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Preventive strategies to reduce clinically significant medication errors (MEs), such as medication review, are often limited by human resources. Identifying high-risk patients to allow for appropriate resource allocation is of the utmost importance. To this end, we developed a predictive model to identify high-risk patients and assessed its impact on clinical decision-making. From March 1st to April 31st 2014, we conducted a prospective cohort study on adult inpatients of a 1,644-bed University Hospital Centre. After a clinical evaluation of identified MEs, we fitted and internally validated a multivariate logistic model predicting their occurrence. Through 5,000 simulated randomized controlled trials, we compared two clinical decision pathways for intervention: one supported by our model and one based on the criterion of age. Among 1,408 patients, 365 (25.9%) experienced at least one clinically significant ME. Eleven variables were identified using multivariable logistic regression and used to build a predictive model which demonstrated fair performance (c-statistic: 0.72). Major predictors were age and number of prescribed drugs. When compared with a decision to treat based on the criterion of age, our model enhanced the interception of potential adverse drug events by 17.5%, with a number needed to treat of 6 patients. We developed and tested a model predicting the occurrence of clinically significant MEs. Preliminary results suggest that its implementation into clinical practice could be used to focus interventions on high-risk patients. This must be confirmed on an independent set of patients and evaluated through a real clinical impact study.

Hip fracture (HF) in older patients is associated with a high six-month mortality rate. Several clinical conditions may affect outcome, including baseline characteristics, co-existing acute illnesses, perioperative factors, and postoperative complications. Our primary objective was to estimate the respective effect of these four domains on six-month mortality after HF. A retrospective observational study using a monocentric cohort of older patients was conducted. All patients ≥ 70 years old admitted to the emergency department for HF and hospitalized in our perioperative geriatric care unit from June 2009 to September 2018 were included. Among 1015 included patients, five (0.5%) were lost to follow-up, and 1010 were retained in the final analysis (mean age 86 ± 6 years). The six-month mortality rate was 14.8%. The six-month attributable mortality estimates were as follows: baseline characteristics (including age, gender, comorbidities, autonomy, type of fracture): 62.4%; co-existing acute illnesses (including acute events present before surgery that could result from the fracture or cause it): 0% (not significantly associated with six-month mortality); perioperative factors (including blood transfusion and delayed surgery): 12.3%; severe postoperative complications: 11.9%. Baseline characteristics explained less than two-thirds of the six-month mortality after HF. Optimizing patients care by improving management of perioperative factors and thus decreasing postoperative complications, could reduce by a maximum of one quarter of the six-month mortality rate after HF.

Background Double-adjustment can be used to remove confounding if imbalance exists after propensity score (PS) matching. However, it is not always possible to include all covariates in adjustment. We aimed to find the optimal imbalance threshold for entering covariates into regression. Methods We conducted a series of Monte Carlo simulations on virtual populations of 5,000 subjects. We performed PS 1:1 nearest-neighbor matching on each sample. We calculated standardized mean differences across groups to detect any remaining imbalance in the matched samples. We examined 25 thresholds (from 0.01 to 0.25, stepwise 0.01) for considering residual imbalance. The treatment effect was estimated using logistic regression that contained only those covariates considered to be unbalanced by these thresholds. Results We showed that regression adjustment could dramatically remove residual confounding bias when it included all of the covariates with a standardized difference greater than 0.10. The additional benefit was negligible when we also adjusted for covariates with less imbalance. We found that the mean squared error of the estimates was minimized under the same conditions. Conclusion If covariate balance is not achieved, we recommend reiterating PS modeling until standardized differences below 0.10 are achieved on most covariates. In case of remaining imbalance, a double adjustment might be worth considering.

