Objective To systematically evaluate the risk factors associated with the occurrence of nosocomial infections(NI)in patients undergoing extracorporeal membrane oxygenation(ECMO)support.Methods A computerized systematic search was performed aross the Chinese databases including CNKI,Wanfang Database,China Biomedical Literature Database(CBMdisc),and Weipu,as well as the English databases such as PubMed,EMBase,Web of Science,and the Cochrane Library for case-control or cohort studies on the risk factors of hospital-acquired infections in patients undergoing ECMO support from the time of database construction to February 2023.The relevant literatures were screened by two researchers independently.Meta-analysis was performed using RevMan 5.4 software.Results A total of 20 papers,including 2 746 patients and 16 risk factors,were included.Meta-analysis results showed that older age[>50 years:odds ratio(OR)= 2.87,95％ confidence interval(95％ CI)was 1.24-6.63,P = 0.01;>65 years:OR = 1.66,95％ CI was 1.22-2.26,P = 0.001],combined hypertension(OR = 1.48,95％ CI was 1.01-2.15,P = 0.04),combined diabetes mellitus(OR = 1.40,95％ CI was 1.02-1.94,P = 0.04),sequential organ failure assessment(SOFA)was higher(OR = 1.06,95％ CI was 1.02-1.10,P = 0.000 7),the installation of ECMO in the intensive care unit(ICU,OR=1.48,95％ Ciwas 1.11-1.99,P=0.008),ECMO course(OR=1.27,95％ Ciwas 1.05-1.54,P = 0.01),ventilator-assistance for >48 hours(OR = 4.91,95％ CI was 2.40-10.05,P<0.000 1),and tracheotomy(OR = 9.56,95％ CI was 3.60-25.35,P<0.000 01)were identified as ECMO risk factors for hospital-acquired infections in patients.Conclusion Older age,combined hypertension,diabetes mellitus,higher SOFA,ECMO installation site in ICU,ECMO course,ventilator assistance>48 hours,tracheotomy are the risk factors for the occurrence of hospital-acquired infections in patients with ECMO,healthcare professionals should promptly identify the risk factors related to hospital-acquired infections,and take active and effective measures against controllable risk factors,including early intervention to prevent the occurrence of NI in ECMO patients.

Objective:To analyze the factors influencing pulmonary infections in elderly neurocritical patients in the intensive care unit (ICU) and to explore the predictive value of risk factors for pulmonary infections.Methods:The clinical data of 713 elderly neurocritical patients [age ≥ 65 years, Glasgow coma score (GCS) ≤ 12 points] admitted to the department of critical care medicine of the Affiliated Hospital of Guizhou Medical University from 1 January 2016 to 31 December 2019 were retrospectively analyzed. According to whether or not they had HAP, the elderly neurocritical patients were divided into hospital-acquired pneumonia (HAP) group and non-HAP group. The differences in baseline data, medication and treatment, and outcome indicators between the two groups were compared. Logistic regression analysis was used to analyze the factors influencing the occurrence of pulmonary infection.The receiver operator characteristic curve (ROC curve) was plotted for risk factors and a predictive model was constructed to evaluate the predictive value for pulmonary infection.Results:A total of 341 patients were enrolled in the analysis, including 164 non-HAP patients and 177 HAP patients. The incidence of HAP was 51.91%. According to univariate analysis, compared with the non-HAP group, mechanical ventilation time, the length of ICU stay and total hospitalization in the HAP group were significantly longer [mechanical ventilation time (hours): 171.00 (95.00, 273.00) vs. 60.17 (24.50, 120.75), the length of ICU stay (hours): 263.50 (160.00, 409.00) vs. 114.00 (77.05, 187.50), total hospitalization (days): 29.00 (13.50, 39.50) vs. 27.00 (11.00, 29.50), all P < 0.01], the proportion of open airway, diabetes, proton pump inhibitor (PPI), sedative, blood transfusion, glucocorticoids, and GCS ≤ 8 points were significantly increased than those in HAP group [open airway: 95.5% vs. 71.3%, diabetes: 42.9% vs. 21.3%, PPI: 76.3% vs. 63.4%, sedative: 93.8% vs. 78.7%, blood transfusion: 57.1% vs. 29.9%, glucocorticoids: 19.2% vs. 4.3%, GCS ≤ 8 points: 83.6% vs. 57.9%, all P < 0.05], prealbumin (PA) and lymphocyte count (LYM) decreased significantly [PA (g/L): 125.28±47.46 vs. 158.57±54.12, LYM (×10 9/L): 0.79 (0.52, 1.23) vs. 1.05 (0.66, 1.57), both P < 0.01]. Logistic regression analysis showed that open airway, diabetes, blood transfusion, glucocorticoids and GCS ≤ 8 points were independent risk factors for pulmonary infection in elderly neurocritical patients [open airway: odds ratio ( OR) = 6.522, 95% confidence interval (95% CI) was 2.369-17.961; diabetes: OR = 3.917, 95% CI was 2.099-7.309; blood transfusion: OR = 2.730, 95% CI was 1.526-4.883; glucocorticoids: OR = 6.609, 95% CI was 2.273-19.215; GCS ≤ 8 points: OR = 4.191, 95% CI was 2.198-7.991, all P < 0.01], and LYM, PA were the protective factors for pulmonary infection in elderly neurocritical patients (LYM: OR = 0.508, 95% CI was 0.345-0.748; PA: OR = 0.988, 95% CI was 0.982-0.994, both P < 0.01). ROC curve analysis showed that the area under the ROC curve (AUC) for predicting HAP using the above risk factors was 0.812 (95% CI was 0.767-0.857, P < 0.001), with a sensitivity of 72.3% and a specificity of 78.7%. Conclusions:Open airway, diabetes, glucocorticoids, blood transfusion, GCS ≤ 8 points are independent risk factors for pulmonary infection in elderly neurocritical patients. The prediction model constructed by the above mentioned risk factors has certain predictive value for the occurrence of pulmonary infection in elderly neurocritical patients.

OBJECTIVE: To investigate the prevalence, risk factors, duration and outcome of delirium in intensive care unit (ICU) patients. METHODS: A prospective observational study was conducted for critically ill patients admitted to the department of critical care medicine, the Affiliated Hospital of Guizhou Medical University from September to November 2021. Delirium assessments were performed twice daily using the Richmond agitation-sedation scale (RASS) and confusion assessment method of ICU (CAM-ICU) for patients who met the inclusions and exclusion criteria. Patient's age, gender, body mass index (BMI), underlying disease, acute physiologic assessment and chronic health evaluation (APACHE) at ICU admission, sequential organ failure assessment (SOFA) at ICU admission, oxygenation index (PaO₂/FiO₂), diagnosis, type of delirium, duration of delirium, outcome, etc. were recorded. Patients were divided into delirium and non-delirium groups according to whether delirium occurred during the study period. The clinical characteristics of the patients in the two groups were compared, and risk factors for the development of delirium were screened using univariate analysis and multivariate Logistic regression analysis. RESULTS: A total of 347 ICU patients were included, and delirium occurred in 57.6% (200/347) patients. The most common type was hypoactive delirium (73.0% of the total). Univariate analysis showed statistically significant differences in age, APACHE score and SOFA score at ICU admission, history of smoking, hypertension, history of cerebral infarction, immunosuppression, neurological disease, sepsis, shock, glucose (Glu), PaO₂/FiO₂ at ICU admission, length of ICU stay, and duration of mechanical ventilation between the two groups. Multivariate Logistic regression analysis showed that age [odds ratio (OR) = 1.045, 95% confidence interval (95%CI) was 1.027-1.063, P < 0.001], APACHE score at ICU admission (OR = 1.049, 95%CI was 1.008-1.091, P = 0.018), neurological disease (OR = 5.275, 95%CI was 1.825-15.248, P = 0.002), sepsis (OR = 1.941, 95%CI was 1.117-3.374, P = 0.019), and duration of mechanical ventilation (OR = 1.005, 95%CI was 1.001-1.009, P = 0.012) were all independent risk factors for the development of delirium in ICU patients. The median duration of delirium in ICU patients was 2 (1, 3) days. Delirium was still present in 52% patients when they discharged from the ICU. CONCLUSIONS The prevalence of delirium in ICU patients is over 50%, with hypoactive delirium being the most common. Age, APACHE score at ICU admission, neurological disease, sepsis and duration of mechanical ventilation were all independent risk factors for the development of delirium in ICU patients. More than half of patients with delirium were still delirious when they discharged from the ICU.
