Integrating Machine Learning into Statistical Methods in Disease Risk Prediction Modeling: A Systematic Review.

Background: Disease prediction models often use statistical methods or machine learning, both with their own corresponding application scenarios, raising the risk of errors when used alone. Integrating machine learning into statistical methods may yield robust prediction models. This systematic review aims to comprehensively assess current development of global disease prediction integration models. Methods: PubMed, EMbase, Web of Science, CNKI, VIP, WanFang, and SinoMed databases were searched to collect studies on prediction models integrating machine learning into statistical methods from database inception to 2023 May 1. Information including basic characteristics of studies, integrating approaches, application scenarios, modeling details, and model performance was extracted. Results: A total of 20 eligible studies in English and 1 in Chinese were included. Five studies concentrated on diagnostic models, while 16 studies concentrated on predicting disease occurrence or prognosis. Integrating strategies of classification models included majority voting, weighted voting, stacking, and model selection (when statistical methods and machine learning disagreed). Regression models adopted strategies including simple statistics, weighted statistics, and stacking. AUROC of integration models surpassed 0.75 and performed better than statistical methods and machine learning in most studies. Stacking was used for situations with >100 predictors and needed relatively larger amount of training data. Conclusion: Research on integrating machine learning into statistical methods in prediction models remains limited, but some studies have exhibited great potential that integration models outperform single models. This study provides insights for the selection of integration methods for different scenarios. Future research could emphasize on the improvement and validation of integrating strategies.

Clinical association between plan complexity and the local-recurrence-free-survival of non-small-cell lung cancer patients receiving stereotactic body radiation therapy.

PURPOSE: To investigate the clinical impact of plan complexity on the local recurrence-free survival (LRFS) of non-small cell lung cancer (NSCLC) patients treated with stereotactic body radiation therapy (SBRT). METHODS: Data from 123 treatment plans for 113 NSCLC patients were analyzed. Plan-averaged beam modulation (PM), plan beam irregularity (PI), monitor unit/Gy (MU/Gy) and spherical disproportion (SD) were calculated. The γ passing rates (GPR) were measured using ArcCHECK 3D phantom with 2 %/2mm criteria. High complexity (HC) and low complexity (LC) groups were statistically stratified based on the aforementioned metrics, using cutoffs determined by their significance in correlation with survival time, as calculated using the R-3.6.1 packages. Kaplan-Meier analysis, Cox regression, and Random Survival Forest (RSF) models were employed for the analysis of local recurrence-free survival (LRFS). Propensity-score-matched pairs were generated to minimize bias in the analysis. RESULTS: The median follow-up time for all patients was 25.5 months (interquartile range 13.4-41.2). The prognostic capacity of PM was suggested using RSF, based on Variable Importance and Minimal Depth methods. The 1-, 2-, and 3-year LRFS rates in the HC group were significantly lower than those in the LC group (p = 0.023), when plan complexity was defined by PM. However, no significant difference was observed between the HC and LC groups when defined by other metrics (p > 0.05). All γ passing rates exceeded 90.5 %. CONCLUSIONS: This study revealed a significant association between higher PM and worse LRFS in NSCLC patients treated with SBRT. This finding offers additional clinical evidence supporting the potential optimization of pre-treatment quality assurance protocols.

PM2.5 constituents associated with childhood obesity and larger BMI growth trajectory: A 14-year longitudinal study.

BACKGROUND: The association of specific PM2.5 chemical constituents with childhood overweight or obesity (OWOB) remain unclear. Furthermore, the long-term impacts of PM2.5 exposure on the trajectory of children's body mass index (BMI) have not been explored. METHODS: We conducted a longitudinal study among 1,450,830 Chinese children aged 6-19 years from Beijing and Zhongshan in China during 2005-2018 to examine the associations of PM2.5 and its chemical constituents with incident OWOB risk. We extracted PM2.5 mass and five main component exposure from Tracking Air Pollution in China (TAP) dataset. Cox proportional hazards models were applied to quantify exposure-response associations. We further performed principal component analysis (PCA) to handle the multi-collinearity and used quantile g-computation (QGC) approach to analyze the impacts of exposure mixtures. Additionally, we selected 125,863 children with at least 8 physical examination measurements and combined group-based trajectory models (GBTM) with multinomial logistic regression models to explore the impacts of exposure to PM2.5 mass and five constituents on BMI and BMI Z-score trajectories during 6-19 years. RESULTS: We observed each interquartile range increment in PM2.5 exposure was significantly associated with a 5.1 % increase in the risk of incident OWOB (95 % confidence Interval [CI]: 1.036-1.066). We also found black carbon, sulfate, organic matter, often linked to fossil combustion, had comparable or larger estimates of the effect (HR = 1.139-1.153) than PM2.5. Furthermore, Exposure to PM2.5 mass, sulfate, nitrate, ammonium, organic matter and black carbon was significantly associated with an increased odds of being in a larger BMI trajectory and being assigned to persistent OWOB trajectory. CONCLUSIONS: Our findings provide evidence that the constituents mainly from fossil fuel combustion may have a perceptible influence on increased OWOB risk associated with PM2.5 exposure in China. Moreover, long-term exposure to PM2.5 contributes to an increased odds of being in a lager BMI and a persistent OWOB trajectories.

