019 in China, and were divided into the training and validation cohorts. Myopia was defined as a spherical equivalent refraction (SER) ≤-0.5 diopters. Predictors of myopia were determined through the least absolute shrinkage and selection operator regression and multivariable Cox proportional hazard model based on the training cohort. The predictive performance of the nomogram was validated internally through time-dependent receiver operating characteristic (ROC) curves, calibration plot, decision curve analysis, and Kaplan-Meier curves.

RESULTS

Independent predictors at baseline including gender, SER, axial length, corneal refractive power, and positive relative accommodation were included in the nomogram prediction model. This nomogram demonstrated excellent calibration, clinical net benefit, and discrimination, with all the area under the ROC curves (AUCs) between 0.74 and 0.86 in the training and validation cohorts. The Kaplan-Meier curves showed that 3 distinct risk groups stratified through X-tile analysis were well discriminated and robust among subgroups. The Harrell's C-index and net reclassification improvement demonstrated that the nomogram substantially improved compared with previous models. An online myopia risk calculator was generated for better individual prediction.

CONCLUTIONS

The nomogram provides accurate and individual prediction of myopia onset in schoolchildren. External validation is needed to verify the generalizability of this nomogram.

A clinical model for predicting knee replacement in early-stage knee osteoarthritis: data from osteoarthritis initiative

BACKGROUND

Knee osteoarthritis (OA) progresses in a heterogeneous way, as a majority of the patients gradually worsen over decades while some undergo rapid progression and require knee replacement. The aim of this study was to develop a predictive model that enables quantified risk prediction of future knee replacement in patients with early-stage knee OA.

METHODS

Patients with early-stage knee OA, intact MRI measurements, and a follow-up time larger than 108 months were retrieved from the Osteoarthritis Initiative database. Twenty-five candidate predictors including demographic data, clinical outcomes, and radiographic parameters were selected. The presence or absence of knee replacement during the first 108 months of the follow-up was regarded as the primary outcome. Patients were randomly divided into derivation and validation groups in the ratio of three to one. Nomograms were developed based on multivariable logistic regressions of derivation group via R language. Those models were further tested in the validation group for external validation.

RESULTS

A total of 839 knees were enrolled, with 98 knees received knee replacement during the first 108 months. Glucocorticoid injection history, knee OA in the contralateral side, extensor muscle strength, area of cartilage deficiency, bone marrow lesion, and meniscus extrusion were selected to develop the nomogram after multivariable logistic regression analysis. The bias-corrected C-index and AUC of our nomogram in the validation group were 0.804 and 0.822, respectively.

CONCLUSION

Our predicting model provided simplified identification of patients with high risk of rapid progression in knee OA, which showed adequate predictive discrimination and calibration.

KEY POINTS

• Knee OA progresses in a heterogeneous way and rises to a challenge when making treatment strategies. • Our predicting model provided simplified identification of patients with high risk of rapid progression in knee OA.

Significance of a PTEN Mutational Status-Associated Gene Signature in the Progression and Prognosis of Endometrial Carcinoma

Background

PTEN mutations have been reported to be involved in the development and prognosis of endometrial carcinoma (EC). However, a prognostic gene signature associated with PTEN mutational status has not yet been developed. In this study, we generated a PTEN mutation-associated prognostic gene signature for EC.

Methods

We obtained the single-nucleotide variation and transcriptomic profiling data from The Cancer Genome Atlas database as training data and implemented the least absolute shrinkage and selection operator (LASSO) Cox regression algorithm to establish a PTEN mutation-associated prognostic gene signature. The overall survival rates of the high-risk and low-risk groups were determined with the Kaplan-Meier (K-M) method, and the accuracy of risk score prediction was tested by using the receiver operating characteristic (ROC) curve.