Humans ; Prevalence ; Critical Care ; Risk Factors ; Sepsis ; Intensive Care Units

OBJECTIVE: To compare and analyze the effect of unplanned versus planned admission to the intensive care unit (ICU) on the prognosis of high-risk patients after surgery, so as to provide a clinical evidence for clinical medical staff to evaluate whether the postoperative patients should be transferred to ICU or not after surgery. METHODS: The clinical data of patients who were transferred to ICU after surgery admitted to the Affiliated Hospital of Guizhou Medical University from January to December in 2021 were retrospectively analyzed, including gender, age, body mass index, past history (whether combined with hypertension, diabetes, pulmonary disease, cardiac disease, renal failure, liver failure, hematologic disorders, tumor, etc.), acute physiology and chronic health evaluation II (APACHE II), elective surgery, pre-operative hospital consultation, length of surgery, worst value of laboratory parameters within 24 hours of ICU admission, need for invasive mechanical ventilation (IMV), duration of IMV, length of ICU stay, total length of hospital stay, ICU mortality, in-hospital mortality, and survival status at 30th day postoperative. The unplanned patients were further divided into the immediate transfer group and delayed transfer group according to the timing of their ICU entrance after surgery, and the prognosis was compared between the two groups. Cox regression analysis was used to find the independent risk factors of 30-day mortality in patients transferred to ICU after surgery. RESULTS: Finally, 377 patients were included in the post-operative admission to the ICU, including 232 in the planned transfer group and 145 in the unplanned transfer group (42 immediate transfers and 103 delayed transfers). Compared to the planned transfer group, patients in the unplanned transfer group had higher peripheral blood white blood cell count (WBC) at the time of transfer to the ICU [×10⁹/L: 10.86 (7.09, 16.68) vs. 10.11 (6.56, 13.27)], longer total length of hospital stay [days: 23.00 (14.00, 34.00) vs. 19.00 (12.00, 29.00)], and 30-day post-operative mortality was higher [29.66% (43/145) vs. 17.24% (40/232)], but haemoglobin (Hb), arterial partial pressure of carbon dioxide (PaCO₂), oxygenation index (PaO₂/FiO₂), and IMV requirement rate were lower [Hb (g/L): 95.00 (78.00, 113.50) vs. 98.00 (85.00, 123.00), PaCO₂ (mmHg, 1 mmHg ≈ 0.133 kPa): 36.00 (29.00, 41.50) vs. 39.00 (33.00, 43.00), PaO₂/FiO₂ (mmHg): 197.00 (137.50, 283.50) vs. 238.00 (178.00, 350.25), IMV requirement rate: 82.76% (120/145) vs. 93.97% (218/232)], all differences were statistically significant (all P < 0.05). Kaplan-Meier survival curve showed that the 30-day cumulative survival rate after surgery was significantly lower in the unplanned transfer group than in the planned transfer group (Log-Rank test: χ² = 7.659, P = 0.006). Univariate Cox regression analysis showed that unplanned transfer, APACHE II score, whether deeded IMV at transfer, total length of hospital stay, WBC, blood K⁺, and blood lactic acid (Lac) were associated with 30-day mortality after operation (all P < 0.05). Multifactorial Cox analysis showed that unplanned transfer [hazard ratio (HR) = 2.45, 95% confidence interval (95%CI) was 1.54-3.89, P < 0.001], APACHE II score (HR = 1.03, 95%CI was 1.00-1.07, P = 0.031), the total length of hospital stay (HR = 0.86, 95%CI was 0.83-0.89, P < 0.001), the need for IMV on admission (HR = 4.31, 95%CI was 1.27-14.63, P = 0.019), highest Lac value within 24 hours of transfer to the ICU (HR = 1.17, 95%CI was 1.10-1.24, P < 0.001), and tumor history (HR = 3.12, 95%CI was 1.36-7.13, P = 0.007) were independent risk factors for patient death at 30 days post-operative, and the risk of death was 2.45 times higher in patients unplanned transferred than in those planned transferred. Subgroup analysis showed that patients in the delayed transfer group had significantly longer IMV times than those in the immediate transfer group [hours: 43.00 (11.00, 121.00) vs. 17.50 (2.75, 73.00), P < 0.05]. CONCLUSIONS The 30-day mortality, WBC and total length of hospital stay were higher in patients who were transferred to ICU after surgery, and PaO₂/FiO₂ was lower. Unplanned transfer, oncology history, use of IMV, APACHE II score, total length of hospital stay, and Lac were independent risk factors for patient death at 30 days postoperatively, and patients with delayed transfer to ICU had longer IMV time.
