Objective:To construct a predictive model for mortality in patients with multiple trauma combined with thoracic injuries and evaluate its predictive value.Methods:A retrospective cohort study was conducted to analyze the clinical data of 184 patients with multiple trauma combined with thoracic injuries admitted to the International Zhuang Medicine Hospital Affiliated to Guangxi University of Chinese Medicine from April 2019 to December 2023, including 129 males and 55 females, aged 19-85 years [(46.1±13.7)years]. According to the prognostic outcomes at 3-month follow-up after discharge, the patients were divided into survival group ( n=145) and death group ( n=39). Data were recorded in both groups at admission, including gender, age, and cause of injury, laboratory tests such as systolic blood pressure, oxygen saturation (SaO 2), hemoglobin (Hb), neutrophil-to-lymphocyte ratio (NLR), and lactate, combined injuries such as the number of combined injuries, number of rib fracture, bilateral rib fracture, first-rib fracture, sternum fracture, thoracic vertebral fracture, bilateral pulmonary contusion, bilateral pneumothorax, subarachnoid hemorrhage, subdural hematoma, epidural hematoma, skull fracture, skull base fracture, cervical vertebral fracture, brain herniation, cerebral contusion, lumbar vertebral fracture, pelvic and abdominal cavity hematoma, liver injury, kidney injury, spleen injury, clavicle fracture, scapular fracture, femoral fracture, and pelvic fracture, and injury scores such as shock index (SI), modified shock index (MSI), injury severity score (ISS), revised trauma score (RTS), Glasgow coma score (GCS), and thoracic trauma severity (TTS) score. Univariate binary logistic regression analysis was used to screen for risk factors of death in patients with multiple trauma combined with thoracic injuries. LASSO regression and multivariate logistic regression analysis were employed to identify predictive variables and independent risk factors for mortality in those patients and to construct a regression equation. A nomogram prediction model based on the regression equation was developed using R language. Receiver operating characteristic (ROC) curves were plotted to evaluate the discrimination of the model. The ROC curves were internally validated using the Bootstrap method with 1 000 resamples. The calibration of the model was assessed using the Hosmer-Lemeshow (H-L) goodness-of-fit test. The clinical application value of the model was evaluated using decision curve analysis (DCA) and clinical impact curve (CIC) analysis. Results:There were statistically significant differences between the survival group and the death group in systolic blood pressure, SaO 2, NLR, lactate, number of combined injuries, subarachnoid hemorrhage, subdural hematoma, skull fracture, skull base fracture, brain herniation, liver injury, SI, MSI, ISS, RTS, GCS, and TTS ( P<0.05 or 0.01). The results of the univariate binary logistic regression analysis showed that the above-mentioned related variables except for systolic blood pressure were all significantly associated with death in patients with multiple trauma combined with thoracic injuries ( P<0.05 or 0.01). Five predictive variables, TTS, GCS, brain herniation, ISS, and lactate were obtained in LASSO regression analysis. The results of the multivariate logistic regression analysis showed that GCS ( OR=0.70, 95% CI 0.58, 0.83), brain herniation ( OR=46.18, 95% CI 4.27, 499.26), TTS ( OR=1.71, 95% CI 1.30, 2.24), and lactate ( OR=1.35, 95% CI 1.01, 1.80) were independent risk factors for death in patients with multiple trauma combined with thoracic injuries ( P<0.05 or 0.01). Based on the aforementioned independent risk factors, a regression formula was constructed as follows: P=e x/(1+e x), with the x=-0.36×"GCS"+3.83×"brain herniation"+0.53×"TTS"+0.30×"lactate levels"-11.03. The area under the ROC curve (AUC) of the predictive model for mortality in patients with multiple trauma combined with thoracic injuries based on the equation was 0.97 (95% CI 0.93, 1.00). The AUC was internally validated using the Bootstrap method with 1 000 samples, resulting in an AUC of 0.97 (95% CI 0.91, 1.00). The results of the H-L goodness-of-fit test showed that the bias-corrected calibration curve of the model was in good consistence with the actual curve and both of them were close to the ideal curve. In the evaluation of the clinical application value of the predictive model, the DCA results showed that the predictive model could achieve good clinical net benefit. The CIC results showed that when the threshold probability was greater than 0.7, the model-identified high-risk patients for death highly matched the patients who actually died. Conclusion:The predictive model for mortality in patients with multiple trauma combined with thoracic injuries based on GCS, brain herniation, TTS, and lactate has good predictive performance and clinical application value.

