Establishing and Validating a Risk Model for In-hospital Mortality Within 60 Days Under ICU Treatment for Tuberculosis in China

Establishing and Validating a Risk Model for In-hospital Mortality Within 60 Days Under ICU Treatment for Tuberculosis in China | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Establishing and Validating a Risk Model for In-hospital Mortality Within 60 Days Under ICU Treatment for Tuberculosis in China Keke Hou, Jianglin He, Tao Li, Xiu Li, Na Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5447218/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Tuberculosis (TB) is the leading cause of death from a single infectious disease. Current studies on TB patient mortality risk factors in intensive care are old and scarce. We aimed to create a model to predict in-hospital mortality risk for TB patients in ICU and identify mortality risk factors. Methods TB patients' data from 2016 to 2020 admitted to the ICU were collected retrospectively and randomly split into derivation and validation groups at a 7:3 ratio. The main outcome was 60-day in-hospital mortality. Analyses included Cox, nomogram, decision curve, and Kaplan‒Meier methods. Results A total of 848 patients were included (594 in the derivation group and 254 in the validation group). A total of 106 (17.85%) patients died in the derivation group. Multivariate Cox regression analysis revealed that sputum smear, severe pneumonia, c-TnI, mold, age, diastolic blood pressure (DBP), and tracheotomy were independent risk factors for 60-day in-hospital mortality in ICU patients with TB, and the prognostic index (PI) was defined as follows: PI = 0.0084 × Age − 0.0026 × DBP + 2.1988 × Severe pneumonia1 + 0.9094 × Tracheotomy1 + 1.2253 × Sputum smear1 + 0.826 × Mold1 + 0.5147 × c-TnI. Decision curve analysis (DCA) diagrams showed that the diagnostic probabilities of the derivation and validation groups were 0–70% and 0–58% respectively, with high model application accuracy and net benefit. Receiver operating characteristic (ROC) curve analysis revealed that the PI could predict death with good sensitivity (0.830) and specificity (0.867), and the cutoff value was 0.195 (the area under the curve (AUC) was 0.894, 95% CI : 0.865 to 0.924). K‒M analysis revealed that the proportion of deaths was increased when the PI was ≥ 0.195. Conclusion The nomogram-based prediction model of mortality within 60 days in TB patients in the ICU showed good discrimination and accuracy, and is of great clinical value for screening patients at high risk of death to support the development of intervention strategies for ICU patients with TB and to reduce mortality. Risk model In-hospital mortality ICU TB Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Tuberculosis (TB) is a highly contagious respiratory disease and a significant global public health problem requiring attention [ 1 , 2 ]. According to the World Health Organization's "Global Tuberculosis Report 2022", approximately 1.6 million people die of TB worldwide in 2021, and it is anticipated that the mortality rate will continue to rise in the future [ 3 ]. Although TB is always subacute or chronic, some patients, especially those with extensive comorbidities, may progress rapidly, with approximately 3% of patients requiring ICU treatment [ 4 ]. Studies have shown that the overall mortality rate of TB patients in the ICU is high, ranging from 25–50% [ 9 ], which is attributed to various causes [ 1 , 5 – 8 ]. The establishment of a prognostic risk model for TB patients under ICU treatment can effectively identify patients at high risk of death, therefore, it is essential for the management of TB and the reduction of overall TB mortality. Previous studies have constructed several prognostic prediction models for TB patients and have shown that age, hypertension, and sputum positivity are risk factors for adverse outcomes such as death [ 10 – 13 ]. However, few studies have focused on the risk factors for mortality in ICU-treated TB patients. Erbes et al. [ 6 ] identified the risk factors for death in patients with TB receiving ICU treatment from 1990 to 2001. Pneumonia, pancreatitis, and sepsis were found to be independent risk factors for death in German patients with TB receiving ICU treatment. Additionally, Sun J [ 14 ] et al. reported that the high mortality rate in TB patients treated in the ICU was associated with fungal infection, type II respiratory failure, liver damage, and elevated APACHE II scores. However, it's important to note that these studies were conducted relatively early on, involved small sample sizes, and did not include prognostic modeling, which may limit the applicability of their findings. Although the mortality rate of severe tuberculosis has remained high for a long time, medical technology have brought significant improvements. The widespread use of noninvasive ventilators and the expansion of their indications have improved ventilation while reducing the chance of respiratory co-infections. Furthermore, the use of fiberoptic bronchoscopic alveolar lavage has become a crucial treatment option for bronchial tuberculosis. It's also noteworthy that the timeframe of our study coincides with the initial year of the COVID-19 pandemic, raising questions about the potential shifts in previous prognostic models and risk factors for adverse outcomes.. In the post-COVID-19 era, a new predictive model incorporating different factors is urgently needed to meet this challenge.There is reason to believe that the survival rate of TB patients treated in ICUs has improved dramatically, while the misuse of antibiotics has led to a growing problem of drug resistance.Therefore, this study aimed to identify the risk factors for 60-day in-hospital mortality of TB patients in ICUs in China under the current level of medical care and to establish corresponding risk prediction models. Materials and Methods Search strategy and selection criteria A total of 883 TB patients who entered the ICU of Chengdu Public Health Clinical Medical Center for hospitalization between January 2016 and December 2020 were retrospectively enrolled. The study was approved by the Ethics Committee of our hospital (Ethics No. YJ-K2023-21-01). The inclusion criteria were as follows: patients with laboratory-confirmed or clinically diagnosed TB according to the 2017 diagnostic criteria for TB in the health sector of the People's Republic of China [ 15 ] and ICU treatment. The exclusion criteria were as follows: discharge within 24 hours or on voluntary terms, incomplete information; and hospitalization longer than 60 days. Finally, 848 TB patients were randomly divided into a derivation group and a validation group at a ratio of 7:3. A total of 795 people were determined to have active tuberculosis after pathogenetic examination, including sputum smear, sputum culture, etc., combined with clinical symptoms and chest imaging. A total of 134 patients enrolled in the study were resistant, 26 of whom were mono-resistant (isoniazid-, ethambutol-, and pyrazinamide-resistant), and the remaining 108 patients were rifampicin resistant and multidrug resistant. The indications for admission to the ICU were as follows: ① vital signs that were not stable, and needed respiratory support, and hemodynamics were monitored; ② the need for CRRT; ③ disorders of consciousness; and ④ severe TB, such as tuberculous meningitis or systemic TB; ⑤ severe TB combined with pregnancy; and ⑥ multiple organ dysfunction. The indications for tracheotomy include a variety of etiologies that require prolonged mechanical ventilation, upper airway obstruction, or poor drainage of lower airway secretions. Other criteria for determining severe pneumonia, kidney dysfunction, etc., are listed in the annex 1. Data extraction and quality assessment General information on the study population was collected by reviewing the following electronic medical records: sex, age, ethnicity, hospitalization days, complications, smoking history, sputum smear results, routine blood tests, blood biochemistry, type of treatment, discharge diagnosis, treatment outcome, etc. The laboratory data collected were the first evaluation of the patient upon admission to the ICU. Ultimately, a total of 42 variables were included in the analysis. The endpoint was in-hospital death, and the follow-up time was 60 days. Model construction and validation The random forest method was adopted for screening variables and we have set decision trees = 100 and nsplit = 10. The variables were screened by importance value, with larger values representing greater importance, and the variables with importance > 0.05 were selected as the derivation variables in the present study. A Cox model was developed using the data from the derivation group, and a nomogram was constructed. The model was evaluated in the derivation and validation groups for calibration, discrimination, and validity. Model calibration was evaluated using calibration curves, model discrimination was assessed using the area under the curve (AUC) of the receiver operating characteristic curves, and the C-index and model validity were evaluated using decision curve analysis (DCA). The cutoff value of the receiver operating characteristic (ROC) curve to differentiate between the low-risk and high-risk groups was selected in the derivation group using the Youden index, and the Kaplan‒Meier (K‒M) curve was plotted and validated in the validation group. Statistical analysis This study used R 4.0.3 (The R Foundation, https://www.r-project.org/ ) for data analysis. Quantitative variables are expressed as means and standard deviations (medians and quartiles), and qualitative variables are expressed as frequencies and percentages. Comparisons were performed using an independent t test (Mann‒Whitney U test) and chi-square test. Results Basic clinical characteristics The baseline data of 848 TB patients receiving ICU treatment are shown in Table 1 , which indicates no statistically significant difference in the general primary data between the derivation and validation groups. A total of 146 patients died (106 in the derivation group and 40 in the validation group), and respiratory failure due to severe pneumonia was the main cause of death (Fig. 1 ). There were 138 deaths (29.74%) among 464 patients requiring intensive care unit treatment for severe pneumonia, 68 deaths (18.68%) among 364 patients requiring noninvasive ventilation, and a high mortality rate of 42.11% among 152 tracheotomized patients. In addition, no significant difference was observed in the anti-TB regimen except for a greater prevalence of antibiotics in patients who died (presented in the appendix 2). Table 1 Comparison of baseline data between the derivation and validation groups Variables Derivation group (n = 594) Validation group (n = 254) Variables Derivation group(n = 594) Validation group(n = 254) Age, y 59.50 [35.25, 73.00] 55.00 [35.00, 73.00] Relapse, % 391 (65.82) 169 (66.54) Temperature, ° 36.80 [36.40, 37.30] 36.80 [36.50, 37.50] NIV, % 255 (42.93) 109 (42.91) SBP, mm/Hg 118.00 [102.00, 138.00] 116.50 [99.25, 136.00] Tracheotomy, % 101 (17.00) 51 (20.08) DBP, mm/Hg 72.00 [62.00, 84.00] 70.00 [60.00, 80.00] DVC, % 115 (19.36) 53 (20.87) Alb, g/L 30.70 [25.92, 36.00] 29.80 [26.55, 35.60] Anti-fungal_drugs, % 240 (40.40) 88 (34.65) Male, % 414 (69.70) 172 (67.72) Anticoagulants, % 236 (39.73) 92 (36.22) Han nationality, % 463 (77.95) 183 (72.05) BAC, % 120 (20.20) 50 (19.69) COPD, % 123 (20.71) 53 (20.87) Sputum_smear, % 112 (18.86) 42 (16.54) Syphilis, % 19 (3.20) 7 (2.76) TB_DNA, % 120 (20.20) 42 (16.54) HIV, % 18 (3.03) 4 (1.57) X_PERT, % 126 (21.21) 46 (18.11) Pneumothorax, % 58 (9.76) 20 (7.87) ATDR, % 98 (16.50) 36 (14.17) Hepatitis_B, % 86 (14.48) 28 (11.02) Fungus, % 203 (34.18) 85 (33.46) Liver_dysfunction, % 296 (49.83) 124 (48.82) Mold, % 12 (2.02) 6 (2.36) Cirrhosis, % 11 (1.85) 7 (2.76) WBC, × 10 9 /L 8.35 [5.40, 12.15] 8.67 [5.41, 11.85] Kidney_dysfunction, % 63 (10.61) 23 (9.06) NEU, × 10 9 /L 7.02 [4.39, 10.57] 6.97 [4.45, 10.38] Severe_pneumonia, % 320 (53.87) 144 (56.69) LYM,×10 9 /L 0.56 [0.33, 0.89] 0.54 [0.32, 0.95] Hypertension, % 120 (20.20) 56 (22.05) CD4, cell/ul 221.50 [116.00, 367.00] 230.00 [101.25, 372.00] Diabetes, % 96 (16.16) 48 (18.90) CD8, cell/ul 166.50 [75.25, 284.00] 161.00 [71.00, 289.00] Cardiac_dysfunction, % 170 (28.62) 78 (30.71) CRP, mg/L 60.72 [24.02, 130.40] 57.00 [25.60, 134.70] Malignancy, % 33 (5.56) 11 (4.33) BNP, pg/mL 732.35 [216.02, 2189.00] 