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308018 occurrences (No.72 in the rank) during 5 years in the PubMed. [cache]

431) The included 307 patients were divided into modeling group (219 cases) and validation group (88 cases) according to the random number table with a ratio of about 7∶3.

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PMID:34139830 DOI:10.3760/cma.j.cn501120-20210114-00021

2021 Zhonghua shao shang za zhi = Zhonghua shaoshang zazhi = Chinese journal of burns

* [Establishment of an early risk prediction model for bloodstream infection and analysis of its predictive value in patients with extremely severe burns].

- Objective: To establish an early prediction model for bloodstream infection in patients with extremely severe burns based on the screened independent risk factors of the infection, and to analyze its predictive value. Methods: A retrospective case-control study was conducted. From January 1, 2010 to December 31, 2019, 307 patients with extremely severe burns were admitted to the Department of Burns and Plastic Surgery of Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medcine, including 251 males and 56 females, aged from 33 to 55 years. According to the occurrence of bloodstream infection, the patients were divided into non-bloodstream infection group (221 cases) and bloodstream infection group (86 cases). The gender, age, body mass index, outcome, length of hospital stay of patients were compared between the two groups, and the detection of bacteria in blood microbial culture of patients was analyzed in bloodstream infection group. The included 307 patients were divided into modeling group (219 cases) and validation group (88 cases) according to the random number table with a ratio of about 7∶3. The gender, age, body mass index, total burn area, full-thickness burn area, combination of inhalation injury, implementation of mechanical ventilation, days of mechanical ventilation, days of intensive care unit (ICU) stay, outcome, length of hospital stay, complication of bloodstream infection of patients were compared between the two groups. According to the occurrence of bloodstream infection, the patients in modeling group were divided into bloodstream infection subgroup (154 cases) and non-bloodstream infection subgroup (165 cases). The total burn area, full-thickness burn area, combination of inhalation injury, implementation of mechanical ventilation, days of mechanical ventilation, and days of ICU stay of patients were compared between the two subgroups. The above-mentioned data between two groups were statistically analyzed with one-way analysis of independent sample t test, chi-square test, and Mann-Whitney U test to screen out the factors with statistically significant differences in the subgroup univariate analysis of modeling group. The factors were used as variables, and binary multivariate logistic regression analysis was performed to screen out the independent risk factors of bloodstream infection in patients with extremely severe burns, based on which the prediction model for bloodstream infection in patients with extremely severe burns of modeling group was established. The receiver operating characteristic (ROC) curve of the prediction model predicting the risk of bloodstream infection of patients in modeling group was drawn, and the area under the ROC curve was calculated. The sensitivity, specificity, and the best prediction probability were calculated according to the Youden index. According to the occurrence of bloodstream infection, the patients in validation group were divided into bloodstream infection subgroup (21 cases) and non-bloodstream infection subgroup (67 cases). The prediction probability >the best prediction probability of model was used as the judgment standard of bloodstream infection. The prediction model was used to predict the occurrence of bloodstream infection of patients in the two subgroups of validation group, and the incidence, specificity, and sensitivity for predicting bloodstream infection were calculated. In addition, the ROC curve of the prediction model predicting the risk of bloodstream infection of patients in validation group was drawn, and the area under the ROC curve was calculated. Results: Compared with those of non-bloodstream infection group, the mortality of patients in bloodstream infection group was significantly higher (χ²=8.485, P<0.01), the length of hospital stay was significantly increased (Z=-3.003, P<0.01), but there was no significant change in gender, age, or body mass index (P>0.05). In patients of bloodstream infection group, 110 strains of bacteria were detected in blood microbial culture, among which Klebsiella pneumoniae, Pseudomonas aeruginosa, and Acinetobacter baumannii were the top three bacteria, accounting for 35.45% (39/110), 26.36% (29/110), and 13.64% (15/110), respectively. Gender, age, body mass index, total burn area, full-thickness burn area, proportion of combination of inhalation injury, proportion of implementation of mechanical ventilation, days of mechanical ventilation, days of ICU stay, outcome, length of hospital stay, and proportion of complication of bloodstream infection of patients were similar between modeling group and validation group (P>0.05). Compared with those of non-bloodstream infection subgroup in modeling group, the total burn area, full-thickness burn area, proportion of combination of inhalation injury, proportion of implementation of mechanical ventilation, days of mechanical ventilation, and days of ICU stay of patients in bloodstream infection subgroup were significantly increased (Z=-4.429, t=-4.045, χ²=7.845, 8.845, Z=-3.904, -4.134, P<0.01). Binary multivariate logistic regression analysis showed that total burn area, days of ICU stay, and combination of inhalation injury were the independent risk factors for bloodstream infection of patients in modeling group (odds ratio=1.031, 1.018, 2.871, 95% confidence interval=1.004-1.059, 1.006-1.030, 1.345-6.128, P<0.05 or P<0.01). In modeling group, the area under the ROC curve was 0.773 (95% confidence interval=0.708-0.838); the sensitivity was 64.6%, the specificity was 77.9%, and the best prediction probability was 0.335 when the Youden index was 0.425. The bloodstream infection incidence of patients predicted by the prediction model in validation group was 27.27% (24/88), with specificity of 82.09% (55/67) and sensitivity of 57.14% (12/21). The area under the ROC curve in validation group was 0.759 (95% confidence interval=0.637-0.882). Conclusions: The total burn area, days of ICU stay, and combination of inhalation injury are the risk factors of bloodstream infection in patients with extremely severe burns. The early prediction model for bloodstream infection risk in patients with extremely severe burns based on these factors has certain predictive value for burn centers with relatively stable treatment methods and bacterial epidemiology.

