UNASSIGNED: Type 3 cardiorenal syndrome (CRS type 3) triggers acute cardiac injury from acute kidney injury (AKI), raising mortality in AKI patients. We aimed to identify risk factors for CRS type 3 and develop a predictive nomogram.
UNASSIGNED: In this retrospective study, 805 AKI patients admitted at the Department of Nephrology, Second Hospital of Shanxi Medical University from 1 January 2017, to 31 December 2021, were categorized into a study cohort (406 patients from 2017.1.1-2021.6.30, with 63 CRS type 3 cases) and a validation cohort (126 patients from 1 July 2021 to 31 Dec 2021, with 22 CRS type 3 cases). Risk factors for CRS type 3, identified by logistic regression, informed the construction of a predictive nomogram. Its performance and accuracy were evaluated by the area under the curve (AUC), calibration curve and decision curve analysis, with further validation through a validation cohort.
UNASSIGNED: The nomogram included 6 risk factors: age (OR = 1.03; 95%CI = 1.009-1.052; p = 0.006), cardiovascular disease (CVD) history (OR = 2.802; 95%CI = 1.193-6.582; p = 0.018), mean artery pressure (MAP) (OR = 1.033; 95%CI = 1.012-1.054; p = 0.002), hemoglobin (OR = 0.973; 95%CI = 0.96--0.987; p < 0.001), homocysteine (OR = 1.05; 95%CI = 1.03-1.069; p < 0.001), AKI stage [(stage 1: reference), (stage 2: OR = 5.427; 95%CI = 1.781-16.534; p = 0.003), (stage 3: OR = 5.554; 95%CI = 2.234-13.805; p < 0.001)]. The nomogram exhibited excellent predictive performance with an AUC of 0.907 in the study cohort and 0.892 in the validation cohort. Calibration and decision curve analyses upheld its accuracy and clinical utility.
UNASSIGNED: We developed a nomogram predicting CRS type 3 in AKI patients, incorporating 6 risk factors: age, CVD history, MAP, hemoglobin, homocysteine, and AKI stage, enhancing early risk identification and patient management.

BACKGROUND: The incidence of major adverse cardiovascular events (MACEs) remains high in patients with acute myocardial infarction (AMI) who undergo percutaneous coronary intervention (PCI), and early prediction models to guide their clinical management are lacking.
OBJECTIVE: This study aimed to develop machine learning-based early prediction models for MACEs in patients with newly diagnosed AMI who underwent PCI.
METHODS: A total of 1531 patients with AMI who underwent PCI from January 2018 to December 2019 were enrolled in this consecutive cohort. The data comprised demographic characteristics, clinical investigations, laboratory tests, and disease-related events. Four machine learning models-artificial neural network (ANN), k-nearest neighbors, support vector machine, and random forest-were developed and compared with the logistic regression model. Our primary outcome was the model performance that predicted the MACEs, which was determined by accuracy, area under the receiver operating characteristic curve, and F1-score.
RESULTS: In total, 1362 patients were successfully followed up. With a median follow-up of 25.9 months, the incidence of MACEs was 18.5% (252/1362). The area under the receiver operating characteristic curve of the ANN, random forest, k-nearest neighbors, support vector machine, and logistic regression models were 80.49%, 72.67%, 79.80%, 77.20%, and 71.77%, respectively. The top 5 predictors in the ANN model were left ventricular ejection fraction, the number of implanted stents, age, diabetes, and the number of vessels with coronary artery disease.
CONCLUSIONS: The ANN model showed good MACE prediction after PCI for patients with AMI. The use of machine learning-based prediction models may improve patient management and outcomes in clinical practice.