Humans ; Retrospective Studies ; Respiration, Artificial ; Hospitalization ; Prognosis ; Intensive Care Units

Objective:To investigate the utilization status and awareness of digital hospital construction among medical staff in critical care department of primary hospitals, so as to promote the process of digital medical health.Methods:One to two doctors and nurses (in the department on that day) from public hospitals in 88 counties and urban areas in 9 cities of Guizhou Province were enrolled of field investigation. The questionnaires form were filled in on-site and sorted out and analyzed by special personnel.Results:A total of 297 medical staff from the department of critical care medicine of 146 hospitals were included. All the questionnaires were filled in with their real names, including 152 doctors and 145 nurses. There were 24 class Ⅲ Grade A hospitals and 122 class Ⅱ and all the hospitals had implemented digital information management. The awareness of hospital digital information management system was generally low among the surveyed medical staff, and the awareness of hospital information system (HIS) was the highest (86.5%), followed by laboratory information management system (LIS, 41.4%) and image archiving and communication system (PACS, 40.7%). The awareness of hospital management system (HERP) was the lowest (7.7%). The total number of remote consultations conducted by hospitals using big data Internet was 25 428 times in 2020, with a median of 24.5 (88.0, 240.0) times in each hospital. From 2018 to 2020, the total number of patients admitted to the intensive care unit of the hospital was 50 473, 57 565 and 57 907, respectively, of which the number of patients over 65 years old accounted for 37.47%, 41.26% and 43.31%, respectively (all P > 0.05). There were 4 242 cases of remote consultation using big data Internet in the department every year, with a median of 257.50 (96.50, 958.25) cases. 12.12% of the departments had independent critical monitoring systems, and 8.75% of them could capture data automatically to form tables. 96.30% of the medical staff participated in systematic and professional training on basic knowledge, basic theory and basic medical care skills through the Internet platform, and the number of meetings, studies and training in the provinces and prefectures were 282 and 357 times per year, respectively. More than 90% of the departments initiated remote consultation, arranged referral or admitted patients who had improved status after treatment in superior hospitals through the Internet platform. Most of the patients (69.02%) were from the lower level of the hospital. The total number of out-patient consultations was 2 959 times per year, with a median of 296 (185 473) times. 54.79% of the departments had fixed service villages, and 28.08% of the departments had fixed service population. The median furthest visit distance was 52.5 (30.0, 80.0) kilometers, and the median average visit distance was 30.0 (20.0, 50.0) kilometers. 54.88% of medical staff believed that the biggest difficulties encountered during house visits were insufficient energy and too large service groups or regions. More than 90% of medical staff had been exposed to cloud learning and cloud training, and most of the surveyed medical staff believed that cloud learning and cloud training greatly improved medical service capacity and service efficiency of medical institutions (71.04% and 67.01%, respectively). Meanwhile, they believe that "Internet + health big data" projects from various aspects brought advantage to medical institutions, but there are also low utilization rate of Internet medical equipment by village doctors, low acceptance for telemedicine and mobile hospitals by farmers. Conclusions:Guizhou public hospitals have implementation of digital information management at the grass-roots level, the surveyed health care workers have a relatively low awareness of the digital information management system, hospital use big data Internet for remote consultation is uneven, intensive care medicine is a clinical discipline used in most remote consultation information system, and can complete two-way referrals. In the past three years, the discipline operation showed an upward trend year by year. Medical staff use artificial intelligence devices such as cloud learning and training to improve medical service capacity and efficiency. The digital transformation of primary hospitals is being continuously improved.