690.35 [211.38, 2126.00] Smoking, % 271 (45.62) 113 (44.49) C-TnI, ng/L 0.00 [0.00, 0.03] 0.00 [0.00, 0.02] Data are summarized as the mean ± SD if normally distributed, the median (first and third quartiles) if nonnormally distributed, and n (%) for categorical variables. Please refer to the abbreviation table at the end of the text. SBP , systolic blood pressure; ALb , albumin; COPD , chronic obstructive pulmonary disease; HIV , human immunodeficiency virus; BAC , bacterological examination for sputum; NIV , noninvasive ventilator; DVC , deep venous catheterization; ATDR , anti-TB drug resistance; WBC , white blood cell; NEU , neutrophilic granulocyte; LYM , lymphocyte; CRP , C-reactive protein; BNP , B-type brain natriuretic peptide. In the derivation group, comparisons between the nonsurviving group and the surviving group showed that 30 variables, including age, systolic blood pressure (SBP), diastolic blood pressure (DBP), and the serum ALB concentration, were significantly different (P < 0.05, Table 2 ). In the validation group, 23 variables were significantly different ( P < 0.05) in the intergroup comparison between the nonsurviving group and the surviving group (in Table 2 ). Table 2 Comparison of baseline data between the nonsurviving and surviving groups Variables Derivation group (n = 594) Validation group (n = 254) Alive (n = 488) Died (n = 106) P Alive (n = 214) Died (n = 40) P Age, y 55.00 [33.00, 71.00] 70.00 [50.00, 80.75] < 0.001❊❊ 54.00 [33.25, 70.75] 73.00 [44.50, 83.00] 0.001 ❊ Temperature, ° 36.80 [36.50, 37.30] 36.70 [36.40, 37.30] 0.156 36.90 [36.50, 37.48] 36.65 [36.48, 37.65] 0.329 SBP, mm/Hg 119.00 [104.00, 139.00] 112.00 [95.00, 132.00] 0.02❊ 118.00 [99.00, 136.00] 116.00 [103.75, 133.00] 0.825 DBP, mm/Hg 73.00 [63.00, 84.00] 69.50 [58.25, 82.00] 0.015❊ 70.00 [60.25, 80.00] 70.00 [60.00, 80.75] 0.725 Alb, g/L 31.45 [26.58, 36.80] 26.60 [22.47, 30.60] < 0.001❊❊ 30.25 [27.33, 36.30] 25.80 [22.17, 30.82] < 0.001❊❊ Male, % 327 (67.01) 87 (82.08) 0.003❊ 139 (64.95) 33 (82.50) 0.046 ❊ Han nationality, % 361 (73.98) 102 (96.23) < 0.001❊❊ 147 (68.69) 36 (90.00) 0.010 ❊ COPD, % 89 (18.24) 34 (32.08) 0.002❊ 35 (16.36) 18 (45.00) < 0.001❊❊ Syphilis, % 9 (1.84) 10 (9.43) < 0.001❊❊ 3 (1.40) 4 (10.00) 0.012 ❊ HIV, % 14 (2.87) 4 (3.77) 0.857 2 (0.93) 2 (5.00) 0.229 Pneumothorax, % 53 (10.86) 5 (4.72) 0.080 19 (8.88) 1 (2.50) 0.291 Hepatitis_B, % 75 (15.37) 11 (10.38) 0.241 27 (12.62) 1 (2.50) 0.110 Liver_dysfunction, % 252 (51.64) 44 (41.51) 0.075 104 (48.60) 20 (50.00) 1.000 Cirrhosis, % 9 (1.84) 2 (1.89) 1.000 7 (3.27) 0 (0.00) 0.526 Kidney_dysfunction, % 46 (9.43) 17 (16.04) 0.067 20 (9.35) 3 (7.50) 0.942 Severe_pneumonia, % 220 (45.08) 100 (94.34) < 0.001❊❊ 106 (49.53) 38 (95.00) < 0.001❊❊ Hypertension, % 95 (19.47) 25 (23.58) 0.410 47 (21.96) 9 (22.50) 1.000 Diabetes, % 62 (12.70) 34 (32.08) < 0.001❊❊ 40 (18.69) 8 (20.00) 1.000 Cardiac_dysfunction, % 133 (27.25) 37 (34.91) 0.144 65 (30.37) 13 (32.50) 0.936 Malignancy, % 27 (5.53) 6 (5.66) 1.000 11 (5.14) 0 (0.00) 0.297 Smoking, % 203 (41.60) 68 (64.15) < 0.001❊❊ 85 (39.72) 28 (70.00) 0.001 ❊❊ Relapse, % 307 (62.91) 84 (79.25) 0.002❊ 139 (64.95) 30 (75.00) 0.292 NIV, % 209 (42.83) 46 (43.40) 1.000 87 (40.65) 22 (55.00) 0.131 Tracheotomy, % 55 (11.27) 46 (43.40) < 0.001❊❊ 33 (15.42) 18 (45.00) < 0.001❊❊ DVC, % 69 (14.14) 46 (43.40) < 0.001❊❊ 39 (18.22) 14 (35.00) 0.029 ❊ Antifungal_drugs, % 171 (35.04) 69 (65.09) < 0.001❊❊ 67 (31.31) 21 (52.50) 0.016 ❊ Anticoagulants, % 164 (33.61) 72 (67.92) < 0.001❊❊ 66 (30.84) 26 (65.00) < 0.001❊❊ BAC, % 75 (15.37) 45 (42.45) < 0.001❊❊ 41 (19.16) 9 (22.50) 0.786 Sputum_smear, % 53 (10.86) 59 (55.66) < 0.001❊❊ 25 (11.68) 17 (42.50) < 0.001❊❊ TB_DNA, % 84 (17.21) 36 (33.96) < 0.001❊❊ 34 (15.89) 8 (20.00) 0.681 X_PERT, % 80 (16.39) 46 (43.40) < 0.001❊❊ 36 (16.82) 10 (25.00) 0.313 ATDR, % 57 (11.68) 41 (38.68) < 0.001❊❊ 25 (11.68) 11 (27.50) 0.017 ❊ Fungus, % 129 (26.43) 74 (69.81) < 0.001❊❊ 61 (28.50) 24 (60.00) < 0.001❊❊ Mold, % 4 (0.82) 8 (7.55) < 0.001❊❊ 4 (1.87) 2 (5.00) 0.529 WBC,× 10 9 /L 8.28 [5.28, 12.15] 9.04 [5.82, 13.37] 0.116 8.26 [5.41, 11.59] 10.35 [6.32, 13.77] 0.038 ❊ NEU,× 10 9 /L 6.77 [4.16, 10.18] 8.12 [4.91, 11.22] 0.021❊ 6.78 [4.27, 9.89] 9.19 [4.82, 11.61] 0.026 ❊ LYM,× 10 9 /L 0.60 [0.36, 0.93] 0.40 [0.27, 0.69] < 0.001❊❊ 0.60 [0.33, 0.97] 0.38 [0.26, 0.59] 0.020 ❊ CD4, cell/ul 236.50 [121.75, 397.25] 150.00 [67.25, 265.00] < 0.001❊❊ 245.00 [126.25, 412.00] 126.00 [66.50, 230.00] < 0.001❊❊ CD8, cell/ul 186.00 [86.25, 312.00] 101.50 [49.00, 207.00] < 0.001❊❊ 173.50 [84.00, 302.00] 80.50 [53.75, 181.50] < 0.001❊❊ CRP, mg/L 52.30 [21.92, 118.62] 95.45 [55.40, 184.60] < 0.001❊❊ 53.50 [19.30, 122.80] 142.50 [73.62, 184.75] < 0.001❊❊ BNP, pg/mL 518.85 [188.50, 1773.00] 1908.00 [1033.00, 4324.00] < 0.001❊❊ 532.15 [179.75, 1540.00] 2627.00 [1245.00, 7155.25] < 0.001❊❊ c-TnI, ng/L 0.00 [0.00, 0.01] 0.02 [0.00, 0.12] < 0.001❊❊ 0.00 [0.00, 0.00] 0.03 [0.00, 0.32] < 0.001❊❊ Same as Table 1 . Data are summarized as the mean ± SD if normally distributed, the median (first and third quartiles) if nonnormally distributed, and n (%) for categorical variables. ❊: P < 0.05; ❊❊: P < 0.001. Risk model establishment The random forest results showed that only 7 variables (sputum smear, severe pneumonia, c-TnI, mold, age, DBP, and tracheotomy) had importance values greater than 0.05 (Fig. 2 -A). The nomogram of the 7 factors is shown in Fig. 2 -B. Predicting the risk of death in patients with TB under ICU treatment. (The value of each variable is given a score on the point scale axis. A total score can be easily calculated by adding all scores together, and by projecting the complete score to the lower total point scale, we can estimate the probability of death.) The seven variables screened by the random forest method were included in Multivariate Cox analysis. Severe pneumonia, tracheotomy, positive sputum smear, and mold infection were found to be prognostic indices for death within 60 days in TB patients under ICU treatment (PI = 0.0084 × Age − 0.0026 × DBP + 2.1988 × Severe pneumonia1 + 0.9094 × Tracheotomy1 + 1.2253 × Sputum smear1 + 0.826 × Mold1 + 0.5147 × c-TnI), as presented in Table 3 . Table 3 Multivariate Cox regression analysis of risk factors for death in patients with pulmonary TB receiving ICU treatment. Variables Coef SE Z HR HR 95% CI P Age 0.008 0.006 1.502 1.008 0.998ཞ1.020 0.133 DBP − 0.003 0.006 − 0.427 0.997 0.986ཞ1.009 0.670 Severe pneumonia1 2.199 0.432 5.088 9.014 3.864ཞ21.028 < 0.0001 Tracheotomy1 0.909 0.208 4.380 2.483 1.653ཞ3.730 < 0.0001 Sputum smear1 1.225 0.218 5.631 3.405 2.223ཞ5.216 < 0.0001 Mold1 0.826 0.379 2.181 2.284 1.087ཞ4.798 0.029 C-TnI 0.515 0.404 4.000 1.673 0.758ཞ3.694 0.203 1: Represents consolidation. We plotted a scatter plot based on the actual observed values (vertical coordinates) and derivative predicted values (horizontal coordinates), with the diagonal line as the reference line (IDEAL), and fitted a trend line to obtain the calibration curve. In both the derivation and validation groups, the calibration curves plotted by the model were close to those of IDEAL (Fig. 3 ), suggesting that the model was well calibrated. Moreover, the decision curve showed that this model had better application accuracy and greater benefit (Fig. 4 ). The C index was 0.858 (95% CI : 0.832–0.885) in the derivation group and 0.824 (95% CI : 0.774–0.874) in the validation group, suggesting that the model has an excellent discriminatory ability. The ROC curve analysis showed the PI could predict death with good sensitivity (0.830) and specificity (0.867), and the cutoff value was 0.195 (the AUC was 0.894, 95% CI: 0.865 to 0.924). The results of the ROC curve analysis for the validation cohort with a threshold = 0.195 are shown in Fig. 5 , the results suggest that specificity = 0.78 and sensitivity = 0.75. When the prognostic index was greater than 0.195, patients had an increased risk of death (presented in K‒M survival analysis in Fig. 6 ). Discussion This study established a prognostic risk model for TB patients treated in the ICU. We found that severe combined pneumonia, mold infection, a positive sputum smear, mold infection, and tracheotomy treatment were risk factors for 60-day in-hospital mortality in TB patients receiving ICU treatment. We further calculated the prognostic index based on the nomogram and found that the proportion of deaths increased when the prognostic index was greater than 0.195. These findings can help clinicians identify patients with high risk of death and develop rational treatment strategies to reduce mortality. TB remains a severe threat to human lives in developing countries and among the low-income populations [ 13 , 16 ], which is a significant burden on society [ 17 , 18 ]. The United Nations Sustainable Development Goal (SDG) of ending the TB epidemic by 2030 has not yet been achieved [ 3 ]. TB patients treated in ICUs account for a large proportion (86.67%) of the total number of TB deaths due to the complications and complexity of the disease [ 6 ], which is an important reason for the poor prognosis of TB patients. In this study, the in-hospital mortality rate of TB patients under ICU treatment within 60 days was as high as 17.22% (146/848), which was much higher than that of ordinary TB patients under short-term treatment (4.55%) [ 12 ]. Therefore, early prevention and treatment after risk prediction can reduce overall TB patient mortality and improve prognosis. In the present study, the mortality rate of TB patients under ICU treatment was significantly lower than reported by Muthu V et al. [ 19 ](17.22% vs. 44.4%), suggesting that the mortality rate of severe TB patients has decreased due to the significant improvement in TB treatment. Therefore, previous models and related factors are no longer applicable to the present situation. For developing countries whose health care systems are relatively weak, it is urgent to establish a risk prediction model suitable for the current ICU treatment paradigm. Our model provided important corresponding information. R. Erbes et al. suggested that acute renal failure, mechanical ventilation, chronic pancreatitis, sepsis, acute respiratory distress syndrome (ARDS), and hospital-acquired pneumonia were independent risk factors for death in TB patients receiving intensive care in the ICU. Patients with renal dysfunction were also included in our study. Nevertheless, renal dysfunction was not significantly different between the nonsurviving group and the surviving group in this study. Sepsis and chronic pancreatitis did not occur at all, which might be due to the substantial improvement in health care. Tracheotomy and combined severe pneumonia were common independent risk factors in both models. Patients with severe TB are prone to severe pneumonia, and tracheotomy and mechanical ventilation are needed [ 8 , 12 – 14 , 16 – 23 ]. The risk of recurrent respiratory infections and death increases at the same time [ 24 ]. Ibn Saied W et al. also reported that severe acquired pneumonia in hospitals increases the risk of 30-day mortality by 82%, while ventilator-associated pneumonia can increase 30-day mortality by 38% [ 8 ]. A positive Mycobacterium tuberculosis sputum smear is the gold standard for the diagnosis of TB, which also indicates that TB is highly contagious and the immunity of the affected patients is relatively low[ 25 ]. It was a significant independent risk factor in our study, and the risk index of death in the nomogram model increased by 1.2253 with this factor. A previous study demonstrated that a positive Mycobacterium tuberculosis sputum smear affects the outcome of treatment and that the mortality rate triples[ 26 , 13 ]. Combined mold infection is also common in TB patients due to diminished immunity. Models by Sun J and other scholars have shown that the high mortality rate of TB patients treated in the ICU is closely related to mold infections [ 14 , 27 – 29 ]. Chronic TB lesions damage the surface integrity of lung tissue and result in poor barrier function, which provides the conditions for mold infections. Anti-mold therapy significantly reduces the efficacy of anti-TB drugs, leading to delayed treatment and even death[ 30 ]. Therefore, strengthening the monitoring of Mycobacterium tuberculosis sputum smears and timely prevention and curing of mold infection can reduce the mortality rate of severe TB patients treated in the ICU. Several previous studies have established different models for mortality