Objective:To construct a predictive model for mortality in patients with multiple trauma combined with thoracic injuries and evaluate its predictive value.Methods:A retrospective cohort study was conducted to analyze the clinical data of 184 patients with multiple trauma combined with thoracic injuries admitted to the International Zhuang Medicine Hospital Affiliated to Guangxi University of Chinese Medicine from April 2019 to December 2023, including 129 males and 55 females, aged 19-85 years [(46.1±13.7)years]. According to the prognostic outcomes at 3-month follow-up after discharge, the patients were divided into survival group ( n=145) and death group ( n=39). Data were recorded in both groups at admission, including gender, age, and cause of injury, laboratory tests such as systolic blood pressure, oxygen saturation (SaO 2), hemoglobin (Hb), neutrophil-to-lymphocyte ratio (NLR), and lactate, combined injuries such as the number of combined injuries, number of rib fracture, bilateral rib fracture, first-rib fracture, sternum fracture, thoracic vertebral fracture, bilateral pulmonary contusion, bilateral pneumothorax, subarachnoid hemorrhage, subdural hematoma, epidural hematoma, skull fracture, skull base fracture, cervical vertebral fracture, brain herniation, cerebral contusion, lumbar vertebral fracture, pelvic and abdominal cavity hematoma, liver injury, kidney injury, spleen injury, clavicle fracture, scapular fracture, femoral fracture, and pelvic fracture, and injury scores such as shock index (SI), modified shock index (MSI), injury severity score (ISS), revised trauma score (RTS), Glasgow coma score (GCS), and thoracic trauma severity (TTS) score. Univariate binary logistic regression analysis was used to screen for risk factors of death in patients with multiple trauma combined with thoracic injuries. LASSO regression and multivariate logistic regression analysis were employed to identify predictive variables and independent risk factors for mortality in those patients and to construct a regression equation. A nomogram prediction model based on the regression equation was developed using R language. Receiver operating characteristic (ROC) curves were plotted to evaluate the discrimination of the model. The ROC curves were internally validated using the Bootstrap method with 1 000 resamples. The calibration of the model was assessed using the Hosmer-Lemeshow (H-L) goodness-of-fit test. The clinical application value of the model was evaluated using decision curve analysis (DCA) and clinical impact curve (CIC) analysis. Results:There were statistically significant differences between the survival group and the death group in systolic blood pressure, SaO 2, NLR, lactate, number of combined injuries, subarachnoid hemorrhage, subdural hematoma, skull fracture, skull base fracture, brain herniation, liver injury, SI, MSI, ISS, RTS, GCS, and TTS ( P<0.05 or 0.01). The results of the univariate binary logistic regression analysis showed that the above-mentioned related variables except for systolic blood pressure were all significantly associated with death in patients with multiple trauma combined with thoracic injuries ( P<0.05 or 0.01). Five predictive variables, TTS, GCS, brain herniation, ISS, and lactate were obtained in LASSO regression analysis. The results of the multivariate logistic regression analysis showed that GCS ( OR=0.70, 95% CI 0.58, 0.83), brain herniation ( OR=46.18, 95% CI 4.27, 499.26), TTS ( OR=1.71, 95% CI 1.30, 2.24), and lactate ( OR=1.35, 95% CI 1.01, 1.80) were independent risk factors for death in patients with multiple trauma combined with thoracic injuries ( P<0.05 or 0.01). Based on the aforementioned independent risk factors, a regression formula was constructed as follows: P=e x/(1+e x), with the x=-0.36×"GCS"+3.83×"brain herniation"+0.53×"TTS"+0.30×"lactate levels"-11.03. The area under the ROC curve (AUC) of the predictive model for mortality in patients with multiple trauma combined with thoracic injuries based on the equation was 0.97 (95% CI 0.93, 1.00). The AUC was internally validated using the Bootstrap method with 1 000 samples, resulting in an AUC of 0.97 (95% CI 0.91, 1.00). The results of the H-L goodness-of-fit test showed that the bias-corrected calibration curve of the model was in good consistence with the actual curve and both of them were close to the ideal curve. In the evaluation of the clinical application value of the predictive model, the DCA results showed that the predictive model could achieve good clinical net benefit. The CIC results showed that when the threshold probability was greater than 0.7, the model-identified high-risk patients for death highly matched the patients who actually died. Conclusion:The predictive model for mortality in patients with multiple trauma combined with thoracic injuries based on GCS, brain herniation, TTS, and lactate has good predictive performance and clinical application value.