Objective:Through retrospective analysis of the admission and treatment of patients in the department of critical care medicine of the Affiliated Hospital of Guizhou Medical University over the past 5 years, it provides a basis for the construction of the subspecialty of intensive care medicine.Methods:Collect clinical data of patients admitted to the department of critical care medicine of the Affiliated Hospital of Guizhou Medical University from January 1, 2016 to December 31, 2020, including gender, age, first consultation department, intensive care unit (ICU) hospitalization time, ventilator use time, main diagnosis, acute physiology and chronic health evaluationⅡ(APACHEⅡ) when transferred into and out of ICU, treatment results, whether to give mechanical ventilation, whether to use a non-invasive ventilator to assist breathing, whether to die in 24 hours, rescue times and success rate, etc. Changes in the above indicators during the 5 years were analyzed.Results:In the past 5 years, our hospital has treated 2 668 patients in the comprehensive ICU with severe neurological, severe circulation, and severe trauma as the main treatment area, including 1 648 males and 1 020 females; aged 6 months to 94 years old, the average age (53.49±19.03) years old. Neurosurgery (907 cases) was the most frequently diagnosed department, the top 3 diseases were cerebral hemorrhage (539 cases), septic shock (214 cases), and hypovolemic shock (200 cases); ICU hospitalization time was 126 (52, 253) hours, ventilator time was 65 (17, 145) hours, APACHE Ⅱ scores were 23.29±8.12 and 12.99±6.37 when transferred into and out of ICU. The proportion of receiving mechanical ventilation was 92.94% (2 147/2 310), and 314 cases used non-invasive ventilators. 84 cases died within 24 hours (mortality was 3.15%). A total of 2 585 rescues were performed, and the rescue success rate was 92.84% (2 400/2 585). From 2016 to 2020, the 5-year cure rates were 65.92%, 65.83%, 61.53%, 65.64%, 69.06%, respectively, and the 5-year mortality were 13.13%, 14.29%, 18.89%, 16.69%, 13.38%, respectively.Conclusions:With the continuous expansion of critical care medicine, the establishment of classified subspecialties can focus on the admission of patients, so that treatment can be professionalized and standardized, improve the cure rate, and reduce mortality. At the same time, medical staff can focus on management and learning related expertise to master the disease, it is also more in-depth, which is helpful for doctors to improve themselves, and is conducive to the proficiency of related sub-specialties, and lays a good foundation for the development of the department.

Causal inference research is a causal test designed to assess the impact of exposures on outcomes.Both experimental and observational studies can be used to examine causal associations between exposure factors and outcomes. Experimental studies are sometimes limited by factors such as ethics or experimental conditions. Observational studies account for a large proportion in clinical studies, but the effectiveness and research value of observational studies will be affected if the design of observational studies is not rigorous and the confounding factors are not well controlled.The Guidelines for controlling confounding factors and reporting results in causal inference studie formulated by a special group of 47 editors from 35 journals from all over the world provide good guidance to researchers. This article interprets the guidelines and hopes to provide help for clinical researchers.