in common TB patients, TB patients with different comorbidities or TB patients in particular regions[ 12 , 31 – 33 ]. The present model targeted patients treated in the ICU and provided strong differentiation (AUC) (0.894: 0.820). In addition, the present model takes more comprehensive factors into account, including basic demographic information, clinical data, laboratory tests, and treatment modalities, which makes it more advantageous for clinical application, especially in critically severe TB patients with more severe conditions. Instead of traditional survival analysis methods, this study used a nomogram to visualize logistic regression analysis results through graphical symbols[ 12 , 34 – 36 ], which helps medical staff identify patients at high risk of adverse treatment outcomes more intuitively at an early stage. The model in this study has a good fitting effect and high predictive accuracy, and the variables included in the established model are easily available through clinical tests. The model provides a high degree of differentiation (AUC = 0.894) and a high net clinical benefit (0%-70%), which can help medical staff monitor the risk at the time of ICU admission to identify high-risk patients at an early stage and increase the success rate of TB treatment. There are several limitations in this study. First, this study was a retrospective analysis with selection bias. Second, this was a single-center study, and the model was not externally validated; thus, the potential for extrapolation of the results is limited. Third, chest imaging data were scarce because the criticality of the disease-related data was not included in this study. Future multicenter, large-sample, prospective studies should be conducted to obtain more findings. Conclusion Severe pneumonia, tracheotomy, a positive sputum smear, and mold infection were found to be independent risk factors for death in our prediction model based on TB patients treated in the ICU. The model showed good discriminatory power and accuracy, implying that it has high clinical value for screening TB patients with an increased risk of death. Abbreviations ICU Intensive care unit TB Tuberculosis DBP Diastolic blood pressure PI Prognostic index DCA Decision curve analysis ROC Receiver operating characteristic curve AUC The area under the curve CRRT Continuous renal replacement therapy K‒M Kaplan‒Meier SBP Systolic blood pressure Alb Albumin COPD Chronic obstructive pulmonary disease HIV Human immunodeficiency virus BAC Bacterological examination for sputum NIV Noninvasive ventilator DVC Deep venous catheterization ATDR Anti-TB drug resistance WBC White blood cell NEU Neutrophilic granulocyte LYM Lymphocyte CRP C-reactive protein BNP B-type brain natriuretic peptide SDG Sustainable Development Goal ARDS Acute respiratory distress syndrome Declarations Supplementary Information Supplementary material is provided in the annex. Acknowledgments We thank to Dr. Hang Fu (Key Laboratory of Obstetric & Gynecologic and Pediatric Diseases and Birth Defects of Ministry of Education, Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu 610017, China) for valuable suggestions on the revision of the discussion. Author contributions KKH, XL and NZ conceptualised the study, KKH and XL wrote the proposal for the acquisition of ethical clearance, KKH and XL provided resources, JLH and TL carried out the investigation, KKH supervised the study, JLH and TL curated the data. KKH and JLH analysed the data, KKH, JLH, TL, XL and NZ wrote the original draft of the manuscript, XL and NZ critically reviewed and edited the manuscript. All authors read and approved the final version of the manuscript. Funding This work was supported by Chengdu Science and Technology Bureau Technology Innovation R&D Program (2024-YF05-01215-SN) and the Chengdu Health Commission Medical Scientific Research Project (2023415). Availability of data and materials The data used for the study are available from the corresponding author upon reasonable request. Ethics approval and consent to participate Ethical clearance with reference number YJ-K2023-21-01 was obtained from the Research Ethics Committee of the Public Health Clinical Center of Chengdu, China. The study was performed according to the guidelines laid out by the Research Ethics Committee. Confidentiality of the participants' information and data resulted was assured. Informed consent was waived by the Research Ethics Committee of the Public Health Clinical Center of Chengdu, China. Clinical trial number Not applicable. Consent for publication Not applicable. Competing interests All the authors declare that they have no competing interests. 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Potential Limitations of a Very Short Course of Antimicrobial Therapy for Ventilator-Associated Pneumonia. Clin Infect Dis. 2017 Jun 15;64(12):1802-1803. https://doi.org/10.1093/cid/cix291 Iwashita Y, Yamashita K, Ikai H, Sanui M, Imai H, Imanaka Y. Epidemiology of mechanically ventilated patients treated in ICU and non-ICU settings in Japan: a retrospective database study. Crit Care. 2018 Dec 4;22(1):329. https://doi.org/10.1186/s13054-018-2250-3 Wilson ME, Barwise A, Heise KJ, Loftsgard TO, Dziadzko M, Cheville A, Majzoub A, Novotny PJ, Gajic O, Biehl M. Long-Term Return to Functional Baseline After Mechanical Ventilation in the ICU. Crit Care Med. 2018 Apr;46(4):562-569. https://doi.org/10.1097/ccm.0000000000002927 Ding A, Sanchez M, Chen Y, et al. Prediction of Physiological Deterioration and Mortality in Mechanically Ventilated Patients Admitted to the Icu[J]. Chest, 2021, 160(4): A1061-A1062.https://doi.org/10.1016/j.chest.2021.07.983 Migliori GB, Sotgiu G, Lange C, Centis R. Extensively drug-resistant tuberculosis: back to the future. Eur Respir J. 2010 Sep;36(3):475-7. https://doi.org/10.1183/09031936.00025910 Singanayagam A, Manalan K, Connell DW, Chalmers JD, Sridhar S, Ritchie AI, Lalvani A, Wickremasinghe M, Kon OM. Evaluation of serum inflammatory biomarkers as predictors of treatment outcome in pulmonary tuberculosis. Int J Tuberc Lung Dis. 2016 Dec;20(12):1653-1660. https://doi.org/10.5588/ijtld.16.0159 Ekeng BE, Davies AA, Osaigbovo II, Warris A, Oladele RO, Denning DW. Pulmonary and Extrapulmonary Manifestations of Fungal Infections Misdiagnosed as Tuberculosis: The Need for Prompt Diagnosis and Management. J Fungi (Basel). 2022 Apr 28;8(5):460. https://doi.org/10.3390/jof8050460 Kunst H, Wickremasinghe M, Wells A, Wilson R. Nontuberculous mycobacterial disease and Aspergillus-related lung disease in bronchiectasis. Eur Respir J. 2006 Aug;28(2):352-7. https://doi.org/10.1183/09031936.06.00139005 Kim SH, Kim MY, Hong SI, Jung J, Lee HJ, Yun SC, Lee SO, Choi SH, Kim YS, Woo JH. Invasive Pulmonary Aspergillosis-mimicking Tuberculosis. Clin Infect Dis. 2015 Jul 1;61(1):9-17. https://doi.org/10.1093/cid/civ216 Vogel M, Hartmann T, Köberle M, Treiber M, Autenrieth IB, Schumacher UK. Rifampicin induces MDR1 expression in Candida albicans . J Antimicrob Chemother. 2008 Mar;61(3):541-7. https://doi.org/10.1093/jac/dkm513 Dos Santos Feltrin AF, Vendramini SH, Neto FC, de Vechi Correa AP, Werneck AL, Dos Santos Sasaki NS, de Lourdes Sperli Geraldes Santos M. Death in patients with tuberculosis and diabetes: Associated factors. Diabetes Res Clin Pract. 2016 Oct;120:111-6. https://doi.org/10.1016/j.diabres.2005.10.003 Nordholm AC, Andersen AB, Wejse C, Norman A, Ekstrøm CT, Andersen PH, Lillebaek T, Koch A. Mortality, risk factors, and causes of death among people with tuberculosis in Denmark, 1990-2018. Int J Infect Dis. 2023 May;130:76-82. https://doi.org/10.1016/j.ijid.2023.02.024 Nguyen DT, Graviss EA. Development and validation of a prognostic score to predict tuberculosis mortality. J Infect. 2018 Oct;77(4):283-290. https://doi.org/10.1016/j.jinf.2018.02.009 Yu Q, Yan J, Tian S, Weng W, Luo H, Wei G, Long G, Ma J, Gong F, Wang X. A scoring system developed from a nomogram to differentiate active pulmonary tuberculosis from inactive pulmonary tuberculosis. Front Cell Infect Microbiol. 2022 Sep 2;12:947954. https://doi.org/10.3389/fcimb.2022.947954 Wang R, Dai W, Gong J, Huang M, Hu T, Li H, Lin K, Tan C, Hu H, Tong T, Cai G. Development of a novel combined nomogram model integrating deep learning-pathomics, radiomics and immunoscore to predict postoperative outcome of colorectal cancer lung metastasis patients. J Hematol Oncol. 2022 Jan 24;15(1):11. https://doi.org/10.1186/s13045-022-01225-3 Cheng B, Wang C, Zou B, Huang D, Yu J, Cheng Y, Meng X. A nomogram to predict outcomes of lung cancer patients after pneumonectomy based on 47 indicators. Cancer Med. 2020 Feb; 9(4): 1430-1440. https://doi.org/10.1002/cam4.2805 Additional Declarations No competing interests reported. Supplementary Files Annex1.docx Annex2.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5447218","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":387901940,"identity":"59bd5745-e4cc-41c5-947c-7f76d3dfcfce","order_by":0,"name":"Keke Hou","email":"","orcid":"","institution":"Public Health Clinical Center of Chengdu","correspondingAuthor":false,"prefix":"","firstName":"Keke","middleName":"","lastName":"Hou","suffix":""},{"id":387901942,"identity":"bd738c02-c677-47c8-bf7c-b65056f0c7b0","order_by":1,"name":"Jianglin He","email":"","orcid":"","institution":"Public Health Clinical Center of Chengdu","correspondingAuthor":false,"prefix":"","firstName":"Jianglin","middleName":"","lastName":"He","suffix":""},{"id":387901947,"identity":"9d46eb23-e68a-4f75-af88-6b092d63e389","order_by":2,"name":"Tao Li","email":"","orcid":"","institution":"Public Health Clinical Center of Chengdu","correspondingAuthor":false,"prefix":"","firstName":"Tao","middleName":"","lastName":"Li","suffix":""},{"id":387901948,"identity":"f361003f-d44e-40f9-a97b-8ca0ae29a988","order_by":3,"name":"Xiu Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIie3PsWrDMBCAYZmD63LUq0yD+woHBq95FQmDpwYyejBUpSUe2tA1j5Gxo7xoUvdscckL2FuHDKFzS+VuHfTN93N3QkTRP4RXr9OguM2Xx1M/qKYNJ9dkCx7XrhC2rnjwLpzkUpXZbgRt7F2ZfTzBjMPIVgUxJg/Glo02KNLuWQV+Mf2JeAGQmPqg3xZC+vd9YEv/+LUFEYQ7aI+C5SqQyApviIEIk81ab2BOUmO2Y5CSAMW8hDzwyI5ZIkjlHQV/ue1ekkGd2/u9TKfps2nztNv+nnxDfxuPoiiKfnQB2u1GOyerDg4AAAAASUVORK5CYII=","orcid":"","institution":"Public Health Clinical Center of Chengdu","correspondingAuthor":true,"prefix":"","firstName":"Xiu","middleName":"","lastName":"Li","suffix":""},{"id":387901949,"identity":"3b3a51a1-418e-49d5-a8d1-4feb31f50acc","order_by":4,"name":"Na Zhang","email":"","orcid":"","institution":"Public Health Clinical Center of Chengdu","correspondingAuthor":false,"prefix":"","firstName":"Na","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2024-11-13 13:08:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5447218/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5447218/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":71801895,"identity":"f474ae67-f167-4d11-9cb6-8b5c7e440a61","added_by":"auto","created_at":"2024-12-18 16:55:48","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":293952,"visible":true,"origin":"","legend":"\u003cp\u003eCauses of death\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5447218/v1/78d2699b6fd3cc077d75279b.png"},{"id":71802794,"identity":"bb161719-9688-4539-b497-fce9260df387","added_by":"auto","created_at":"2024-12-18 17:03:48","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":204052,"visible":true,"origin":"","legend":"\u003cp\u003eA: A random forest plot of the derivation variables; B: The nomogram\u003c/p\u003e\n\u003cp\u003ePredicting the risk of death in patients with TB under ICU treatment. (The value of each variable is given a score on the point scale axis. A total score can be easily calculated by adding all scores together, and by projecting the complete score to the lower total point scale, we can estimate the probability of death.)\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5447218/v1/c1488f2662dbafd456419acb.png"},{"id":71802791,"identity":"8e4f0a73-5d28-4db1-9632-577c35be6938","added_by":"auto","created_at":"2024-12-18 17:03:48","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":131392,"visible":true,"origin":"","legend":"\u003cp\u003eThe nomogram calibration curves for mortality risk (A: derivation group; B: validation group). The x-axis represents the nomogram-predicted probability, and the y-axis represents the actual probability. A perfect prediction corresponds to the 45° black dashed line. The blue × sign represents the entire cohort (A=594, B=254), and the black solid line indicates performing bias correction.)