Results

The K-M curves revealed that the EC patients with PTEN mutations augured favorable survival outcomes. Differential expression analysis between the EC patients with PTEN mutation and wild-type PTEN identified 224 differentially expressed genes (DEGs). Eighty-four DEGs that manifested prognostic value were fitted into the LASSO-Cox analysis, and a PTEN gene signature with seven mutation-associated prognostic genes that showed robust prognostic ability was constructed; this signature was then successfully validated in the other two datasets from the cBioPortal database as well as with 60 clinical specimens. Furthermore, the PTEN mutation-associated prognostic gene signature proved to be an independent prognostic predictor of EC. Remarkably, the EC patients in the high-risk group were characterized by higher tumor stages and grades as well as lower tumor mutation burden with respect to EC, with a poor survival outcome. Collectively, the PTEN mutation-associated prognostic gene signature that we developed could now be used as a favorable prognostic biomarker for EC.

Conclusion

In summary, we developed and validated a prognostic predictor for EC associated with PTEN mutational status that may be used as a favorable prognostic biomarker and therapeutic target for EC.

Construction and validation of a prognostic nomogram for predicting the survival of HIV/AIDS adults who received antiretroviral therapy: a cohort between 2003 and 2019 in Nanjing

BACKGROUND

Great achievements have been achieved by free antiretroviral therapy (ART). A rapid and accurate prediction of survival in people living with HIV/AIDS (PLHIV) is needed for effective management. We aimed to establish an effective prognostic model to forecast the survival of PLHIV after ART.

METHODS

The participants were enrolled from a follow-up cohort over 2003-2019 in Nanjing AIDS Prevention and Control Information System. A nested case-control study was employed with HIV-related death, and a propensity-score matching (PSM) approach was applied in a ratio of 1:4 to allocate the patients. Univariable and multivariable Cox proportional hazards analyses were performed based on the training set to determine the risk factors. The discrimination was qualified using the area under the curve (AUC) and concordance index (C-Index). The nomogram was calibrated using the calibration curve. The clinical benefit of prognostic nomogram was assessed by decision curve analysis (DCA).

RESULTS

Predictive factors including CD4 cell count (CD4), body mass index (BMI) and hemoglobin (HB) were determined and incorporated into the nomogram. In the training set, AUC and C-index (95% CI) were 0.831 and 0.798 (0.758, 0.839), respectively. The validation set revealed a good discrimination with an AUC of 0.802 and a C-index (95% CI) of 0.786 (0.681, 0.892). The calibration curve also exhibited a high consistency in the predictive power (especially in the first 3 years after ART initiation) of the nomogram. Moreover, DCA demonstrated that the nomogram was clinically beneficial.

CONCLUSION

The nomogram is effective and accurate in forecasting the survival of PLHIV, and beneficial for medical workers in health administration.

Development and external validation of a prognostic model for survival of people living with HIV/AIDS initiating antiretroviral therapy

Background: Most existing prognostic models for people living with HIV/AIDS (PLWHA) were derived from cohorts in high-income settings established a decade ago and may not be applicable for contemporary patients, especially for patients in developing settings. The aim of this study was to develop and externally validate a prognostic model for survival in PLWHA initiating ART based on a large population-based cohort in China.

Methods: We obtained data for patients from the Chinese National Free Antiretroviral Treatment Program database. The derivation cohort consisted of PLWHA treated between February 2004 and December 2019 in a tertiary center in Guangzhou, South China, and validation cohort of patients treated between February 2004 to December 2018 in another tertiary hospital in Shenyang, Northeast China. We included ART-naive patients aged above 16 who initiated a combination ART regimen containing at least three drugs and had at least one follow-up record. We assessed 20 candidate predictors including patient characteristics, disease characteristics, and laboratory tests for an endpoint of death from all causes. The prognostic model was developed from a multivariable cox regression model with predictors selected using the least absolute shrinkage and selection operator (Lasso). To assess the model's predictive ability, we quantified the discriminative power using the concordance (C) statistic and calibration accuracy by comparing predicted survival probabilities with observed survival probabilities estimated with the Kaplan–Meier method.