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--- WordNet output for number --- =>に番号をつける, 数, 番号, 総数, 若干, 多数, 曲, 過ごす, 達する, 番号を唱える Overview of noun number The noun number has 11 senses (first 9 from tagged texts) 1. (131) number, figure -- (the property possessed by a sum or total or indefinite quantity of units or individuals; "he had a number of chores to do"; "the number of parameters is small"; "the figure was about a thousand") 2. (63) number -- (a concept of quantity involving zero and units; "every number has a unique position in the sequence") 3. (5) act, routine, number, turn, bit -- (a short theatrical performance that is part of a longer program; "he did his act three times every evening"; "she had a catchy little routine"; "it was one of the best numbers he ever did") 4. (5) phone number, telephone number, number -- (the number is used in calling a particular telephone; "he has an unlisted number") 5. (2) numeral, number -- (a symbol used to represent a number; "he learned to write the numerals before he went to school") 6. (2) issue, number -- (one of a series published periodically; "she found an old issue of the magazine in her dentist's waiting room") 7. (1) number -- (a select company of people; "I hope to become one of their number before I die") 8. (1) number, identification number -- (a numeral or string of numerals that is used for identification; "she refused to give them her Social Security number") 9. (1) number -- (a clothing measurement; "a number 13 shoe") 10. number -- (the grammatical category for the forms of nouns and pronouns and verbs that are used depending on the number of entities involved (singular or dual or plural); "in English the subject and the verb must agree in number") 11. number -- (an item of merchandise offered for sale; "she preferred the black nylon number"; "this sweater is an all-wool number") Overview of verb number The verb number has 6 senses (first 3 from tagged texts) 1. (6) total, number, add up, come, amount -- (add up in number or quantity; "The bills amounted to $2,000"; "The bill came to $2,000") 2. (2) number -- (give numbers to; "You should number the pages of the thesis") 3. (1) number, list -- (enumerate; "We must number the names of the great mathematicians") 4. count, number -- (put into a group; "The academy counts several Nobel Prize winners among its members") 5. count, number, enumerate, numerate -- (determine the number or amount of; "Can you count the books on your shelf?"; "Count your change") 6. number, keep down -- (place a limit on the number of) --- WordNet end ---