\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5447218/v1/4a5ded6850568b099a910975.png"},{"id":71801899,"identity":"b5b79757-cfde-4bb7-9723-e6493d834220","added_by":"auto","created_at":"2024-12-18 16:55:48","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":118873,"visible":true,"origin":"","legend":"\u003cp\u003eDCA curves for the derivation group and validation group (A: the derivation group, n=594; B: the validation group, n = 254)\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-5447218/v1/ff97592107086bb34084f22f.png"},{"id":71801901,"identity":"393b9e52-985f-4304-9533-5a00c85e20ad","added_by":"auto","created_at":"2024-12-18 16:55:48","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":125484,"visible":true,"origin":"","legend":"\u003cp\u003eResults of the ROC curve analysis for the validation cohort\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-5447218/v1/7ab946583910a2d54b260b75.png"},{"id":71803913,"identity":"fdb06621-52a4-4f25-947f-f98c9739c09d","added_by":"auto","created_at":"2024-12-18 17:11:48","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":123931,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan–Meier analysis of in-ICU survival (A: derivation groups, n = 594; B: the validation groups, n = 254)\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-5447218/v1/92b00a168b871900d53a2a78.png"},{"id":84172121,"identity":"18cf1086-e79a-4777-b56e-cc59d900f4d9","added_by":"auto","created_at":"2025-06-08 23:31:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2140143,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5447218/v1/fcbaeaf7-591f-4327-b976-e947f973d892.pdf"},{"id":71801896,"identity":"018114ca-26fc-4cdd-a007-2d7c2f1e5e81","added_by":"auto","created_at":"2024-12-18 16:55:48","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":13448,"visible":true,"origin":"","legend":"","description":"","filename":"Annex1.docx","url":"https://assets-eu.researchsquare.com/files/rs-5447218/v1/55d5d7277b9ad14bfd45c15d.docx"},{"id":71802792,"identity":"1bc709a5-784e-4dbf-9056-ab8e6b61c2a0","added_by":"auto","created_at":"2024-12-18 17:03:48","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":18196,"visible":true,"origin":"","legend":"","description":"","filename":"Annex2.docx","url":"https://assets-eu.researchsquare.com/files/rs-5447218/v1/77d7bb939955226dd4a7d8c2.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Establishing and Validating a Risk Model for In-hospital Mortality Within 60 Days Under ICU Treatment for Tuberculosis in China","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTuberculosis (TB) is a highly contagious respiratory disease and a significant global public health problem requiring attention [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. According to the World Health Organization's \"Global Tuberculosis Report 2022\", approximately 1.6\u0026nbsp;million people die of TB worldwide in 2021, and it is anticipated that the mortality rate will continue to rise in the future [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Although TB is always subacute or chronic, some patients, especially those with extensive comorbidities, may progress rapidly, with approximately 3% of patients requiring ICU treatment [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Studies have shown that the overall mortality rate of TB patients in the ICU is high, ranging from 25\u0026ndash;50% [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], which is attributed to various causes [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The establishment of a prognostic risk model for TB patients under ICU treatment can effectively identify patients at high risk of death, therefore, it is essential for the management of TB and the reduction of overall TB mortality.\u003c/p\u003e \u003cp\u003ePrevious studies have constructed several prognostic prediction models for TB patients and have shown that age, hypertension, and sputum positivity are risk factors for adverse outcomes such as death [\u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, few studies have focused on the risk factors for mortality in ICU-treated TB patients. Erbes et al. [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] identified the risk factors for death in patients with TB receiving ICU treatment from 1990 to 2001. Pneumonia, pancreatitis, and sepsis were found to be independent risk factors for death in German patients with TB receiving ICU treatment. Additionally, Sun J [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] et al. reported that the high mortality rate in TB patients treated in the ICU was associated with fungal infection, type II respiratory failure, liver damage, and elevated APACHE II scores. However, it's important to note that these studies were conducted relatively early on, involved small sample sizes, and did not include prognostic modeling, which may limit the applicability of their findings.\u003c/p\u003e \u003cp\u003eAlthough the mortality rate of severe tuberculosis has remained high for a long time, medical technology have brought significant improvements. The widespread use of noninvasive ventilators and the expansion of their indications have improved ventilation while reducing the chance of respiratory co-infections. Furthermore, the use of fiberoptic bronchoscopic alveolar lavage has become a crucial treatment option for bronchial tuberculosis. It's also noteworthy that the timeframe of our study coincides with the initial year of the COVID-19 pandemic, raising questions about the potential shifts in previous prognostic models and risk factors for adverse outcomes.. In the post-COVID-19 era, a new predictive model incorporating different factors is urgently needed to meet this challenge.There is reason to believe that the survival rate of TB patients treated in ICUs has improved dramatically, while the misuse of antibiotics has led to a growing problem of drug resistance.Therefore, this study aimed to identify the risk factors for 60-day in-hospital mortality of TB patients in ICUs in China under the current level of medical care and to establish corresponding risk prediction models.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSearch strategy and selection criteria\u003c/h2\u003e \u003cp\u003eA total of 883 TB patients who entered the ICU of Chengdu Public Health Clinical Medical Center for hospitalization between January 2016 and December 2020 were retrospectively enrolled. The study was approved by the Ethics Committee of our hospital (Ethics No. YJ-K2023-21-01). The inclusion criteria were as follows: patients with laboratory-confirmed or clinically diagnosed TB according to the 2017 diagnostic criteria for TB in the health sector of the People's Republic of China [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] and ICU treatment. The exclusion criteria were as follows: discharge within 24 hours or on voluntary terms, incomplete information; and hospitalization longer than 60 days. Finally, 848 TB patients were randomly divided into a derivation group and a validation group at a ratio of 7:3.\u003c/p\u003e \u003cp\u003eA total of 795 people were determined to have active tuberculosis after pathogenetic examination, including sputum smear, sputum culture, etc., combined with clinical symptoms and chest imaging. A total of 134 patients enrolled in the study were resistant, 26 of whom were mono-resistant (isoniazid-, ethambutol-, and pyrazinamide-resistant), and the remaining 108 patients were rifampicin resistant and multidrug resistant.\u003c/p\u003e \u003cp\u003eThe indications for admission to the ICU were as follows: ① vital signs that were not stable, and needed respiratory support, and hemodynamics were monitored; ② the need for CRRT; ③ disorders of consciousness; and ④ severe TB, such as tuberculous meningitis or systemic TB; ⑤ severe TB combined with pregnancy; and ⑥ multiple organ dysfunction. The indications for tracheotomy include a variety of etiologies that require prolonged mechanical ventilation, upper airway obstruction, or poor drainage of lower airway secretions. Other criteria for determining severe pneumonia, kidney dysfunction, etc., are listed in the annex 1.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData extraction and quality assessment\u003c/h3\u003e\n\u003cp\u003eGeneral information on the study population was collected by reviewing the following electronic medical records: sex, age, ethnicity, hospitalization days, complications, smoking history, sputum smear results, routine blood tests, blood biochemistry, type of treatment, discharge diagnosis, treatment outcome, etc. The laboratory data collected were the first evaluation of the patient upon admission to the ICU. Ultimately, a total of 42 variables were included in the analysis. The endpoint was in-hospital death, and the follow-up time was 60 days.\u003c/p\u003e\n\u003ch3\u003eModel construction and validation\u003c/h3\u003e\n\u003cp\u003eThe random forest method was adopted for screening variables and we have set decision trees\u0026thinsp;=\u0026thinsp;100 and nsplit\u0026thinsp;=\u0026thinsp;10. The variables were screened by importance value, with larger values representing greater importance, and the variables with importance\u0026thinsp;\u0026gt;\u0026thinsp;0.05 were selected as the derivation variables in the present study. A Cox model was developed using the data from the derivation group, and a nomogram was constructed. The model was evaluated in the derivation and validation groups for calibration, discrimination, and validity. Model calibration was evaluated using calibration curves, model discrimination was assessed using the area under the curve (AUC) of the receiver operating characteristic curves, and the C-index and model validity were evaluated using decision curve analysis (DCA). The cutoff value of the receiver operating characteristic (ROC) curve to differentiate between the low-risk and high-risk groups was selected in the derivation group using the Youden index, and the Kaplan‒Meier (K‒M) curve was plotted and validated in the validation group.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThis study used R 4.0.3 (The R Foundation, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.r-project.org/\u003c/span\u003e\u003cspan address=\"https://www.r-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) for data analysis. Quantitative variables are expressed as means and standard deviations (medians and quartiles), and qualitative variables are expressed as frequencies and percentages. Comparisons were performed using an independent t test (Mann‒Whitney U test) and chi-square test.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eBasic clinical characteristics\u003c/h2\u003e \u003cp\u003eThe baseline data of 848 TB patients receiving ICU treatment are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, which indicates no statistically significant difference in the general primary data between the derivation and validation groups. A total of 146 patients died (106 in the derivation group and 40 in the validation group), and respiratory failure due to severe pneumonia was the main cause of death (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). There were 138 deaths (29.74%) among 464 patients requiring intensive care unit treatment for severe pneumonia, 68 deaths (18.68%) among 364 patients requiring noninvasive ventilation, and a high mortality rate of 42.11% among 152 tracheotomized patients. In addition, no significant difference was observed in the anti-TB regimen except for a greater prevalence of antibiotics in patients who died (presented in the appendix 2).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of baseline data between the derivation and validation groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDerivation group\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;594)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eValidation group\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;254)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDerivation group(n\u0026thinsp;=\u0026thinsp;594)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eValidation group(n\u0026thinsp;=\u0026thinsp;254)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, y\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59.50 [35.25, 73.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55.00 [35.00, 73.