Findings: The derivation cohort included 16481 patients with a median follow-up of 3·41 years, among whom 735 died. The external validation cohort comprised 5751 participants with a median follow-up of 2·71 years, of whom 185 died. The final model included 10 predictors: age, body mass index, route of HIV acquisition, coinfection with tuberculosis, coinfection with hepatitis C virus, haemoglobin, CD4 cell count, platelet count, aspartate transaminase, and plasma glucose. The C-statistic was 0·84 (95% confidence interval 0·82–0·85) in internal validation after adjustment of optimism and 0·84 (0·82–0·87) in external validation, which remained consistently above 0·75 in all landmark time points within five years of follow up when using time-updated laboratory measurements. The calibration accuracy was satisfactory in both derivation and validation cohorts.

Interpretation: We have developed and externally validated a model to predict long-term survival in PLWHA on ART. This model could be applied to individualized patient counseling and management during treatment, and future innovative trial design.

Funding: Natural Science Foundation of China Excellent Young Scientists Fund, Natural Science Foundation of China International/Regional Research Collaboration Project, Natural Science Foundation of China Young Scientist Fund, the National Science and Technology Major Project of China,National Special Research Program of China for Important Infectious Diseases, 13th Five-Year Key Special Project of Ministry of Science and Technology, and the Joint-innovation Program in Healthcare for Special Scientific Research Projects of Guangzhou.

Inflammation-Immunity-Nutrition Score: A Novel Prognostic Score for Patients with Resectable Colorectal Cancer

Purpose

This study was designed to investigate the prognostic value of the combination of high-sensitivity C-reactive protein, lymphocyte, and albumin in patients with resectable colorectal cancer.

Patients and Methods

Seven-hundred-and-nineteen patients who underwent colorectal cancer resection in Hubei Cancer Hospital were included. Inflammation-Immunity-Nutrition score (0–6) was constructed based on preoperative high-sensitivity C-reactive protein, lymphocyte, and albumin. Time-dependent receiver operating characteristic curve, decision curve, Kaplan-Meier survival curve, Cox regression, and C-index were conducted to detect the prognostic values of inflammation-immunity-nutrition score. The prognostic values of inflammation-immunity-nutrition score in different subgroups by sex, location of tumor, pathologic stage, and KRAS mutation were also explored. The prognostic performance of inflammation-immunity-nutrition score was further compared with that of other traditional prognostic indicators.

Results

The median follow-up time was 40 months. High inflammation-immunity-nutrition score (>2 scores) presented worse survival, with the adjusted hazard ratios (95% confidence intervals) of 3.106 (2.202–4.380) for overall survival and 2.105 (1.604–2.764) for disease-free survival. Besides, the associations of high inflammation-immunity-nutrition score with overall survival were even stronger in cases with wild type KRAS, with the adjusted hazard ratios (95% confidence intervals) of 4.018 (2.355–6.854). Considering the AUCs, C-indices, and hazard ratios estimates, inflammation-immunity-nutrition score presented better prognostic performance than high-sensitivity modified Glasgow prognostic score, high-sensitivity C-reactive protein to albumin ratio, prognostic nutrition index, carcinoembryonic antigen, and carbohydrate antigen 19-9 for overall survival.

Conclusion

Inflammation-immunity-nutrition score might serve as a powerful prognostic score in patients with colorectal cancer for overall survival, particularly in patients with wild type KRAS.

Whole-blood magnesium and blood lipids are individually and jointly associated with an elevated likelihood of youngsters being overweight or obese: A matched case-control study using the propensity score

OBJECTIVES

Youngsters who are overweight or obese (YOO) have become an important global health concern. Some micronutrients may be modifiable influential factors. This study aimed to investigate the individual and joint association of whole-blood magnesium (WBMg) and total cholesterol (TC), triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), or high-density lipoprotein cholesterol (HDL-C) in YOO.

METHODS

This is a propensity score matching-based case-control study. YOO was defined depending on age- and sex-specific body mass index z-score, calculated with SAS macros (%group_standard and %WHO2007) from the World Health Organization website. WBMg, blood lipids, and covariates were carefully measured by trained technicians using a whole-blood, five-element, basic analyzer and atomic absorption spectrometer or automatic biochemical analyzer. Locally weighted scattered plot smoothing and multivariable conditional logistic regression models were applied to estimate the associations of WBMg and blood lipids in YOO.

