ObjectiveTo review individual treatment effect (ITE) models developed from randomized controlled trials, with the aim of systematically summarizing the current state of model development and assessing the risk of bias. MethodsPubMed and Embase databases were searched for studies published between 1990 and 14 June 2024. Data were extracted using the CHARMS inventory, and the PROBAST risk of bias tool was used to assess model quality. ResultsA total of 11 publications were included, containing 19 ITE models. The ITE modelling methods were regression models with interaction terms (n=8, 42.1%), dual-range models (n=5, 26.3%) and machine learning (n=6, 31.6%). The ITE models had a reporting rate of 78.9%, 73.2% and 10.5% for differentiation, calibration and clinical validity, respectively. Fourteen models were assessed as having a high risk of bias (73.7%), particularly in the area of statistical analysis, due to inappropriate handling of missing data (n=15, 78.9%), inappropriate consideration of model fit issues (n=5, 26.3%), etc. ConclusionCommon approaches to ITE model development include constructing interaction terms, dual procedure theory, and machine learning, but suffer from a low number of model developments, more complex modeling methods, and non-standardized reporting. In the future, emphasis should be placed on further exploration of ITE models, promoting diversified modeling methods and standardized reporting to improve the clinical promotion and practical application value of the models.
The specific binding of T cell receptors (TCRs) to antigenic peptides plays a key role in the regulation and mediation of the immune process and provides an essential basis for the development of tumour vaccines. In recent years, studies have mainly focused on TCR prediction of major histocompatibility complex (MHC) class I antigens, but TCR prediction of MHC class II antigens has not been sufficiently investigated and there is still much room for improvement. In this study, the combination of MHC class II antigen peptide and TCR prediction was investigated using the ProtT5 grand model to explore its feature extraction capability. In addition, the model was fine-tuned to retain the underlying features of the model, and a feed-forward neural network structure was constructed for fusion to achieve the prediction model. The experimental results showed that the method proposed in this study performed better than the traditional methods, with a prediction accuracy of 0.96 and an AUC of 0.93, which verifies the effectiveness of the model proposed in this paper.
Objective To explore the change of serum levels of neutrophil gelatinase-associated lipocalin (NGAL), tissue inhibitor of metalloproteinases-2 (TIMP-2), and insulin-like growth factor-binding protein 7 (IGFBP-7) in the early stage of multiple trauma, and their predictive efficacy for acute kidney injury (AKI). Methods The multiple trauma patients admitted between February 2020 and July 2021 were prospectively selected, and they were divided into AKI group and non-AKI group according to whether they developed AKI within 72 h after injury. The serum levels of NGAL, TIMP-2, and IGFBP-7 measured at admission and 12, 24, and 48 h after injury, the Acute Pathophysiology and Chronic Health Evaluation Ⅱ(APACHE Ⅱ) score, intensive care unit duration, rate of renal replacement therapy, and 28-day mortality rate were compared between the two groups. Results A total of 51 patients were included, including 20 in the AKI group and 31 in the non-AKI group. The APACHE Ⅱ at admission (20.60±3.57 vs. 11.61±3.44), intensive care unit duration [(16.75±2.71) vs. (11.13±3.41) d], rate of renal replacement therapy (35.0% vs. 0.0%), and 28-day mortality rate (25.0% vs. 3.2%) in the AKI group were higher than those in the non-AKI group (P<0.05). The serum levels of NGAL and IGFBP-7 at admission and 12, 24, and 48 h after injury in the AKI group were all higher than those in the non-AKI group (P<0.05). For the prediction of AKI, the areas under receiver operating characteristic curves and 95% confidence intervals of serum NGAL, TIMP-2 and IGFBP-7 12 h after injury were 0.98 (0.96, 1.00), 0.92 (0.83, 1.00), and 0.87 (0.78, 0.97), respectively. Conclusion Serum NGAL, TIMP-2, and IGFBP-7 have high predictive efficacy for AKI secondary to multiple trauma, and continuous monitoring of serum NGAL can be used for early prediction of AKI secondary to multiple trauma.