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRelapse, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e391 (65.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e169 (66.54)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemperature, \u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.80 [36.40, 37.30]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.80 [36.50, 37.50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNIV, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e255 (42.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e109 (42.91)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP, mm/Hg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e118.00 [102.00, 138.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e116.50 [99.25, 136.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTracheotomy, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e101 (17.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e51 (20.08)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP, mm/Hg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72.00 [62.00, 84.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70.00 [60.00, 80.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDVC, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e115 (19.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e53 (20.87)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlb, g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.70 [25.92, 36.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.80 [26.55, 35.60]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnti-fungal_drugs, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e240 (40.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e88 (34.65)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e414 (69.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e172 (67.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnticoagulants, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e236 (39.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e92 (36.22)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHan nationality, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e463 (77.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e183 (72.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBAC, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e120 (20.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e50 (19.69)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOPD, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e123 (20.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53 (20.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSputum_smear, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e112 (18.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e42 (16.54)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSyphilis, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 (3.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (2.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTB_DNA, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e120 (20.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e42 (16.54)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHIV, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (3.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (1.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eX_PERT, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e126 (21.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e46 (18.11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePneumothorax, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58 (9.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (7.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eATDR, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e98 (16.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36 (14.17)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHepatitis_B, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86 (14.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (11.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFungus, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e203 (34.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e85 (33.46)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiver_dysfunction, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e296 (49.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e124 (48.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMold, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12 (2.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6 (2.36)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCirrhosis, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (1.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (2.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWBC, \u0026times; 10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.35 [5.40, 12.15]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.67 [5.41, 11.85]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKidney_dysfunction, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63 (10.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (9.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNEU, \u0026times; 10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.02 [4.39, 10.57]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.97 [4.45, 10.38]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSevere_pneumonia, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e320 (53.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e144 (56.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLYM,\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.56 [0.33, 0.89]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.54 [0.32, 0.95]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e120 (20.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56 (22.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCD4, cell/ul\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e221.50 [116.00, 367.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e230.00 [101.25, 372.00]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e96 (16.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48 (18.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCD8, cell/ul\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e166.50 [75.25, 284.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e161.00 [71.00, 289.00]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiac_dysfunction, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e170 (28.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78 (30.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCRP, mg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e60.72 [24.02, 130.40]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e57.00 [25.60, 134.70]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMalignancy, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33 (5.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (4.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBNP, pg/mL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e732.35 [216.02, 2189.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e690.35 [211.38, 2126.00]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e271 (45.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e113 (44.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC-TnI, ng/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00 [0.00, 0.03]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00 [0.00, 0.02]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eData are summarized as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD if normally distributed, the median (first and third quartiles) if nonnormally distributed, and n (%) for categorical variables. Please refer to the abbreviation table at the end of the text.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eSBP\u003c/em\u003e, systolic blood pressure; \u003cem\u003eALb\u003c/em\u003e, albumin; \u003cem\u003eCOPD\u003c/em\u003e, chronic obstructive pulmonary disease; \u003cem\u003eHIV\u003c/em\u003e, human immunodeficiency virus; \u003cem\u003eBAC\u003c/em\u003e, bacterological examination for sputum; \u003cem\u003eNIV\u003c/em\u003e, noninvasive ventilator; \u003cem\u003eDVC\u003c/em\u003e, deep venous catheterization; \u003cem\u003eATDR\u003c/em\u003e, anti-TB drug resistance; \u003cem\u003eWBC\u003c/em\u003e, white blood cell; \u003cem\u003eNEU\u003c/em\u003e, neutrophilic granulocyte; \u003cem\u003eLYM\u003c/em\u003e, lymphocyte; \u003cem\u003eCRP\u003c/em\u003e, C-reactive protein; \u003cem\u003eBNP\u003c/em\u003e, B-type brain natriuretic peptide.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the derivation group, comparisons between the nonsurviving group and the surviving group showed that 30 variables, including age, systolic blood pressure (SBP), diastolic blood pressure (DBP), and the serum ALB concentration, were significantly different (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In the validation group, 23 variables were significantly different (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in the intergroup comparison between the nonsurviving group and the surviving group (in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of baseline data between the nonsurviving and surviving groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eDerivation group (n\u0026thinsp;=\u0026thinsp;594)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eValidation group (n\u0026thinsp;=\u0026thinsp;254)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlive (n\u0026thinsp;=\u0026thinsp;488)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDied (n\u0026thinsp;=\u0026thinsp;106)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAlive (n\u0026thinsp;=\u0026thinsp;214)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDied (n\u0026thinsp;=\u0026thinsp;40)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, y\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55.00 [33.00, 71.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70.00 [50.00, 80.75]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001❊❊\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e54.00 [33.25, 70.75]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e73.00 [44.50, 83.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.001 ❊\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemperature, \u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.80 [36.50, 37.30]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.70 [36.40, 37.30]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36.90 [36.50, 37.48]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36.65 [36.48, 37.65]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.329\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP, mm/Hg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e119.00 [104.00, 139.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e112.00 [95.00, 132.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02❊\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e118.00 [99.00, 136.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e116.00 [103.75, 133.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.825\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP, mm/Hg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73.00 [63.00, 84.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69.50 [58.25, 82.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.015❊\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e70.00 [60.25, 80.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e70.00 [60.00, 80.75]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.725\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlb, g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.45 [26.58, 36.80]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.60 [22.47, 30.60]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001❊❊\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30.25 [27.33, 36.30]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25.80 [22.17, 30.82]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001❊❊\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e327 (67.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87 (82.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.003❊\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e139 (64.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33 (82.