RESULTS

WBMg was positively associated with YOO. The adjusted likelihood of YOO significantly increased by 21% (odds ratio: 1.21; 95% confidence interval [CI], 1.10-1.33) with per-interquartile range elevation of WBMg. Compared with the 1st quartile, adjusted odds ratios among youngsters in the 2^nd, 3^rd, and 4^th quartiles of WBMg were 1.11 (95% CI, 0.92-1.35), 1.29 (95% CI, 1.06-1.57), and 1.47 (95% CI, 1.18-1.83), respectively. Furthermore, the relationship between WBMg and YOO was moderated by lipid profiles. Compared with those having lower (< median) WBMg and TC, TG, LDL-C, or higher (≥ median) HDL-C, youngsters with both higher WBMg and TC, TG, LDL-C, or lower HDL-C had higher YOO odds, which averagely increased by 188%, 250%, 339%, and 369%, respectively.

CONCLUSIONS

WBMg was an independent risk factor of YOO, and the associations were stronger among those with unhealthy blood lipids. Our findings can help to guide clinical and public health policies on the relevance of magnesium nutritional status.

A Potential Immune-Related Long Non-coding RNA Prognostic Signature for Ovarian Cancer

Ovarian cancer (OC), the most lethal gynecologic malignancy, ranks fifth in cancer deaths among women, largely because of late diagnosis. Recent studies suggest that the expression levels of immune-related long non-coding RNAs (lncRNAs) play a significant role in the prognosis of OC; however, the potential of immune-related lncRNAs as prognostic factors in OC remains unexplored. In this study, we aimed to identify a potential immune-related lncRNA prognostic signature for OC patients. We used RNA sequencing and clinical data from The Cancer Genome Atlas and the Gene Expression Omnibus database to identify immune-related lncRNAs that could serve as useful biomarkers for OC diagnosis and prognosis. Univariate Cox regression analysis was used to identify the immune-related lncRNAs with prognostic value. Functional annotation of the data was performed through the GenCLiP310 website. Seven differentially expressed lncRNAs (AC007406.4, AC008750.1, AL022341.2, AL133351.1, FAM74A7, LINC02229, and HOXB-AS2) were found to be independent prognostic factors for OC patients. The Kaplan-Meier curve indicated that patients in the high-risk group had a poorer survival outcome than those in the low-risk group. The receiver operating characteristic curve revealed that the predictive potential of the immune-related lncRNA signature for OC was robust. The prognostic signature of the seven lncRNAs was successfully validated in the GSE9891, GSE26193 datasets and our clinical specimens. Multivariate analyses suggested that the signature of the seven lncRNAs was an independent prognostic factor for OC patients. Finally, we constructed a nomogram model and a competing endogenous RNA network related to the lncRNA prognostic signature. In conclusion, our study reveals novel immune-related lncRNAs that may serve as independent prognostic factors in OC.

Low prevalence and independent prognostic role of del(11q) in Chinese patients with chronic lymphocytic leukemia

The 11q deletion (del(11q)) is a conventional cytogenetic aberration observed in chronic lymphocytic leukemia (CLL) patients. However, the prevalence and the prognostic value of del(11q) are still controversial. In this research, we retrospectively explored the prevalence, association, and prognostic significance of del(11q) in 352 untreated and 99 relapsed/refractory Chinese CLL patients. Totally 11.4% of untreated and 19.2% of relapsed/refractory patients harbored del(11q). Del(11q) was more common in patients with β2-microglobulin > 3.5 mg/L, positive CD38, positive zeta-chain associated protein kinase 70, unmutated immunoglobulin heavy variable-region gene and ataxia telangiectasia mutated mutation. Kaplan-Meier method and univariate Cox regression indicated that del(11q) was an independent prognostic factor for overall survival (OS). Based on the results of univariate Cox regression analysis, two nomograms that included del(11q) were established to predict survival. Desirable area under curve of receiver operating characteristic curves was obtained in the training and validation cohorts. In addition, the calibration curves for the probability of survival showed good agreement between the prediction by nomogram and actual observation. In summary, the prevalence of del(11q) is relatively low in our cohort and del(11q) is an unfavorable prognostic factor for untreated CLL patients. Besides, these two nomograms could be used to accurately predict the prognosis of untreated CLL patients.