BACKGROUND: Low birth weight (LBW) is a leading cause of neonatal morbidity and mortality, and increases various disease risks across life stages. Prediction models of LBW have been developed before, but have limitations including small sample sizes, absence of genetic factors and no stratification of neonate into preterm and term birth groups. In this study, we challenged the development of early prediction models of LBW based on environmental and genetic factors in preterm and term birth groups, and clarified influential variables for LBW prediction.
METHODS: We selected 22,711 neonates, their 21,581 mothers and 8,593 fathers from the Tohoku Medical Megabank Project Birth and Three-Generation cohort study. To establish early prediction models of LBW for preterm birth and term birth groups, we trained AI-based models using genetic and environmental factors of lifestyles. We then clarified influential environmental and genetic factors for predicting LBW in the term and preterm groups.
RESULTS: We identified 2,327 (10.22%) LBW neonates consisting of 1,077 preterm births and 1,248 term births. Our early prediction models archived the area under curve 0.96 and 0.95 for term LBW and preterm LBW models, respectively. We revealed that environmental factors regarding eating habits and genetic features related to fetal growth were influential for predicting LBW in the term LBW model. On the other hand, we identified that genomic features related to toll-like receptor regulations and infection reactions are influential genetic factors for prediction in the preterm LBW model.
CONCLUSIONS: We developed precise early prediction models of LBW based on lifestyle factors in the term birth group and genetic factors in the preterm birth group. Because of its accuracy and generalisability, our prediction model could contribute to risk assessment of LBW in the early stage of pregnancy and control LBW risk in the term birth group. Our prediction model could also contribute to precise prediction of LBW based on genetic factors in the preterm birth group. We then identified parental genetic and maternal environmental factors during pregnancy influencing LBW prediction, which are major targets for understanding the LBW to address serious burdens on newborns\' health throughout life.

UNASSIGNED: When sepsis is detected, organ damage may have progressed to irreversible stages, leading to poor prognosis. The use of machine learning for predicting sepsis early has shown promise, however international validations are missing.
UNASSIGNED: This was a retrospective, observational, multi-centre cohort study. We developed and externally validated a deep learning system for the prediction of sepsis in the intensive care unit (ICU). Our analysis represents the first international, multi-centre in-ICU cohort study for sepsis prediction using deep learning to our knowledge. Our dataset contains 136,478 unique ICU admissions, representing a refined and harmonised subset of four large ICU databases comprising data collected from ICUs in the US, the Netherlands, and Switzerland between 2001 and 2016. Using the international consensus definition Sepsis-3, we derived hourly-resolved sepsis annotations, amounting to 25,694 (18.8%) patient stays with sepsis. We compared our approach to clinical baselines as well as machine learning baselines and performed an extensive internal and external statistical validation within and across databases, reporting area under the receiver-operating-characteristic curve (AUC).
UNASSIGNED: Averaged over sites, our model was able to predict sepsis with an AUC of 0.846 (95% confidence interval [CI], 0.841-0.852) on a held-out validation cohort internal to each site, and an AUC of 0.761 (95% CI, 0.746-0.770) when validating externally across sites. Given access to a small fine-tuning set (10% per site), the transfer to target sites was improved to an AUC of 0.807 (95% CI, 0.801-0.813). Our model raised 1.4 false alerts per true alert and detected 80% of the septic patients 3.7 h (95% CI, 3.0-4.3) prior to the onset of sepsis, opening a vital window for intervention.
UNASSIGNED: By monitoring clinical and laboratory measurements in a retrospective simulation of a real-time prediction scenario, a deep learning system for the detection of sepsis generalised to previously unseen ICU cohorts, internationally.
UNASSIGNED: This study was funded by the Personalized Health and Related Technologies (PHRT) strategic focus area of the ETH domain.

BACKGROUND: Lenvatinib is widely used to treat unresectable and advanced thyroid carcinomas. We aimed to determine whether 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) performed 1 week after lenvatinib treatment initiation could predict treatment outcomes.
RESULTS: This was a prospective, nonrandomised, multicentre study. Patients with pathologically confirmed differentiated thyroid carcinoma (DTC) and lesions refractory to radioiodine treatment were eligible for inclusion. Patients were treated with 24 mg lenvatinib as the initial dose and underwent PET/CT examination 1 week after treatment initiation. Contrast-enhanced CT was scheduled at least 4 weeks later as the gold standard for evaluation. The primary endpoint was to evaluate the discrimination power of maximum standardised uptake value (SUVmax) obtained by PET/CT compared to that obtained by contrast-enhanced CT. Evaluation was performed using the area under the receiver operating characteristic (ROC-AUC) curve. Twenty-one patients were included in this analysis. Receiver operating characteristic (ROC) curve analysis yielded an AUC of 0.714 for SUVmax after 1 week of lenvatinib treatment. The best cut-off value for the treatment response for SUVmax was 15.211. The sensitivity and specificity of this cut-off value were 0.583 and 0.857, respectively. The median progression-free survival was 26.3 months in patients with an under-cut-off value and 19.7 months in patients with an over-cut-off value (P = 0.078).
CONCLUSIONS: The therapeutic effects of lenvatinib were detected earlier than those of CT because of decreased FDG uptake on PET/CT. PET/CT examination 1 week after the initiation of lenvatinib treatment may predict treatment outcomes in patients with DTC.
BACKGROUND: This trial was registered in the University Hospital Medical Information Network (UMIN) Clinical Trials Registry (number UMIN000022592) on 6 June, 2016.