Objective:To investigate the risk factors that were associated with the death of elderly patients who were admitted to the intensive care unit (ICU) after elective abdominal surgery, and to find reliable and sensitive predictive indicators for early interventions and reducing the mortality.Methods:A retrospective case-control study was conducted. The clinical data of elderly (age≥65 years old) patients after elective abdominal surgery admitted to the ICU of the Affiliated Hospital of Guizhou Medical University from January 1st 2016 to December 31st 2020 were collected, including the patient's gender, age, body mass index (BMI), medical history, American Society of Anesthesiologists (ASA) grades, surgical classification, intraoperative blood loss, duration of operation, interval time between end of operation and admission to the ICU, acute physiology and chronic health evaluationⅡ(APACHEⅡ) score and the worst laboratory examination results within 24 hours of ICU admission, the first blood gas analysis in ICU, the duration of invasive mechanical ventilation, and the length of ICU stay. Postoperative abdominal infection was evaluated by the pathogenic culture of peritoneal drainage fluid and clinical symptoms and signs. The patients were divided into death group and survival group based on clinical outcomes, and clinical data were compared between the two groups. Binary multivariate Logistic regression analysis was used to screen the risk factors of death, and the receiver operator characteristic curve (ROC curve) was plotted to analyze the predictive values of these risk factors.Results:A total of 226 elderly patients with elective abdominal surgery were admitted to the ICU of our hospital during the past 5 years, of whom, two patients who did not undergo laboratory examinations within 24 hours of admission to the ICU were excluded. Finally, 224 patients met the criteria, with 158 survivors and 66 deaths. Univariate analysis showed that: compared with survival group, APACHEⅡscore, blood lactate acid (Lac) and the proportion of postoperative abdominal infection were higher in death group [APACHEⅡ score: 27.5 (25.0, 31.3) vs. 23.0 (18.0, 27.0), Lac (mmol/L): 2.9 (1.8, 6.6) vs. 1.8 (1.1, 2.8), the proportion of postoperative abdominal infection: 65.2% (43/66) vs. 35.4% (56/158), all P < 0.01], prothrombin time (PT), activated partial thromboplastin time (APTT) and interval time between end of surgery and admission to ICU were longer [PT (s): 17.20 (14.50, 18.63) vs. 14.65 (13.90, 16.23), APTT (s): 45.15 (38.68, 55.15) vs. 39.45 (36.40, 45.70), interval time between end of surgery and admission to ICU (hours): 39.2 (0.7, 128.9) vs. 0.7 (0.3, 2.0), all P <0.01], postoperative hemoglobin (Hb), platelet count (PLT), prealbumin (PA), mean arterial pressure (MAP) and oxygenation index (PaO 2/FiO 2) were lower in death group [Hb (g/L): 95.79±23.64 vs. 105.58±19.82, PLT (×10 9/L): 138.5 (101.0, 177.5) vs. 160.5 (118.5, 232.3), PA (g/L): 80.88±43.63 vs. 116.54±50.80, MAP (mmHg, 1 mmHg = 0.133 kPa): 76.8±19.1 vs. 91.6±19.8, PaO 2/FiO 2 (mmHg): 180.0 (123.5, 242.5) vs. 223.5 (174.8, 310.0), all P < 0.05]. Binary multivariate Logistic regression analysis showed that APACHEⅡscore [odds ratio ( OR) = 1.187, 95% confidence interval (95% CI) =1.008-1.294, P < 0.001], interval time between end of operation and admission to ICU ( OR = 1.005, 95% CI = 1.001-1.009, P = 0.016) and postoperative abdominal infection ( OR = 2.630, 95% CI = 1.148-6.024, P = 0.022) were independent risk factors for prognosis in these patients. MAP ( OR = 0.978, 95% CI = 0.957-0.999, P = 0.041) and PaO 2/FiO 2 ( OR = 0.994, 95% CI = 0.990-0.998, P = 0.003) were protective factors for the patients' prognosis. Lac, Hb, PLT, PA, PT and APTT had no predictive value for the prognosis of elderly patients admitted to ICU after elective abdominal surgery [ OR value and 95% CI were 1.075 (0.945-1.223), 1.011 (0.99-1.032), 1.000 (0.995-1.005), 0.998 (0.989-1.007), 1.051 (0.927-1.192) and 1.003 (0.991-1.016), respectively, all P > 0.05. ROC curve analysis showed that APACHEⅡscore, interval time between end of operation and admission to the ICU and the postoperative abdominal infection had certain predictive values for the prognosis of elderly patients, the area under ROC curve (AUC) were 0.755, 0.732 and 0.649 respectively, all P < 0.001; When the cut-off of APACHEⅡscore and interval time between end of operation and admission to the ICU were 24.5 scores and 2.15 hours, the sensitivity were 78.8% and 66.7%, respectively, and the specificity were 62.0% and 76.6%, respectively. The combined predictive value of the three variables was the highest, which AUC was 0.846, the joint prediction probability was 0.27, the sensitivity was 83.3%, and the specificity was 75.3%. Conclusion:APACHEⅡscore, interval time between end of surgery and admission to ICU, and postoperative abdominal infection may be independent risk factors for the death of elderly patients who were admitted to the ICU after elective abdominal surgery, there would be far greater predictive values when the three variables were combined.