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.046 ❊\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHan nationality, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e361 (73.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e102 (96.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001❊❊\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e147 (68.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36 (90.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.010 ❊\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOPD, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89 (18.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34 (32.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002❊\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35 (16.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18 (45.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001❊❊\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSyphilis, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (1.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (9.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001❊❊\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (1.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4 (10.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.012 ❊\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHIV, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (2.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (3.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (0.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2 (5.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.229\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePneumothorax, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53 (10.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (4.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19 (8.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (2.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.291\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHepatitis_B, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75 (15.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (10.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27 (12.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (2.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.110\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiver_dysfunction, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e252 (51.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44 (41.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e104 (48.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20 (50.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCirrhosis, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (1.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (1.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7 (3.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.526\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKidney_dysfunction, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46 (9.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (16.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20 (9.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3 (7.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.942\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSevere_pneumonia, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e220 (45.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100 (94.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001❊❊\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e106 (49.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38 (95.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001❊❊\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95 (19.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (23.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.410\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47 (21.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9 (22.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62 (12.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34 (32.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001❊❊\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40 (18.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8 (20.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiac_dysfunction, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e133 (27.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37 (34.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e65 (30.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13 (32.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.936\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMalignancy, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 (5.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (5.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11 (5.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.297\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e203 (41.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68 (64.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001❊❊\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e85 (39.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28 (70.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.001 ❊❊\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelapse, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e307 (62.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84 (79.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002❊\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e139 (64.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30 (75.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.292\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNIV, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e209 (42.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46 (43.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e87 (40.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22 (55.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.131\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTracheotomy, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55 (11.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46 (43.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001❊❊\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33 (15.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18 (45.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001❊❊\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDVC, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69 (14.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46 (43.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001❊❊\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39 (18.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14 (35.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.029 ❊\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntifungal_drugs, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e171 (35.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69 (65.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001❊❊\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e67 (31.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21 (52.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.016 ❊\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnticoagulants, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e164 (33.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72 (67.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001❊❊\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e66 (30.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26 (65.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001❊❊\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBAC, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75 (15.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45 (42.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001❊❊\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41 (19.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9 (22.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.786\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSputum_smear, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53 (10.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59 (55.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001❊❊\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25 (11.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17 (42.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001❊❊\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB_DNA, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84 (17.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36 (33.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001❊❊\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34 (15.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8 (20.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.681\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX_PERT, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80 (16.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46 (43.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001❊❊\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36 (16.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10 (25.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.313\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eATDR, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57 (11.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41 (38.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001❊❊\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25 (11.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11 (27.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.017 ❊\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFungus, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e129 (26.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74 (69.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001❊❊\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e61 (28.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24 (60.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001❊❊\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMold, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (0.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (7.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001❊❊\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 (1.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2 (5.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.529\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC,\u0026times; 10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.28 [5.28, 12.15]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.04 [5.82, 13.37]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.26 [5.41, 11.59]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.35 [6.32, 13.77]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.038 ❊\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNEU,\u0026times; 10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.77 [4.16, 10.18]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.12 [4.91, 11.22]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.021❊\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.78 [4.27, 9.89]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.19 [4.82, 11.61]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.026 ❊\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLYM,\u0026times; 10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.60 [0.36, 0.93]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.40 [0.27, 0.69]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001❊❊\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.60 [0.33, 0.97]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.38 [0.26, 0.59]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.020 ❊\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD4, cell/ul\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e236.50 [121.75, 397.25]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e150.00 [67.25, 265.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001❊❊\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e245.00 [126.25, 412.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e126.00 [66.50, 230.