Effectiveness of an artificial intelligence clinical assistant decision support system to improve the incidence of hospital-associated venous thromboembolism: a prospective, randomised controlled study.

BACKGROUND: Thromboprophylaxis has been determined to be safe, effective and cost-effective for hospitalised patients at venous thromboembolism (VTE) risk. However, Chinese medical institutions have not yet fully used or improperly used thromboprophylaxis. The effectiveness of information technology applied to thromboprophylaxis in hospitalised patients has been proved in many retrospective studies, lacking of prospective research evidence. METHODS: All hospitalised patients aged >18 years not discharged within 24 hours from 1 September 2020 to 31 May 2021 were prospectively enrolled. Patients were randomly assigned to the control (9890 patients) or intervention group (9895 patients). The control group implemented conventional VTE prevention programmes; the intervention group implemented an Artificial Intelligence Clinical Assistant Decision Support System (AI-CDSS) on the basis of conventional prevention. Intergroup demographics, disease status, hospital length of stay (LOS), VTE risk assessment and VTE prophylaxis were compared using the χ2 test, Fisher's exact test, t-test or Wilcoxon rank-sum test. Univariate and multivariate logistic regressions were used to explore the risk factor of VTE. RESULTS: The control and intervention groups had similar baseline characteristics. The mean age was 58.32±15.41 years, and mean LOS was 7.82±7.07 days. In total, 5027 (25.40%) and 2707 (13.67%) patients were assessed as having intermediate-to-high VTE risk and high bleeding risk, respectively. The incidence of hospital-associated VTE (HA-VTE) was 0.38%, of which 86.84% had deep vein thrombosis. Compared with the control group, the incidence of HA-VTE decreased by 46.00%, mechanical prophylaxis rate increased by 24.00% and intensity of drug use increased by 9.72% in the intervention group. However, AI-CDSS use did not increase the number of clinical diagnostic tests, prophylaxis rate or appropriate prophylaxis rate. CONCLUSIONS: Thromboprophylaxis is inadequate in hospitalised patients with VTE risk. The role of AI-CDSS in VTE risk management is unknown and needs further in-depth study. TRIAL REGISTRATION NUMBER: ChiCTR2000035452.

A retrospective study on the effectiveness of Artificial Intelligence-based Clinical Decision Support System (AI-CDSS) to improve the incidence of hospital-related venous thromboembolism (VTE).

BACKGROUND: Artificial intelligence technology is widely used in the medical industry. Our retrospective study evaluated the effectiveness of an AI-CDSS in improving the incidence of hospital-related VTE and the impact of anticoagulant drug use. METHODS: This study collected relevant data on adult patients over 18 years of age who are not discharged 24 hours, from January to July 2019 and from January to July 2020, the VTE high-risk department of Ruijin Hospital. Before and after using AI-CDSS, the incidence of hospital-related VTE and using anticoagulants were analyzed. RESULTS: Between January to July 2019 and January to July 2020, 3,565 and 4,423 adult patients over 18 years old were hospitalized in our hospital and were designed as a control group and intervention group, respectively (7,988 in total). Both groups had similar baseline characteristics. There were 4,716 (59.03%) male patients, the mean age was 60.43±13.09 years, and the mean stay was 7.56±7.76 days. More than half of the patients (4,605, 57.58%) came from the respiratory. VTE events during hospitalization occurred in 41 patients; overall, 5.13/1,000 (41 episodes in 7,988 patients). Compared with the control group, before implementing AI-CDSS, the rate of VTE during hospitalization was reduced from 5.89/1,000 (21 episodes in 3,565 patients) to 4.75/1,000 patients (20 episodes in 4,423 patients) (relative reduction of 19.35%) in the intervention group. The use rate of anticoagulant drugs was increased from 19.97% (712/3,565) in the control group to 22.88% (1,012/4,423) in intervention group [P<0.01, odds ratio (OR): 1.19, 95 percent confidence interval (95% CI) (1.07-1.32)], (relative 14.57% increase). Poisson's regression results showed that department, age ≥75 years [OR: 3.09, 95% Cl (1.45-6.33)], duration of hospitalization [OR: 1.04, 95% CI (1.03-1.05)], heart failure [OR: 5.13, 95% CI (1.74-13.54)] and renal failure [OR: 3.60, 95% CI (0.90-11.34)] were high-risk factors for VTE events. CONCLUSIONS: Implementing AI-CDSS can help clinicians identify hospitalized patients at increased VTE risk, take effective preventive measures, and improve clinicians' compliance with the American College of Chest Physicians (ACCP) guidelines.

[Case-control study on effects of two approaches on multifidus muscle injuries during lumbar interbody fusion of lumbar vertebrae].

OBJECTIVE: To study differences of multifidus muscle injury between Wiltse intermuscular approach and intramuscular stripping approach in one-level small incision transforaminal lumbar interbody fusion (TLIF) surgery. METHODS: A total of 46 patients with unilateral lumbar degenerative disease underwent small incision TLIF from August 2009 to February 2011 by one group of surgeons at a single institution. The decompression side of all patients adopted intra-muscular stripping approach; for the non-decompression side, 22 patients adopted Wiltse intermuscular approach (group A),and 24 patients adopted intra-muscular striping approach (group B). In group A, there were 13 males and 9 females, ranging in age from 36 to 74 years old,averaged 52.7 +/- 9.2; 1 patient had disease in L3, 4 12 in L4,5 and 9 in L5S1. In group B,there were 11 males and 13 females,ranging in age from 32 tio 72 years old, averaged 51.8 +/- 8.7; two patients had disease in L3,4, 14 in L4,5, and 8 in L5S1. The following data were compared between the 2 groups: surgical time from skin incision to completion of pedicle screw placement, suturation time, blood loss. Clinical effects were evaluated by VAS score pre-operatively, as well as 1, 6 and 12 months post-operatively. At the latest follow-up, all the patients were evaluated by MRI. This enabled the cross-sectional area (CSA) of lean multifidus muscle, and the T2 signal intensity ratio of multifidus to psoas muscle, to be compared at the operative level. RESULTS: There was no obvious difference in suturation time, but less surgical time from skin incision to completion of pedicle screw placement, less blood loss, less postoperative back pain in Wiltse intermuscular approach group. For the comparison between the two groups or paired comparison between sides in the Wiltse group, the reduction of lean CSA and increase in the multifidus:psoas T2 signal intensity ratio were all significant lower in Wiltse intermuscular approach group or side. CONCLUSION: The Wiltse intermuscular approach is an easy way for pedicle screw placement, and caused less paraspinal muscle damage than intra-muscular stripping approach, and had positive effects on less back pain.