ObjectiveConstructing a prediction model for seizures after stroke, and exploring the risk factors that lead to seizures after stroke. MethodsA retrospective analysis was conducted on 1 741 patients with stroke admitted to People's Hospital of Zhongjiang from July 2020 to September 2022 who met the inclusion and exclusion criteria. These patients were followed up for one year after the occurrence of stroke to observe whether they experienced seizures. Patient data such as gender, age, diagnosis, National Institute of Health Stroke Scale (NIHSS) score, Activity of daily living (ADL) score, laboratory tests, and imaging examination data were recorded. Taking the occurrence of seizures as the outcome, an analysis was conducted on the above data. The Least absolute shrinkage and selection operator (LASSO) regression analysis was used to screen predictive variables, and multivariate Logistic regression analysis was performed. Subsequently, the data were randomly divided into a training set and a validation set in a 7:3 ratio. Construct prediction model, calculate the C-index, draw nomogram, calibration plot, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) to evaluate the model's performance and clinical application value. ResultsThrough LASSO regression, nine non-zero coefficient predictive variables were identified: NIHSS score, homocysteine (Hcy), aspartate aminotransferase (AST), platelet count, hyperuricemia, hyponatremia, frontal lobe lesions, temporal lobe lesions, and pons lesions. Multivariate logistic regression analysis revealed that NIHSS score, Hcy, hyperuricemia, hyponatremia, and pons lesions were positively correlated with seizures after stroke, while AST and platelet count were negatively correlated with seizures after stroke. A nomogram for predicting seizures after stroke was established. The C-index of the training set and validation set were 0.854 [95%CI (0.841, 0.947)] and 0.838 [95%CI (0.800, 0.988)], respectively. The areas under the ROC curves were 0.842 [95%CI (0.777, 0.899)] and 0.829 [95%CI (0.694, 0.936)] respectively. Conclusion These nine variables can be used to predict seizures after stroke, and they provide new insights into its risk factors.
Objective
To explore the predicted precision of discharged patients number using curve estimation combined with trend-season model.
Methods
Curve estimation and trend-season model were both applied, and the quarterly number of discharged patients of 363 hospital from 2009 to 2015 was collected and analyzed in order to predict discharged patients in 2016. Relative error between predicted value and actual number was also calculated.
Results
An optimal quadratic regression equation Yt=3 006.050 1+202.350 8×t–3.544 4×t2 was established (Coefficient of determination R2=0.927, P<0.001), and a total of 23 462 discharged patients were predicted based on this equation combined with trend-season model, with a relative error of 1.79% compared to the actual number.
Conclusion
The curve estimation combined with trend-season model is a convenient and visual tool for predicting analysis. It has a high predicted accuracy in predicting the number of hospital discharged patients or outpatients, which can provide a reference basis for hospital operation and management.
ObjectiveTo explore the risk factors for accompanying depression in patients with community type Ⅱ diabetes and to construct their risk prediction model. MethodsA total of 269 patients with type Ⅱ diabetes accompanied with depression and 217 patients with simple type Ⅱ diabetes from three community health service centers in two streets of Pingshan District, Shenzhen from October 2021 to April 2022 were included. The risk factors were analyzed and screened out, and a logistic regression risk prediction model was constructed. The goodness of fit and prediction ability of the model were tested by the Hosmer-Lemeshow test and the receiver operating characteristic (ROC) curve. Finally, the model was verified. ResultsLogistic regression analysis showed that smoking, diabetes complications, physical function, psychological dimension, medical coping for face, and medical coping for avoidance were independent risk factors for depressive disorder in patients with type Ⅱ diabetes. Modeling group Hosmer-Lemeshow test P=0.345, the area under the ROC curve was 0.987, sensitivity was 95.2% and specificity was 98.6%. The area under the ROC curve was 0.945, sensitivity was 89.8%, specificity was 84.8%, and accuracy was 86.8%, showing the model predictive value. ConclusionThe risk prediction model of type Ⅱ diabetes patients with depressive disorder constructed in this study has good predictive and discriminating ability.