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001❊❊\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD8, cell/ul\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e186.00 [86.25, 312.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e101.50 [49.00, 207.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001❊❊\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e173.50 [84.00, 302.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e80.50 [53.75, 181.50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001❊❊\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP, mg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52.30 [21.92, 118.62]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95.45 [55.40, 184.60]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001❊❊\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e53.50 [19.30, 122.80]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e142.50 [73.62, 184.75]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001❊❊\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBNP, pg/mL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e518.85 [188.50, 1773.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1908.00 [1033.00, 4324.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001❊❊\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e532.15 [179.75, 1540.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2627.00 [1245.00, 7155.25]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001❊❊\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ec-TnI, ng/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00 [0.00, 0.01]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02 [0.00, 0.12]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001❊❊\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00 [0.00, 0.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.03 [0.00, 0.32]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001❊❊\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eSame as Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Data are summarized as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD if normally distributed, the median (first and third quartiles) if nonnormally distributed, and n (%) for categorical variables. ❊: \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.05; ❊❊: \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.001.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eRisk model establishment\u003c/h3\u003e\n\u003cp\u003eThe random forest results showed that only 7 variables (sputum smear, severe pneumonia, c-TnI, mold, age, DBP, and tracheotomy) had importance values greater than 0.05 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e-A). The nomogram of the 7 factors is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e-B.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePredicting the risk of death in patients with TB under ICU treatment. (The value of each variable is given a score on the point scale axis. A total score can be easily calculated by adding all scores together, and by projecting the complete score to the lower total point scale, we can estimate the probability of death.)\u003c/p\u003e \u003cp\u003eThe seven variables screened by the random forest method were included in Multivariate Cox analysis. Severe pneumonia, tracheotomy, positive sputum smear, and mold infection were found to be prognostic indices for death within 60 days in TB patients under ICU treatment (PI\u0026thinsp;=\u0026thinsp;0.0084 \u0026times; Age \u0026minus;\u0026thinsp;0.0026 \u0026times; DBP\u0026thinsp;+\u0026thinsp;2.1988 \u0026times; Severe pneumonia1\u0026thinsp;+\u0026thinsp;0.9094 \u0026times; Tracheotomy1\u0026thinsp;+\u0026thinsp;1.2253 \u0026times; Sputum smear1\u0026thinsp;+\u0026thinsp;0.826 \u0026times; Mold1\u0026thinsp;+\u0026thinsp;0.5147 \u0026times; c-TnI), as presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate Cox regression analysis of risk factors for death in patients with pulmonary TB receiving ICU treatment.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoef\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHR 95% \u003cem\u003eCI\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.998ཞ1.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.133\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;0.427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.986ཞ1.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.670\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSevere pneumonia1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.432\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.864ཞ21.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTracheotomy1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.483\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.653ཞ3.730\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSputum smear1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.223ཞ5.216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMold1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.087ཞ4.798\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC-TnI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.515\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.673\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.758ཞ3.694\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.203\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e1: Represents consolidation.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWe plotted a scatter plot based on the actual observed values (vertical coordinates) and derivative predicted values (horizontal coordinates), with the diagonal line as the reference line (IDEAL), and fitted a trend line to obtain the calibration curve. In both the derivation and validation groups, the calibration curves plotted by the model were close to those of IDEAL (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), suggesting that the model was well calibrated. Moreover, the decision curve showed that this model had better application accuracy and greater benefit (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe C index was 0.858 (95% \u003cem\u003eCI\u003c/em\u003e: 0.832\u0026ndash;0.885) in the derivation group and 0.824 (95% \u003cem\u003eCI\u003c/em\u003e: 0.774\u0026ndash;0.874) in the validation group, suggesting that the model has an excellent discriminatory ability. The ROC curve analysis showed the PI could predict death with good sensitivity (0.830) and specificity (0.867), and the cutoff value was 0.195 (the AUC was 0.894, 95% CI: 0.865 to 0.924). The results of the ROC curve analysis for the validation cohort with a threshold\u0026thinsp;=\u0026thinsp;0.195 are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, the results suggest that specificity\u0026thinsp;=\u0026thinsp;0.78 and sensitivity\u0026thinsp;=\u0026thinsp;0.75. When the prognostic index was greater than 0.195, patients had an increased risk of death (presented in K‒M survival analysis in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study established a prognostic risk model for TB patients treated in the ICU. We found that severe combined pneumonia, mold infection, a positive sputum smear, mold infection, and tracheotomy treatment were risk factors for 60-day in-hospital mortality in TB patients receiving ICU treatment. We further calculated the prognostic index based on the nomogram and found that the proportion of deaths increased when the prognostic index was greater than 0.195. These findings can help clinicians identify patients with high risk of death and develop rational treatment strategies to reduce mortality.\u003c/p\u003e \u003cp\u003eTB remains a severe threat to human lives in developing countries and among the low-income populations [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], which is a significant burden on society [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The United Nations Sustainable Development Goal (SDG) of ending the TB epidemic by 2030 has not yet been achieved [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. TB patients treated in ICUs account for a large proportion (86.67%) of the total number of TB deaths due to the complications and complexity of the disease [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], which is an important reason for the poor prognosis of TB patients. In this study, the in-hospital mortality rate of TB patients under ICU treatment within 60 days was as high as 17.22% (146/848), which was much higher than that of ordinary TB patients under short-term treatment (4.55%) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Therefore, early prevention and treatment after risk prediction can reduce overall TB patient mortality and improve prognosis. In the present study, the mortality rate of TB patients under ICU treatment was significantly lower than reported by Muthu V et al. [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e](17.22% vs. 44.4%), suggesting that the mortality rate of severe TB patients has decreased due to the significant improvement in TB treatment. Therefore, previous models and related factors are no longer applicable to the present situation. For developing countries whose health care systems are relatively weak, it is urgent to establish a risk prediction model suitable for the current ICU treatment paradigm. Our model provided important corresponding information.\u003c/p\u003e \u003cp\u003eR. Erbes et al. suggested that acute renal failure, mechanical ventilation, chronic pancreatitis, sepsis, acute respiratory distress syndrome (ARDS), and hospital-acquired pneumonia were independent risk factors for death in TB patients receiving intensive care in the ICU. Patients with renal dysfunction were also included in our study. Nevertheless, renal dysfunction was not significantly different between the nonsurviving group and the surviving group in this study. Sepsis and chronic pancreatitis did not occur at all, which might be due to the substantial improvement in health care. Tracheotomy and combined severe pneumonia were common independent risk factors in both models. Patients with severe TB are prone to severe pneumonia, and tracheotomy and mechanical ventilation are needed [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan additionalcitationids=\"CR17 CR18 CR19 CR20 CR21 CR22\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The risk of recurrent respiratory infections and death increases at the same time [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Ibn Saied W et al. also reported that severe acquired pneumonia in hospitals increases the risk of 30-day mortality by 82%, while ventilator-associated pneumonia can increase 30-day mortality by 38% [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cem\u003eA positive Mycobacterium tuberculosis\u003c/em\u003e sputum smear is the gold standard for the diagnosis of TB, which also indicates that TB is highly contagious and the immunity of the affected patients is relatively low[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. It was a significant independent risk factor in our study, and the risk index of death in the nomogram model increased by 1.2253 with this factor. A previous study demonstrated that a positive \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e sputum smear affects the outcome of treatment and that the mortality rate triples[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Combined mold infection is also common in TB patients due to diminished immunity. Models by Sun J and other scholars have shown that the high mortality rate of TB patients treated in the ICU is closely related to mold infections [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Chronic TB lesions damage the surface integrity of lung tissue and result in poor barrier function, which provides the conditions for mold infections. Anti-mold therapy significantly reduces the efficacy of anti-TB drugs, leading to delayed treatment and even death[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Therefore, strengthening the monitoring of \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e sputum smears and timely prevention and curing of mold infection can reduce the mortality rate of severe TB patients treated in the ICU.