Objective:To investigate the standardized construction of critical care departments in different cities and counties of Guizhou province to promote the homogenization development of critical care medicine in Guizhou Province.Methods:Using research methods such as field investigation and data collection, the public hospitals of 88 counties and urban areas in 9 prefectures and cities of Guizhou province were divided into five routes: southeast, northeast, northwest, southwest, and Guiyang. To design the survey form for the standardized construction of ICU, the e-form was sent to the director of ICU or his/her designated personnel by email or wechat 2-3 days in advance. Check the authenticity of data item by item on site, and leave the hospital after checking the receipt form.Results:From April to July 2021, the survey and research data collection was completed for 146 public hospitals (excluding provincial hospitals) with intensive care departments in 88 counties and cities of 9 dizhou cities in Guizhou Province, including 24 Grade-Ⅲ Level A hospitals. 122 Grade-Ⅱ and above hospitals (including 8 Grade-Ⅲ Level B hospitals, 11 Grade-Ⅲ comprehensive hospitals, 97 Level-Ⅱ A hospitals, 3 Level-Ⅱ B hospitals, and 3 Level-Ⅱ comprehensive hospitals). 146 public hospitals have a total of 80 983 beds and 104 017 open beds. The department of Critical Care has 2 035 beds. The ratio of actual beds in ICU to total beds in hospital was 2.51%. From 1999 to 2010, 18 (12.33%) established departments, and from 2011 to 2021, 128 (87.67%) established departments. The total area of the discipline is 113 355.48 m 2, with an average bed area of 55.70 m 2. There were 97 hospitals with 1.5-2.0 m bed spacing, accounting for 66.44%, and 49 hospitals with 2.1- > 2.5 m spacing, accounting for 33.56%. The number of negative pressure wards: 1 in each of 43 hospitals, accounting for 29.45%; 103 hospitals did not have, accounting for 70.55%. The number of single rooms: 288 in 140 hospitals, accounting for 95.89%; 6 hospitals did not have, accounting for 4.11%. Central oxygen supply: 138 hospitals have (94.52%); 8 hospitals did not have, accounting for 5.48%. Natural ventilation: in 129 hospitals with 88.36%; 17 hospitals did not have, accounting for 11.64%. Specialized ICU construction: 66 hospitals, accounting for 45.21%; none in 80 hospitals, accounting for 54.79%. There are 3 712 doctors and nurses in 146 public hospitals. The total number of doctors was 1 041, and the ratio of doctors to beds was 0.51∶1. The total number of nurses was 2 675, and the ratio of nurses to beds was 1.31∶1. Conclusions:All 88 counties and districts in 9 prefectures and cities of Guizhou province have established intensive care medicine departments. The standardization of the discipline construction has been significantly improved. Lack of talents is still an important factor restricting the rapid development of the discipline.