ObjectivesTo evaluate the predicting value of bedside pulmonary ultrasound in bronchopulmonary dysplasia (BPD) in premature infants.MethodsPremature infants with gestational age below 28 weeks or birth weight below 1 500 g admitted to NICU of Chengdu Women and Children’s Central Hospital from June 2018 to June 2019 were included. Pulmonary bedside ultrasound monitoring was performed on the 3rd, 7th, 14th and 28th day after admission, and the characteristic ultrasound images were recorded and scored. BPD were diagnosed by NICHD standard. The clinical data and pulmonary ultrasound data were compared and analyzed. Then diagnostic value of bedside pulmonary ultrasound in BPD of premature infants were analyzed.ResultsA total of 81 children involving 32 BPD and 49 non-BPD were included. The sensitivity (Sen), specificity (Spe) and area under curve (AUC) of receiver operating characteristic (ROC) of the "alveolar-interstitial syndrome" within 3 days after birth and the "fragment sign" on 28 days after birth were 81.25%, 51.02%, 0.66 and 31.25%, 97.96%, 0.65, respectively. The lung ultrasound scores in the BPD group on the 3rd, 7th, 14th, and 28th day after birth were 71.99.%, 68.39%, 0.71; 87.50%, 57.14%, 0.72; 78.13%, 73.47%, 0.76 and 56.25 %, 75.51%, 0.66. Sen, Spe and ROC AUC of comprehensive evaluation of lung ultrasound predicted the occurrence of BPD been 81.25%, 63.27%, and 0.85.ConclusionsThe comprehensive evaluation of combination of "alveolar interstitial syndrome" image characteristics within 3 days after birth, "fragment sign" image characteristics after 28 days, and lung ultrasound score at different times after birth can predict the premature infants with bronchopulmonary dysplasia.
ObjectiveTo systematically review the risk prediction models for the occurrence of low anterior resection syndrome in patients with rectal cancer after surgery. MethodsThe PubMed, Web of Science, Embase, Cochrane Library, Scopus, CINHAL, CNKI, CBM, WanFang Data and VIP databases were electronically searched to collect studies related to the objectives from inception to June 13, 2023. Two reviewers independently screened the literature, extracted data using the critical appraisal and data extraction for systematic reviews of prediction modelling studies (CHARMS) checklist, and assessed quality of the included studies using prediction model risk of bias assessment tool (PROBAST). ResultsA total of 14 studies were included, all studies reported model discrimination, and 10 studies reported calibration. The models were internally validated in 8 studies, externally validated in 5 studies. The most common predictors included in the models were tumour distance from the anal verge, neoadjuvant therapy, anastomotic leak and BMI. Only 5 studies had good overall applicability, and all studies had a high risk of bias, with the risk of bias mainly stemming from the field of participants, outcomes and analysis. ConclusionThere are still many shortcomings in the risk prediction models for the occurrence of low anterior resection syndrome in patients with rectal cancer after surgery. Future studies may consider external validation and recalibration of existing models. New prediction models should be built and validated according to methodological guidelines.