A Prognostic Model to Assess Long-Term Survival of Patients on Antiretroviral Therapy: A 15-Year Retrospective Cohort Study in Southwestern China

BACKGROUND

Because there is no assessment tool for survival of people with human immunodeficiency virus (PWH) who received antiretroviral therapy (ART) in rural southwestern China, we aimed to formulate and validate a simple-to-use model to predict long-term overall survival at the initiation of ART.

METHODS

In total, 36 268 eligible participants registered in the Guangxi autonomous region between December 2003 and December 2018 were enrolled and randomized into development and validation cohorts. Predictive variables were determined based on Cox hazard models and specialists' advice. Discrimination, calibration, and clinical utility were measured, respectively.

RESULTS

The prognostic combined 14 variables: sex, age, marital status, infectious route, opportunistic infection, acquired immunodeficiency syndrome (AIDS)-related symptoms, body mass index, CD4+ T lymphocyte count, white blood cell, platelet, hemoglobin, serum creatinine, aspartate transaminase, and total bilirubin. Age, aspartate transaminase, and serum creatinine were assigned higher risk scores than that of CD4+ T lymphocytopenia count and having opportunistic infections or AIDS-related symptoms. At 3 time points (1, 3, and 5 years), the area under the curve ranged from 0.75 to 0.81 and the Brier scores ranged from 0.03 to 0.07. The decision curve analysis showed an acceptable clinical net benefit.

CONCLUSIONS

The prognostic model incorporating routine baseline data can provide a useful tool for early risk appraisal and treatment management in ART in rural southwestern China. Moreover, our study underscores the role of non-AIDS-defining events in long-term survival in ART.

Plasma hemoglobin and the risk of death in HIV/AIDS patients treated with antiretroviral therapy

Background: Previous studies concerning the effect of plasma hemoglobin (HB) and other factors that may modify the risk of death in people living with HIV/AIDS (PLHIV) treated with antiretroviral therapy (ART) are limited.

Results: Higher HB was independently linked to a lower death risk in PLHIV, with a decrease of 29% (13%, 43%) per standard deviation (SD) increment after adjusting for CD4, VL and other potential factors [hazard ratio (HR): 0.71, 95% confidence interval (CI): 0.57-0.87, P<0.001]. In addition, the addition of HB to the predictive model containing VL and CD4 significantly improved the C-index, by 0.69% (95% CI: 0.68%-0.71%), and net discrimination, by 0.5% (95% CI: 0.0%-1.6%, P=0.040), when predicting the death risk of PLHIV.

Conclusions: A lower level of HB was an independent risk factor for HIV/AIDS-associated death in PLHIV. HB combined with VL and CD4 may be an appropriate predictive model of the death risk of PLHIV.

Materials and methods: A propensity-score matching (PSM) approach was applied to select a total of 750 PLHIV (150 deceased and 600 living) from the AIDS prevention and control information system in the Wenzhou area from 2006 to 2018. Multivariable Cox proportional hazards regression models were formulated to estimate the effect of HB. The predictive performance improvement contributed by HB was evaluated using the C-index and net reclassification improvement.

Development and Validation of Nomograms Based on Gamma-Glutamyl Transpeptidase to Platelet Ratio for Hepatocellular Carcinoma Patients Reveal Novel Prognostic Value and the Ratio Is Negatively Correlated With P38MAPK Expression

Background

Early prediction of recurrence and death risks is significant to the treatment of hepatocellular carcinoma (HCC) patients. We aimed to develop and validate prognosis nomogram models based on the gamma-glutamyl transpeptidase (GGT)-to-platelet (PLT) ratio (GPR) for HCC and to explore the relationship between the GPR and inflammation-related signaling pathways.