Chimeric antigen receptor T (CAR-T) cell therapy is highly effective in inducing complete remission in haematological malignancies. Severe cytokine release syndrome (CRS) is the most significant and life-threatening adverse effect of this therapy. This multi-centre study was conducted at six hospitals in China. The training cohort included 87 patients with multiple myeloma (MM), an external validation cohort of 59 patients with MM and another external validation cohort of 68 patients with acute lymphoblastic leukaemia (ALL) or non-Hodgkin lymphoma (NHL). The levels of 45 cytokines on days 1-2 after CAR-T cell infusion and clinical characteristics of patients were used to develop the nomogram. A nomogram was developed, including CX3CL1, GZMB, IL4, IL6 and PDGFAA. Based on the training cohort, the nomogram had a bias-corrected AUC of 0.876 (95% CI = 0.871-0.882) for predicting severe CRS. The AUC was stable in both external validation cohorts (MM, AUC = 0.907, 95% CI = 0.899-0.916; ALL/NHL, AUC = 0.908, 95% CI = 0.903-0.913). The calibration plots (apparent and bias-corrected) overlapped with the ideal line in all cohorts. We developed a nomogram that can predict which patients are likely to develop severe CRS before they become critically ill, improving our understanding of CRS biology, and may guide future cytokine-directed therapies.

Anastomotic leakage (AL) is one of the common complications after rectal cancer surgery. This study aimed to evaluate the combination of biomarkers for the early prediction of symptomatic AL after surgery.
A prospective cohort study evaluated the serum and peritoneal biomarkers of patients who underwent laparoscopic low anterior resection (Lap LAR) from November 1, 2021, to May 1, 2022. Multivariate-penalized logistic regression was performed to explore the independent biomarker with a P-value <.1, and receiver operating characteristic (ROC) curve was used to analyze the area under the curve (AUC), sensitivity, and specificity of the independent biomarkers. A predictive model for symptomatic AL was built based on the independent biomarkers and was visualized with a nomogram. The calibration curve with the concordance index (c-index) was further applied to evaluate the efficacy of the predictive model.
A total of 157 patients were included in this study, and 7 (4.5%) were diagnosed with symptomatic AL. C-reactive protein/album ratio (CAR) on postoperative day 1 and systemic immune-inflammation index (SII) and peritoneal interleukin-6 (IL-6) on postoperative day 3 were proven to be independent predictors for the early prediction of symptomatic AL. The optimal cutoff values of CAR, SII, and peritoneal IL-6 were 1.04, 916.99, and 26430.09 pg/ml, respectively. Finally, the nomogram, including these predictors, was established, and the c-index of this nomogram was 0.812, indicating that the nomogram could be used for potential clinical reference.
The combination of CAR, SII, and peritoneal IL-6 might contribute to the early prediction of symptomatic AL in patients following Lap LAR. Given the limitations of this study and the emergence of other novel biomarkers, multicenter prospective studies are worthy of further exploration.

OBJECTIVE: To define risk factors for the early prediction of gestational diabetes mellitus (GDM) because the risk of pre-eclampsia and preterm birth increases in mothers who are diagnosed with GDM.
METHODS: A prospective study was designed and the data were collected by physicians prospectively from the patients who came to the clinic between the years 2019 and 2021; informed consent was obtained from the women. The prospective data comprised 489 patient records with 72 variables and the risk factors for early prediction of GDM were determined using logistic regression and random forest (RF), which is an advanced analysis method.
RESULTS: The obtained sensitivity and specificity values are 90% and 75% for logistic regression and 71% and 90% for the RF, respectively.
CONCLUSIONS: In this prospective study of GDM in Turkish women; age, body mass index, level of hemoglobin A1c, level of fasting blood sugar, physical activity time in first trimester, gravidity, triglycerides, and high-density lipoprotein cholesterol were confirmed to be risk factors in analysis results.