BACKGROUND: Studies have shown that the incidence and severity of corona virus disease 2019 (COVID-19) in patients with lung cancer are higher than those in healthy people. At present, the main anti-tumor treatments for lung cancer include surgery, immunotherapy, chemotherapy, radiotherapy, targeted therapy and anti-angiogenesis therapy. While the effects of different anti-tumor treatments on the occurrence and severity of COVID-19 pneumonia are not uniform. Therefore, we aimed to describe clinical characteristics and antitumor therapy of patients with lung cancer and COVID-19 pneumonia, and examined risk factors for severity in this population. METHODS: From December 1, 2022 to February 15, 2023, a retrospective study was conducted in 217 patients diagnosed with COVID-19 and pathologically confirmed lung cancer in the Jinling Hospital. We collected data about patients' clinical features, antitumor treatment regimen within 6 months, and the diagnosis and treatment of COVID-19. Risk factors for occurrence and severity of COVID-19 pneumonia were identified by univariable and multivariable Logistic regression models. RESULTS: (1) Among the 217 patients included, 51 (23.5%) developed COVID-19 pneumonia, of which 42 (82.4%) were classified as medium and 9 (17.6%) were classified as severe; (2) Univariate and multivariate analysis revealed overweight (OR=2.405, 95%CI: 1.095-5.286) and intrapulmonary focal radiotherapy (OR=2.977, 95%CI: 1.071-8.274) are risk factors for increasing occurrence of COVID-19 pneumonia, while other therapies are not; (3) Chronic obstructive pulmonary disease (COPD) history (OR=7.600, 95%CI: 1.430-40.387) was more likely to develop severe pneumonia and anti-tumor therapies such as intrapulmonary focal radiotherapy, chemotherapy, targeted therapy and immunotherapy did not increase severity. CONCLUSIONS Intrapulmonary focal radiation therapy within 6 months increased the incidence of COVID-19 pneumonia, but did not increase the severity. However, there was no safety concern for chemotherapy, targeted therapy, surgery and immunotherapy.
Humans ; COVID-19 ; Retrospective Studies ; Lung Neoplasms/drug therapy* ; Incidence ; Pneumonia/etiology*

Objective: To investigate the association between parenting style and sleep problems among school aged children with autism spectrum disorder (ASD). Methods: A total of 98 children with ASD aged 6-10 years old and 98 age and gender matched typically developing (TD) children from mainstream schools were recruited. Parenting style and sleep problems were measured via Parent Behavior Inventory (PBI) and Children s Sleep Habits Questionnaire(CSHQ), respectively. The symptom severity and intelligence level were also evaluated. Generalized linear model was used to analyze the relationship between parenting style and sleep problems. Results: There was no statistically significant difference in the parenting style of the two groups of children( P > 0.05 ); weekend sleep time of children with ASD was significantly shorter than that of the TD group [(9.1±0.7)(9.5±0.8)h, P < 0.01 ], and the score of sleep onset delay was significantly higher than that of the TD group[(1.8±0.7)(1.5±0.7), P <0.01]. However, there was no statistically significant difference in the incidence of total sleep problems and various problems between the two groups of children( P >0.05). The parental support/engagement of children with ASD was negatively associated with the total score of sleep problems( β=-2.68, 95%CI =-4.88--0.47), bedtime resistance ( β=-1.65, 95%CI =-2.54--0.77) and sleep anxiety( β=-1.01, 95%CI =-1.70--0.32). The parental hostility/coercion was positively correlated with score of daytime sleepiness( β=1.41, 95%CI =0.53-2.29)( P <0.05). Conclusion Parenting style of support/engagement is associated with lower sleep problems in children with ASD, while hostile/coercion is associated with higher sleep problems. It should be improve parental style to reduce the sleep problems in children with ASD.