Objective:To know the critical care resources of the different class-hospitals in Guizhou Province, China, and to provide the direction and evidence for quality improvement and discipline construction of critical care medicine in Guizhou Province.Methods:The resource status of the departments of intensive care unit (ICU) in Guizhou Province was obtained through form filling and/or field investigation. The forms were filled and submitted from May 2017 to February 2018, and the field investigation (some of the hospitals) was carried out in March 2018. The data of hospitals in Guizhou Province in 2018, was obtained from the official website of Health Committee of Guizhou Province, which was released online on November 28th, 2019. The obtained data were summarized and analyzed according to different aspects such asthe status of ICU construction, main equipment configuration and technology implementation.Results:There were 39 third-class hospitals and 77 second-class hospitals included in this study, which accounted for 76.5% (39/51) of third-class public hospitals and 50.0% (77/154) of second-class public hospitals respectively. Among them, there were 86.8% (33/38) of third-class general hospitals and 50.4% (69/137) of second-class general hospitals respectively. In terms of ICU construction, compared with the ICUs of second-class hospitals, the ICUs of third-class hospitals were established earlier [years: 2011 (2008, 2012) vs. 2013 (2011, 2015), P < 0.01], had more ICU beds, doctors and nurses [15 (11, 20) vs. 8 (6, 10), 9 (8, 11) vs. 6 (5, 7), 25 (20, 41) vs. 15 (12, 19), respectively, all P < 0.01]. However, there were no significant differences regarding the doctor-bed ratio and the nurse-bed ratio in ICUs between second-class hospitals and third-class hospitals. In terms of main equipment configuration, compared with the ICUs of second-class hospitals, the ICUs of third-class hospitals had more ventilators, higher ratio of ventilators to beds, more infusion pumps, higher ratio of infusion pumps to beds, more monitor, gastrointestinal nutrition pumps and single rooms, and higher proportion of ICUs equipped with negative pressure rooms [ventilators: 14 (10, 18) vs. 6 (4, 8), ratio of ventilators to beds: 1.0 (0.7, 1.1) vs. 0.8 (0.6, 1.0), infusion pumps: 10 (6, 20) vs. 5 (3, 8), ratio of infusion pumps to beds: 0.8 (0.0, 1.0) vs. 0.0 (0.0, 0.4), monitor: 18 (13, 24) vs. 9 (6, 12), gastrointestinal nutrition pumps: 2 (1, 5) vs. 1 (0, 3), single rooms: 2 (1, 3) vs. 1 (0, 3), proportion of ICUs equipped with negative pressure rooms: 53.8% (21/39) vs. 31.5% (23/73), respectively, all P < 0.05]. Furthermore, there were higher proportions of ICUs equipped with portable ventilator, pulse indicator continuous cardiac output monitoring (PiCCO), intra-aortic balloon pump (IABP), extra-corporeal membrane oxygenation (ECMO), B ultrasound machine, bronchoscope, pressure of end-tidal carbondioxide (P ETCO 2) monitoring, bispectral index (BIS) monitoring, bedside gastroscopy, the apparatus used for the prevention of deep vein thrombosis of lower extremity in third-class hospitals than in second-class hospitals [portable ventilator: 86.7% (26/30) vs. 59.6% (28/47), 43.3% (13/30) vs. 1.5% (1/66), 14.3% (4/28) vs. 0% (0/65), 10.7% (3/28) vs. 0% (0/65), 62.5% (20/32) vs. 37.3% (25/67), 97.1% (33/34) vs. 63.6% (42/66), 60.6% (20/33) vs. 28.4% (19/67), 17.2% (5/29) vs. 0% (0/65), 27.6% (8/29) vs. 1.5% (1/65), 77.4% (24/31) vs. 52.3% (34/65), respectively, all P < 0.05]. In terms of skills development, there were more ICUs carried out intracranial pressure monitoring, abdominal pressure monitoring, ultrasound diagnosis, bronchoscope examination and treatment and blood purification in third-class hospitals than in second-class hospitals [31.6% (12/38) vs. 14.7% (11/75), 75.7% (28/37) vs. 38.6% (27/70), 61.5% (24/39) vs. 24.3% (18/74), 89.7% (35/39) vs. 45.9% (34/74), 92.3% (36/39) vs. 48.6% (36/74), respectively, all P < 0.05]. Conclusions:The data were mainly derived from public general hospitals in Guizhou Province. Compared with the ICUs of second-class hospitals, the ICUs of third-class hospitals were founded earlier and larger, had better hardware configuration and could carry out more skills. However, the human resource situations were similar between second-class hospitals and third-class hospitals. Both second-class hospitals and third-class hospitals have a need to improve the allocation of manpower and equipment and expand various skills in ICUs, while it is more urgent for second-class hospitals.