Methods

All data were obtained from 2000 to 2012 in the Affiliated Hospital of Qingdao University. In the training cohort, factors included in the nomograms were determined by univariate and multivariate analyses. In the training and validation cohorts, the concordance index (C-index) and calibration curves were used to assess predictive accuracy, and receiver operating characteristic curves were used to assess discriminative ability. Clinical utility was evaluated using decision curve analysis. Moreover, improvement of the predictive accuracy of the nomograms was evaluated by calculating the decision curve analysis, the integrated discrimination improvement, and the net reclassification improvement. Finally, the relationship between the GPR and inflammation-related signaling pathways was evaluated using the independent-samples t-test.

Results

A larger tumor size and higher GPR were common independent risk factors for both disease-free survival (DFS) and overall survival (OS) in HCC (P < 0.05). Good agreement between our nomogram models' predictions and actual observations was detected by the C-index and calibration curves. Our nomogram models showed significantly better performance in predicting the HCC prognosis compared to other models (P < 0.05). Online webserver and scoring system tables were built based on the proposed nomogram for convenient clinical use. Notably, including the GPR greatly improved the predictive ability of our nomogram models (P < 0.05). In the validation cohort, p38 mitogen-activated protein kinase (P38MAPK) expression was significantly negatively correlated with the GPR (P < 0.01) and GGT (P = 0.039), but was not correlated with PLT levels (P = 0.063). And we found that P38MAPK can regulate the expression of GGT by quantitative real-time PCR and Western blotting experiments.

Conclusions

The dynamic nomogram based on the GPR provides accurate and effective prognostic predictions for HCC, and P38MAPK-GGT may be a suitable therapeutic target to improve the prognosis of HCC patients.

Critical appraisal and external validation of a prognostic model for survival of people living with HIV/AIDS who underwent antiretroviral therapy

BACKGROUND

HIV/AIDS remains a leading cause of death worldwide. Recently, a model has been developed in Wenzhou, China, to predict the survival of people living with HIV/AIDS (PLWHA) who underwent antiretroviral therapy (ART). We aimed to evaluate the methodological quality and validate the model in an external population-based cohort.

METHODS

Prediction Model Risk of Bias Assessment Tool (PROBAST) was used to assess the risk of bias of the Wenzhou model. Data were from the National Free Antiretroviral Treatment Program database. We included PLWHA treated between February 2004 and December 2019 in a tertiary hospital in Guangzhou city, China. The endpoint was all-cause deaths and assessed until January 2020. We assessed the discrimination performance of the model by Harrell's overall C-statistics and time-dependent C-statistics and calibration by comparing observed survival probabilities estimated with the Kaplan-Meier method versus predicted survival probabilities. To assess the potential prediction value of age and gender which were precluded in developing the Wenzhou model, we compared the discriminative ability of the original model with an extended model added with age and gender.

RESULTS

Based on PROBAST, the Wenzhou model was rated as high risk of bias in three out of the four domains (selection of participants, definition of outcome, and methods for statistical analysis) mainly because of the misuse of nested case-control design and propensity score matching. In the external validation analysis, 16758 patients were included, among whom 743 patients died (mortality rate 11.41 per 1000 person-years) during follow-up (median 3.41 years, interquartile range 1.64-5.62). The predictor of HIV viral load was missing in 14361 patients (85.7%). The discriminative ability of the Wenzhou model decreased in the external dataset, with the Harrell's overall C-statistics being 0.76, and time-dependent C-statistics dropping from 0.81 at 6 months to 0.48 at 10 years after ART initiation. The model consistently underestimated the survival, and the level was 6.23%, 10.02%, and 14.82% at 1, 2, and 3 years after ART initiation, respectively. The overall and time-dependent discriminative ability of the model improved after adding age and gender to the original model.

CONCLUSION

The Wenzhou prognostic model is at high risk of bias in model development, with inadequate model performance in external validation. Thereby, we could not confirm the validity and extended utility of the Wenzhou model. Future prediction model development and validation studies need to comply with the methodological standards and guidelines specifically developed for prediction models.