Objective: To investigate the associations between early life exposure to particulate matter with an aerodynamic diameter less than 2.5 μm (PM 2.5 ) and the risk of autism spectrum disorder (ASD) among school aged children. Methods: A total of 165 children with ASD and 165 age and gender matched typical development (TD) children were recruited. Children s basic information were obtained via questionnaires, and the severity of ASD symptoms was assessed with Social Responsiveness Scale (SRS). Early life PM 2.5 exposure (preconception, entire pregnancy, and the first two years after birth) were extracted from the Tracking Air Pollution in China (TAP) datasets. Conditional Logistic regression and generalized linear model were used to evaluate the associations of early life exposure to PM 2.5 with the risk and the ASD severity symptoms, respectively. Results: The PM 2.5 exposure of ASD group during preconception[(55.08±9.34)μg/m 3], entire pregnancy[(50.44±8.71)μg/m 3], the first year after birth [(45.04± 8.25 )μg/m 3] and the second year after birth [(40.19±7.12)μg/m 3] were significant higher than those in TD children [(47.66± 7.63 , 44.19±7.16, 38.95±6.07, 35.76±5.65)μg/m 3]( t =7.94, 7.13, 7.70, 6.32, P <0.05). After adjusting for potential confounding, each increase of 1 μg/m 3 in PM 2.5 was associated with higher risk of ASD during preconception ( OR=1.21, 95%CI =1.13-1.29), entire pregnancy( OR=1.18, 95%CI =1.11-1.26), the first year after birth ( OR=1.30, 95%CI =1.18-1.43) and the second year after birth ( OR=1.29, 95%CI =1.17-1.42). No similar results were observed regarding the analyses of SRS total and sub scale scores( P >0.05). Conclusion Early life exposure to PM 2.5 is relate to the risk of ASD, these findings indicated that more attention should be paid to ambient PM pollution in the early life prevention and control of ASD.

Objective:To analyze the value of minute ventilation to carbon dioxide production slope (VE/VCO 2 slope) combined with peak systolic blood pressure (SBP) in predicting prognosis for patients with chronic heart failure (CHF). Methods:A total of 170 patients with CHF who visited the Cardiac Rehabilitation Center of Tongji Hospital Affiliated to Tongji University and completed cardiopulmonary exercise test from March 2007 to December 2018 were enrolled in the study. The clinical data, cardiopulmonary exercise testing results and follow-up information of patients were collected to explore the predictors of all-cause mortality in patients with CHF.Results:The median follow-up time was 647 (182-1 764) days. All-cause death occurred in 34 patients. Compared with surviving patients, the proportion of diabetes and angiotensin-converting enzyme inhibitor/angiotensin Ⅱ receptor blocker (ACEI/ARB) use in fatal patients was significantly higher ( P<0.01). The VE/VCO 2 slope and peak SBP*VE/VCO 2 in the fatal patients were significantly higher, and the peak oxygen consumption (peak VO 2) was lower than those in the surviving patients ( P<0.01). The areas under the receiver operating characteristic curve (AUC) of VE/VCO 2 slope and peak SBP*VE/VCO 2 in predicting all-cause mortality in patients with CHF were 0.648 ( P=0.008) and 0.681 ( P=0.001), respectively; the optimal thresholds were >40.95 ( P=0.008) and > 5 423.50 mmHg (1 mmHg=0.133 kPa, P=0.006), the sensitivity was 0.559 and 0.588, and the specificity was 0.728 and 0.735, respectively. Multivariate Cox regression analysis showed that after adjusting for age, gender, diabetes and ACEI/ARB use, VE/VCO 2 slope ( HR=2.12, P=0.036) and peak SBP*VE/VCO 2 ( HR=2.42, P=0.016) were independent risk factors for all-cause mortality in patients with CHF. Conclusion:Compared to the traditional index VE/VCO 2 slope, a novel index peak SBP* VE/VCO 2 provides a relatively better predictive value for all-cause death of CHF patients.

Objective:To explore the value of nursing checklist in posterior surgery for thoracolumbar fracture with general anesthesia under prone position.Methods:A retrospective case series study was conducted to analyze the clinical data of 106 patients with thoracolumbar fracture admitted to Sir Run Run Shaw Hospital，Zhejiang University School of Medicine from June 2018 to May 2020. There were 80 males and 26 females，with age range of 25-57 years［（48.6 ± 11.9）years］. Segments of injury were located at T 11 in 18 patients，at T 12 in 26，at L 1 in 21，at L 2 in 25 and at L 3 in 16. All patients were treated with thoracolumbar posterior screw fixation under general anesthesia. Of all，51 patients received conventional postural nursing from June 2018 to May 2019（control group），and 55 patients received prone position nursing scheme for general anesthesia on the basis of conventional postural nursing from June 2019 to May 2020（verification group）. The incidence of postoperative complications including stress injury，brachial plexus injury，ulnar nerve injury and ocular discomfort as well as length of hospital stay and patients’ satisfaction were compared between the two groups. Visual analogue scale（VAS）and Oswestry disability index（ODI）were also used to measures outcome at postoperative 3 months. Results:All patients were followed up for 8-12 months［（10.5±0.9）months］. Verification group and control group showed significant differences in the incidence of stress injury（4%∶29%），brachial plexus injury（4%∶16%）and ocular discomfort consisiting of tears（2%∶12%），foreign body sensation（0%∶4%）and dryness（4%∶16%）（ P < 0.05），not in ulnar nerve injury and blurred vision. Length of hospitalization in verification group was（7.0±1.3）days，significantly shorter than that in control group［（9.9±1.9）days］（ P < 0.05）. Satisfaction of patients in verification group and control group was 85%（47/55）and 69%（35/51），respectively（ P < 0.05）. At 3 months postoperatively，VAS in verification group［（1.9 ± 0.8）points］was significantly lower than that in control group［（3.5±1.1）points］（ P < 0.05），and ODI was similar between the two groups（ P > 0.05）. Conclusions For patients with thoracolumbar fracture treated by posterior surgery with general anaesthesia under prone position，nursing checklist helps reduce occurrence of the related complication，shorten length of hospital stay，improve patient satisfaction，reduce postoperative pain and promote rehabilitation.

