The Impact of Deep Vein Thrombosis on Outcomes in Critically Ill Patients: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Abstract Background: The clinical consequences of Deep Vein Thrombosis (DVT) have the potential to be serious yet are frequently unrecognized in the Intensive Care Unit (ICU). We hypothesized that both undetected and clinically evident VTE would affect the prognosis of critically ill patients Purpose: To systematically review whether a diagnosis of DVT in critically ill patients affects clinically important outcomes including length of stay, duration of mechanical ventilation and mortality. Material and Methods: Data sources used were the MEDLINE, EMBASE and PUBMED databases. Studies selected evaluated one or more of the following outcomes: duration of patient stay in hospital and in ICU, hospital and ICU mortality, and duration of mechanical ventilation. Two investigators independently extracted and reviewed data from each study; including study and patient characteristics and outcomes. Statistical heterogeneity was evaluated using the I2 statistic; Cohen’s Kappa for inter-rater agreement was used to assess inter-rater reliability. Data was pooled using the Mantel-Haenszel method and a random effects model using Review Manager. Results: Five studies were included in the systematic review. Patients diagnosed with DVT compared to those without DVT had increased ICU and hospital stay (7.3 days (95% confidence interval [CI] 1.4 to 13.2; P= 0.02) and 16.5 days (95% CI 1.51 to 30.59; P= 0.03), respectively. Duration of mechanical ventilation was increased by 3.41 days (95 % CI −1.12 to 7.94; P=0.14). Patients diagnosed with DVT also had increased relative risk (RR) for ICU mortality of 9.19 (95% CI 1.07 to 78.65, P=0.04) and a trend towards increased hospital mortality (RR 14.32 [95% CI 0.59 to 347.96, P = 0.10]). Conclusions: A diagnosis of DVT upon ICU admission appears to affect clinically important outcomes including length of ICU and hospital stay and ICU mortality. Further research involving larger prospective study designs are warranted. Outcomes Study Duration of mechanical ventilation in days (DVT/NO DVT) Hospitalization length In days (DVT/NO DVT) ICU Stay In days (DVT/NO DVT) Hospital mortality rate (DVT/NO DVT) n (%) ICU mortality rate (DVT, n/NO DVT, n) Legend PEPP: positive end-expiratory pressur * IQR ** median “ [95%CI]) ^ Necessity for ventilation measured by PEEP ≥10: DVT/no DVT: 11 (42%)/37 (21%) Ibrahim 2002 18.9±19.7/14.6±12.9M p=0.310 31.4±21.7/27.5± 18.2 p=0.375 18.6±14.6/15.9±1.04 p=0.388 8.9 (34.6%)/26.8(32.1) p=0.815 n/a Velmahos 1998 Not given. ^ 49±32/31±24, p=< 0.05 34±31/19±18, p=<0.05 n/a 31%,8.06/18%,31.2 Major 2003 n/a n/a n/a n/a 17%, 2/2%, 15 p=0.03 Patel 2005 n/a 26** (14,49)*/− 6** (3,15)*/− 70** (28.5%) [22.8–34.1])″/− 16.7%,41 [12.0- 21.3]″/− Cook 2005 9** (4,25)*/6 (3,13)* p=0.03 51** (24,73)*/23 ** (12,47)* p=<0.001 17.5** (8.5, 30.5)*/9** (5,17)* 17 (53.1%)/85 (37.4%) p=0.04 -, 8 **/−, 62** p=0.78
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.032 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".