\u003c/p\u003e \u003cp\u003eSeveral previous studies have established different models for mortality in common TB patients, TB patients with different comorbidities or TB patients in particular regions[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The present model targeted patients treated in the ICU and provided strong differentiation (AUC) (0.894: 0.820). In addition, the present model takes more comprehensive factors into account, including basic demographic information, clinical data, laboratory tests, and treatment modalities, which makes it more advantageous for clinical application, especially in critically severe TB patients with more severe conditions. Instead of traditional survival analysis methods, this study used a nomogram to visualize logistic regression analysis results through graphical symbols[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], which helps medical staff identify patients at high risk of adverse treatment outcomes more intuitively at an early stage. The model in this study has a good fitting effect and high predictive accuracy, and the variables included in the established model are easily available through clinical tests. The model provides a high degree of differentiation (AUC\u0026thinsp;=\u0026thinsp;0.894) and a high net clinical benefit (0%-70%), which can help medical staff monitor the risk at the time of ICU admission to identify high-risk patients at an early stage and increase the success rate of TB treatment.\u003c/p\u003e \u003cp\u003eThere are several limitations in this study. First, this study was a retrospective analysis with selection bias. Second, this was a single-center study, and the model was not externally validated; thus, the potential for extrapolation of the results is limited. Third, chest imaging data were scarce because the criticality of the disease-related data was not included in this study. Future multicenter, large-sample, prospective studies should be conducted to obtain more findings.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eSevere pneumonia, tracheotomy, a positive sputum smear, and mold infection were found to be independent risk factors for death in our prediction model based on TB patients treated in the ICU. The model showed good discriminatory power and accuracy, implying that it has high clinical value for screening TB patients with an increased risk of death.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0598%;\"\u003e\n \u003cp\u003eICU \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85.9402%;\"\u003e\n \u003cp\u003eIntensive care unit\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0598%;\"\u003e\n \u003cp\u003eTB \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85.9402%;\"\u003e\n \u003cp\u003eTuberculosis \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0598%;\"\u003e\n \u003cp\u003eDBP \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85.9402%;\"\u003e\n \u003cp\u003eDiastolic blood pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0598%;\"\u003e\n \u003cp\u003ePI \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85.9402%;\"\u003e\n \u003cp\u003ePrognostic index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0598%;\"\u003e\n \u003cp\u003eDCA \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85.9402%;\"\u003e\n \u003cp\u003eDecision curve analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0598%;\"\u003e\n \u003cp\u003eROC \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85.9402%;\"\u003e\n \u003cp\u003eReceiver operating characteristic curve\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0598%;\"\u003e\n \u003cp\u003eAUC \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85.9402%;\"\u003e\n \u003cp\u003eThe area under the curve\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0598%;\"\u003e\n \u003cp\u003eCRRT \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85.9402%;\"\u003e\n \u003cp\u003eContinuous renal replacement therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0598%;\"\u003e\n \u003cp\u003eK‒M \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85.9402%;\"\u003e\n \u003cp\u003eKaplan‒Meier\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0598%;\"\u003e\n \u003cp\u003eSBP \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85.9402%;\"\u003e\n \u003cp\u003eSystolic blood pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0598%;\"\u003e\n \u003cp\u003eAlb \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85.9402%;\"\u003e\n \u003cp\u003eAlbumin\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0598%;\"\u003e\n \u003cp\u003eCOPD \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85.9402%;\"\u003e\n \u003cp\u003eChronic obstructive pulmonary disease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0598%;\"\u003e\n \u003cp\u003eHIV \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85.9402%;\"\u003e\n \u003cp\u003eHuman immunodeficiency virus\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0598%;\"\u003e\n \u003cp\u003eBAC \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85.9402%;\"\u003e\n \u003cp\u003eBacterological examination for sputum\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0598%;\"\u003e\n \u003cp\u003eNIV \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85.9402%;\"\u003e\n \u003cp\u003eNoninvasive ventilator\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0598%;\"\u003e\n \u003cp\u003eDVC \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85.9402%;\"\u003e\n \u003cp\u003eDeep venous catheterization\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0598%;\"\u003e\n \u003cp\u003eATDR \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85.9402%;\"\u003e\n \u003cp\u003eAnti-TB drug resistance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0598%;\"\u003e\n \u003cp\u003eWBC \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85.9402%;\"\u003e\n \u003cp\u003eWhite blood cell\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0598%;\"\u003e\n \u003cp\u003eNEU \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85.9402%;\"\u003e\n \u003cp\u003eNeutrophilic granulocyte\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0598%;\"\u003e\n \u003cp\u003eLYM \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85.9402%;\"\u003e\n \u003cp\u003eLymphocyte\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0598%;\"\u003e\n \u003cp\u003eCRP \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85.9402%;\"\u003e\n \u003cp\u003eC-reactive protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0598%;\"\u003e\n \u003cp\u003eBNP \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85.9402%;\"\u003e\n \u003cp\u003eB-type brain natriuretic peptide\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0598%;\"\u003e\n \u003cp\u003eSDG \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85.9402%;\"\u003e\n \u003cp\u003eSustainable Development Goal\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0598%;\"\u003e\n \u003cp\u003eARDS \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85.9402%;\"\u003e\n \u003cp\u003eAcute respiratory distress syndrome\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eSupplementary Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupplementary material is provided in the annex.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank to Dr. Hang Fu (Key Laboratory of Obstetric \u0026amp; Gynecologic and Pediatric Diseases and Birth Defects of Ministry of Education, Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu 610017, China) for valuable suggestions on the revision of the discussion.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKKH, XL and NZ conceptualised the study, KKH and XL wrote the proposal for the acquisition of ethical clearance, KKH and XL provided resources, JLH and TL carried out the investigation, KKH supervised the study, JLH and TL curated the data. KKH and JLH analysed the data, KKH, JLH, TL, XL and NZ wrote the original draft of the manuscript, XL and NZ critically reviewed and edited the manuscript. All authors read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Chengdu Science and Technology Bureau Technology Innovation R\u0026amp;D Program (2024-YF05-01215-SN) and the Chengdu Health Commission Medical Scientific Research Project (2023415).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used for the study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical clearance with reference number YJ-K2023-21-01 was obtained from the Research Ethics Committee of the Public Health Clinical Center of Chengdu, China. The study was performed according to the guidelines laid out by the Research Ethics Committee. Confidentiality of the participants\u0026apos; information and data resulted was assured. Informed consent was waived by the Research Ethics Committee of the Public Health Clinical Center of Chengdu, China.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFurin J, Cox H, Pai M. Tuberculosis. Lancet. 2019 Apr 20;393(10181):1642-1656. https://doi.org/10.1016/s0140-6736(19)30308-3\u003c/li\u003e\n\u003cli\u003eBohlbro AS, H\u0026oslash;nge BL, Engell-S\u0026oslash;rensen T, Mendes AM, Sifna A, Gomes V, Rudolf F, Wejse C. 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Cancer Med. 2020 Feb; 9(4): 1430-1440. https://doi.org/10.1002/cam4.2805\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Risk model, In-hospital mortality, ICU, TB","lastPublishedDoi":"10.21203/rs.3.rs-5447218/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5447218/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eTuberculosis (TB) is the leading cause of death from a single infectious disease. Current studies on TB patient mortality risk factors in intensive care are old and scarce. We aimed to create a model to predict in-hospital mortality risk for TB patients in ICU and identify mortality risk factors.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eTB patients' data from 2016 to 2020 admitted to the ICU were collected retrospectively and randomly split into derivation and validation groups at a 7:3 ratio. The main outcome was 60-day in-hospital mortality. Analyses included Cox, nomogram, decision curve, and Kaplan‒Meier methods.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 848 patients were included (594 in the derivation group and 254 in the validation group). A total of 106 (17.85%) patients died in the derivation group. Multivariate Cox regression analysis revealed that sputum smear, severe pneumonia, c-TnI, mold, age, diastolic blood pressure (DBP), and tracheotomy were independent risk factors for 60-day in-hospital mortality in ICU patients with TB, and the prognostic index (PI) was defined as follows: PI\u0026thinsp;=\u0026thinsp;0.0084 \u0026times; Age \u0026minus;\u0026thinsp;0.0026 \u0026times; DBP\u0026thinsp;+\u0026thinsp;2.1988 \u0026times; Severe pneumonia1\u0026thinsp;+\u0026thinsp;0.9094 \u0026times; Tracheotomy1\u0026thinsp;+\u0026thinsp;1.2253 \u0026times; Sputum smear1\u0026thinsp;+\u0026thinsp;0.826 \u0026times; Mold1\u0026thinsp;+\u0026thinsp;0.5147 \u0026times; c-TnI. Decision curve analysis (DCA) diagrams showed that the diagnostic probabilities of the derivation and validation groups were 0\u0026ndash;70% and 0\u0026ndash;58% respectively, with high model application accuracy and net benefit. Receiver operating characteristic (ROC) curve analysis revealed that the PI could predict death with good sensitivity (0.830) and specificity (0.867), and the cutoff value was 0.195 (the area under the curve (AUC) was 0.894, 95% \u003cem\u003eCI\u003c/em\u003e: 0.865 to 0.924). K‒M analysis revealed that the proportion of deaths was increased when the PI was \u0026ge;\u0026thinsp;0.195.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe nomogram-based prediction model of mortality within 60 days in TB patients in the ICU showed good discrimination and accuracy, and is of great clinical value for screening patients at high risk of death to support the development of intervention strategies for ICU patients with TB and to reduce mortality.\u003c/p\u003e","manuscriptTitle":"Establishing and Validating a Risk Model for In-hospital Mortality Within 60 Days Under ICU Treatment for Tuberculosis in China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-18 16:55:44","doi":"10.21203/rs.3.rs-5447218/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c5c1db0e-9664-4bc6-a1e3-0c1752bbe00b","owner":[],"postedDate":"December 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-08T23:23:19+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-18 16:55:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5447218","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5447218","identity":"rs-5447218","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}