Objective:To analyze the effects of various factors on the clinical outcome of artificial insemination with donor sperm (AID) under natural cycles.Methods:A total of 16 458 natural cycles with donor sperm were analyzed from January 2011 to December 2018 in Reproductive Center of Guangdong Province Family Planning Science and Technology Research Institute. The relationship between the clinical outcome and the factors such as the women's character, donor sperm quality and cycle related factors with χ 2 and multiple factor generalized estimating equation. Results:Many factors such as women's age ≤ 30 years ( OR=1.865, P<0.001), the woman's age from 31 to 35 years ( OR=1.215, P<0.001), duration of infertility≤5 ( OR=1.139, P=0.007), day 3 luteining hormone (LH) level>8.10 IU/L ( OR=1.309, P=0.022), day 3 estrogen level≤77.10 pmol/L ( OR=1.301, P=0.012), day 3 estrogen level from 77.11 pmol/L to 293.60 pmol/L ( OR=1.099, P=0.044), one dominant follicle per cycle ( OR=1.473, P=0.038), cervical mucus score ≥10 ( OR=1.256, P=0.026), A type endometrium ( OR=1.114, P=0.005), urinary LH strong positive ( OR=1.171, P=0.002), sperm activity ratio more than 54% after thawing ( OR=1.142, P=0.002), progressively motile sperm number ≥ 35×10 6 after thawing ( OR=1.217, P=0.001) and double inseminations per cycle ( OR=1.376, P=0.001) significantly affected the pregnancy rates of AID women under natural cycles. Conclusion:Many factors such as the woman's age, duration of infertility, day 3 LH level, day 3 estrogen level, dominant follicle number per cycle, cervical mucus score, endometrial type, sperm activity ratio after thawing, progressively motile sperm number and insemination times per cycle can affect the women’s pregnancy rate under AID natural cycles.

Objective:To analyze the effects of various factors on the clinical outcome of artificial insemination with donor sperm (AID) under natural cycles.Methods:A total of 16 458 natural cycles with donor sperm were analyzed from January 2011 to December 2018 in Reproductive Center of Guangdong Province Family Planning Science and Technology Research Institute. The relationship between the clinical outcome and the factors such as the women's character, donor sperm quality and cycle related factors with χ 2 and multiple factor generalized estimating equation. Results:Many factors such as women's age ≤ 30 years ( OR=1.865, P<0.001), the woman's age from 31 to 35 years ( OR=1.215, P<0.001), duration of infertility≤5 ( OR=1.139, P=0.007), day 3 luteining hormone (LH) level>8.10 IU/L ( OR=1.309, P=0.022), day 3 estrogen level≤77.10 pmol/L ( OR=1.301, P=0.012), day 3 estrogen level from 77.11 pmol/L to 293.60 pmol/L ( OR=1.099, P=0.044), one dominant follicle per cycle ( OR=1.473, P=0.038), cervical mucus score ≥10 ( OR=1.256, P=0.026), A type endometrium ( OR=1.114, P=0.005), urinary LH strong positive ( OR=1.171, P=0.002), sperm activity ratio more than 54% after thawing ( OR=1.142, P=0.002), progressively motile sperm number ≥ 35×10 6 after thawing ( OR=1.217, P=0.001) and double inseminations per cycle ( OR=1.376, P=0.001) significantly affected the pregnancy rates of AID women under natural cycles. Conclusion:Many factors such as the woman's age, duration of infertility, day 3 LH level, day 3 estrogen level, dominant follicle number per cycle, cervical mucus score, endometrial type, sperm activity ratio after thawing, progressively motile sperm number and insemination times per cycle can affect the women’s pregnancy rate under AID natural cycles.