This study aimed to develop simple diagnostic guidelines which would be useful for the early detection of severe dengue infections. Retrospective data of patients with dengue infection were reviewed. Patients with diagnosed dengue infection were categorized in line with the International Statistical Classification of Diseases (ICD-10): A90, dengue fever; A91, dengue hemorrhagic fever; and A910, dengue hemorrhagic fever with shock. A total of 302 dengue-infected patients were enrolled, of which 136 (45%) were male and 166 (55%) were female. Multivariate analysis was conducted to determine independent diagnostic predictors of severe dengue infection and to convert simple diagnostic guidelines into a scoring system for disease severity. Coefficients for significant predictors of disease severity generated by ordinal multivariable logistic regression analysis were transformed into item scores. The derived total scores ranged from 0 to 38.6. The cut-off score for predicting dengue severity was higher than 14, with an area under the receiver operating curve (AUROC) of 0.902. The predicted positive value (PPV) was 68.7% and the negative predictive value (NPV) was 94.1%. Our study demonstrates that several diagnostic parameters can be effectively combined into a simple score sheet with predictive value for the severity evaluation of dengue infection.

UNASSIGNED: Early prediction of treatment response to neoadjuvant chemotherapy (NACT) in patients with human epidermal growth factor receptor 2 (HER2)-positive breast cancer can facilitate timely adjustment of treatment regimens. We aimed to develop and validate a Siamese multi-task network (SMTN) for predicting pathological complete response (pCR) based on longitudinal ultrasound images at the early stage of NACT.
UNASSIGNED: In this multicentre, retrospective cohort study, a total of 393 patients with biopsy-proven HER2-positive breast cancer were retrospectively enrolled from three hospitals in china between December 16, 2013 and March 05, 2021, and allocated into a training cohort and two external validation cohorts. Patients receiving full cycles of NACT and with surgical pathological results available were eligible for inclusion. The key exclusion criteria were missing ultrasound images and/or clinicopathological characteristics. The proposed SMTN consists of two subnetworks that could be joined at multiple layers, which allowed for the integration of multi-scale features and extraction of dynamic information from longitudinal ultrasound images before and after the first /second cycles of NACT. We constructed the clinical model as a baseline using multivariable logistic regression analysis. Then the performance of SMTN was evaluated and compared with the clinical model.
UNASSIGNED: The training cohort, comprising 215 patients, were selected from Yunnan Cancer Hospital. The two independent external validation cohorts, comprising 95 and 83 patients, were selected from Guangdong Provincial People\'s Hospital, and Shanxi Cancer Hospital, respectively. The SMTN yielded an area under the receiver operating characteristic curve (AUC) values of 0.986 (95% CI: 0.977-0.995), 0.902 (95%CI: 0.856-0.948), and 0.957 (95%CI: 0.924-0.990) in the training cohort and two external validation cohorts, respectively, which were significantly higher than that those of the clinical model (AUC: 0.524-0.588, P all < 0.05). The AUCs values of the SMTN within the anti-HER2 therapy subgroups were 0.833-0.972 in the two external validation cohorts. Moreover, 272 of 279 (97.5%) non-pCR patients (159 of 160 (99.4%), 53 of 54 (98.1%), and 60 of 65 (92.3%) in the training and two external validation cohorts, respectively) were successfully identified by the SMTN, suggesting that they could benefit from regime adjustment at the early-stage of NACT.
UNASSIGNED: The SMTN was able to predict pCR in the early-stage of NACT for HER2-positive breast cancer patients, which could guide clinicians in adjusting treatment regimes.
UNASSIGNED: Key-Area Research and Development Program of Guangdong Province (No.2021B0101420006); National Natural Science Foundation of China (No.82071892, 82171920); Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application (No.2022B1212010011); the National Science Foundation for Young Scientists of China (No.82102019, 82001986); Project Funded by China Postdoctoral Science Foundation (No.2020M682643); the Outstanding Youth Science Foundation of Yunnan Basic Research Project (202101AW070001); Scientific research fund project of Department of Education of Yunnan Province(2022J0249). Science and technology Projects in Guangzhou (202201020001;202201010513); High-level Hospital Construction Project (DFJH201805, DFJHBF202105).