Impact of Comorbidity on the Duration from Symptom Onset to Death in Patients with Coronavirus Disease 2019: A Retrospective Study of 104,753 Cases in Pakistan.

(1) Background: The evidence indicates that comorbidities are associated with an increase in the risk of death from coronavirus disease 2019 (COVID-19). It is unclear whether such an association is different for various combinations of chronic disease comorbidities. (2) Methods: From 16 March 2020 to 30 November 2021, 104,753 patients with confirmed COVID-19 from Khyber Pakhtunkhwa Province, Pakistan, were studied to determine the association between comorbidities and the duration from symptom onset to death in patients with COVID-19 by stratifying their comorbidity status. (3) Results: The patients with comorbidities had an 84% (OR, 0.16; 95% CI, 0.14 to 0.17) decrease in the duration from symptom onset to death, as opposed to patients without a comorbidity. Among the patients with only one comorbidity, chronic lung disease (OR, 0.06; 95% CI, 0.03 to 0.09) had a greater impact on the duration from symptom onset to death than hypertension (OR, 0.15; 95% CI, 0.13 to 0.18) or diabetes (OR, 0.15; 95% CI, 0.12 to 0.18). The patients with both hypertension and diabetes had the shortest duration (OR, 0.17; 95% CI, 0.14 to 0.20) among the patients with two comorbidities. (4) Conclusions: Comorbidity yielded significant adverse impacts on the duration from symptom onset to death in COVID-19 patients in Pakistan. The impact varied with different combinations of chronic disease comorbidities in terms of the number and type of comorbidities.

Stereotactic ablative brachytherapy with or without assistance of 3D-printing templates for inoperable locally recurrent or oligometastatic soft-tissue sarcoma: a multicenter real-world study.

The management of inoperable locally recurrent or oligometastatic soft-tissue sarcoma (STS) remains a clinical challenge. This study aimed to explore the long-term outcomes of stereotactic ablative brachytherapy (SABT) for these patients. Patients diagnosed with inoperable locally recurrent or oligometastatic STS from eight hospitals between 2006 and 2021 underwent iodine-125 (I-125) seed SABT, either with or without the assistance of three-dimensional (3D)-printing templates. The analysis concentrated on several key parameters, including objective response rate (ORR), disease control rate (DCR), local control time (LCT), overall survival (OS), adverse events (AEs), pain relief rate, and performance improvement rate. The ORR and DCR reached 78.3% and 95.0%, respectively. The results of multivariate logistic regression analysis indicated that a smaller tumor volume and a higher treatment dose were significantly associated with complete response (P < 0.001; P=0.036). The 1-, 3-, and 5-year LCT rates were 73.2%, 40.6%, and 37.9%, respectively. The 1-, 3-, and 5-year OS rates reached 83.1%, 50.5%, and 36.1%, respectively. Multivariate analysis revealed that a higher dose, a smaller tumor volume, and utilization of 3D-printing templates were significantly positive prognostic factors of LCT (P=0.006; P=0.007; P=0.034). Moreover, the tumor locations of trunk wall and extremities and lower tumor grade (G1/2) were significantly positive prognostic factors of survival (P=0.008; P=0.002). Pain relief rate was 88.0%, and the performance improvement rate was 46.7%. The AEs were predominantly of grade ≤ 2 and were well-tolerated. SABT seems to be an efficacious and safe alternative therapy for inoperable locally recurrent or oligometastatic STS.