ObjectiveTo explore the independent factors related to clinical severe events in community acquired pneumonia patients and to find out a simple, effective and more accurate prediction method.MethodsConsecutive patients admitted to our hospital from August 2018 to July 2019 were enrolled in this retrospective study. The endpoint was the occurrence of severe events defined as a condition as follows intensive care unit admission, the need for mechanical ventilation or vasoactive drugs, or 30-day mortality during hospitalization. The patients were divided into severe event group and non-severe event group, and general clinical data were compared between two groups. Multivariate logistic regression analysis was performed to identify the independent predictors of adverse outcomes. Receiver operating characteristic (ROC) curve was constructed to calculate and compare the area under curve (AUC) of different prediction methods.ResultsA total of 410 patients were enrolled, 96 (23.4%) of whom experienced clinical severe events. Age (OR: 1.035, 95%CI: 1.012 - 1.059, P=0.003), high-density lipoprotein (OR: 0.266, 95%CI: 0.088 - 0.802, P=0.019) and lactate dehydrogenase (OR: 1.006, 95%CI: 1.004 - 1.059, P<0.001) levels on admission were independent factors associated with clinical severe events in CAP patients. The AUCs in the prediction of clinical severe events were 0.744 (95%CI: 0.699 - 0.785, P=0.028) and 0.814 (95%CI: 0.772 - 0.850, P=0.025) for CURB65 and PSI respectively. CURB65-LH, combining CURB65, HDL and LDH simultaneously, had the largest AUC of 0.843 (95%CI: 0.804 - 0.876, P=0.022) among these prediction methods and its sensitivity (69.8%) and specificity (81.5%) were higher than that of CURB65 (61.5% and 76.1%) respectively.ConclusionCURB65-LH is a simple, effective and more accurate prediction method of clinical severe events in CAP patients, which not only has higher sensitivity and specificity, but also significantly improves the predictive value when compared with CURB65.
Objective
To explore the predictive factors for long-term adverse prognosis in patients with tuberculosis meningitis.
Methods
We retrospectively analyzed the clinical data (general clinical data, laboratory test results, and imaging findings) of hospitalized cases of tuberculosis meningitis admitted to West China Hospital of Sichuan University from 00:00:00 on August 1st, 2011 to 23:59:59 on July 31st, 2012. We collected data of prognosis results after 6 years of illness by telephone follow-up, and quantified outcome measures by modified Rankin Scale (mRS) score (0–6 points). According to the mRS score, the cases obtaining 0 points≤mRS<3 points were divided into the good prognosis group and the cases obtaining 3≤mRS≤6 points were divided into the poor prognosis group, logistic regression analysis was executed to find the independent risk factors affecting long-term poor prognosis.
Results
A total of 119 cases were included, including 63 males and 56 females; the average age was (35±17) years. Among them, 53 patients had poor prognosis and 66 patients had good prognosis. After univariate analysis, the age (t=–3.812, P<0.001), systolic blood pressure at admission (t=–2.009, P=0.049), Glasgow Coma Scale score (t=3.987, P<0.001), Medical Research Council (MRC) staging system (Z=–4.854, P<0.001), headache (χ2=4.101, P=0.043), alter consciousness (χ2=10.621, P=0.001), cognitive dysfunction (χ2=4.075, P=0.044), cranial nerve palsy (χ2=5.853, P=0.016), peripheral nerve dysfunction (χ2=14.925, P<0.001), meningeal irritation (χ2=7.174, P=0.007), serum potassium (t=3.080, P=0.003), cerebrospinal fluid protein content (Z=–2.568, P=0.010), cerebrospinal fluid chlorine (t=2.543, P=0.012), hydrocephalus (χ2=11.766, P=0.001), and cerebral infarction (χ2=6.539, P=0.012) were associated with long-term poor prognosis of tuberculosis meningitis. Multivariate analysis showed that age [odds ratio (OR)=1.061, 95% confidence interval (CI) (1.027, 1.096), P<0.001], peripheral nerve dysfunction [OR=3.537, 95%CI (1.070, 11.697), P=0.038], MRC Stage Ⅱ[OR=9.317, 95%CI (1.692, 51.303), P=0.010], MRC Stage Ⅲ [OR=43.953, 95%CI (3.996, 483.398), P=0.002] were the independent risk factors for long-term poor prognosis of tuberculosis meningitis. Hydrocephalus [OR=2.826, 95%CI (0.999, 8.200), P=0.050] might be an independent risk factor for long-term poor prognosis of tuberculosis meningitis.
Conclusions
Age, MRC staging system (Stage Ⅱ, Stage Ⅲ) and peripheral neurological dysfunction are chronic poor-prognostic independent risk factors for tuberculosis meningitis. Hydrocephalus may be associated with long-term adverse prognosis of tuberculosis meningitis