In this trial, administering aspirin before surgery and during the early postsurgical period did not affect the rate of death or nonfatal MI but increased the risk of major bleeding. This was true in patients who had not been taking aspirin and in those on a long-term aspirin regimen. Myocardial infarction is the most common major vascular complication that occurs after noncardiac surgery. 1 – 3 Noncardiac surgery is associated with platelet activation, 4 and coronary-artery thrombus may be a mechanism of perioperative myocardial infarction. 5 , 6 Aspirin inhibits platelet aggregation, 7 and the perioperative administration of aspirin may prevent major vascular complications by inhibiting thrombus formation. 8 In a meta-analysis of data from large, randomized trials involving more than 110,000 patients who were not undergoing surgery, the use of aspirin was shown to prevent myocardial infarction and major vascular events. 9 High-dose aspirin has not been shown to be superior to low-dose aspirin in preventing . . .

A prospective cohort study of 13,862 patients showed that among those who underwent isolated coronary-artery bypass grafting or aortic-valve replacement or repair, the threshold high-sensitivity cardiac troponin I level (within 1 day after surgery) associated with an adjusted hazard ratio for death within 30 days of more than 1.00 was 5670 ng per liter — 218 times the upper reference limit.

Causal treatment effects are estimated at the population level in randomized controlled trials, while clinical decision is often to be made at the individual level in practice. We aim to show how clinical prediction models used under a counterfactual framework may help to infer individualized treatment effects. As an illustrative example, we reanalyze the International Stroke Trial. This large, multicenter trial enrolled 19,435 adult patients with suspected acute ischemic stroke from 36 countries, and reported a modest average benefit of aspirin (vs. no aspirin) on a composite outcome of death or dependency at 6 months. We derive and validate multivariable logistic regression models that predict the patient counterfactual risks of outcome with and without aspirin, conditionally on 23 predictors. The counterfactual prediction models display good performance in terms of calibration and discrimination (validation c-statistics: 0.798 and 0.794). Comparing the counterfactual predicted risks on an absolute difference scale, we show that aspirin—despite an average benefit—may increase the risk of death or dependency at 6 months (compared with the control) in a quarter of stroke patients. Counterfactual prediction models could help researchers and clinicians (i) infer individualized treatment effects and (ii) better target patients who may benefit from treatments.

Elderly patients with hip fracture have a 5 to 8 fold increased risk of death during the months following surgery. We tested the hypothesis that early geriatric management of these patients focused on co-morbidities and rehabilitation improved long term mortality. In a cohort study over a 6 year period, we compared patients aged >70 years with hip fracture admitted to orthopedic versus geriatric departments in a time series analysis corresponding to the creation of a dedicated geriatric unit. Co-morbidities were assessed using the Cumulative Illness Rating Scale (CIRS). Each cohort was compared to matched cohorts extracted from a national registry (n = 51,275) to validate the observed results. Main outcome measure was 6-month mortality. We included 131 patients in the orthopedic cohort and 203 in the geriatric cohort. Co-morbidities were more frequent in the geriatric cohort (median CIRS: 8 vs 5, P<0.001). In the geriatric cohort, the proportion of patients who never walked again decreased (6% versus 22%, P<0.001). At 6 months, re-admission (14% versus 29%, P = 0.007) and mortality (15% versus 24%, P = 0.04) were decreased. When co-morbidities were taken into account, the risk ratio of death at 6 months was reduced (0.43, 95%CI 0.25 to 0.73, P = 0.002). Using matched cohorts, the average treatment effects on the treated associated to early geriatric management indicated a reduction in hospital mortality (-63%; 95% CI: -92% to -6%, P = 0.006). Early admission to a dedicated geriatric unit improved 6-month mortality and morbidity in elderly patients with hip fracture.

Background Intensive care unit (ICU) hospitalisations of elderly patients with acute respiratory infection have increased, yet the long-term effects of ICU admission among elderly individuals remain unknown. We examined differences over the 2 years after discharge in mortality, healthcare utilisation and frailty score between elderly survivors of ARI in the ICU and an elderly control population. Methods We used 2009–2017 data from 39 hospital discharge databases. Patients ≥ 80 years old discharged alive from ICU hospitalisation for acute respiratory infection were propensity score-matched with controls (cataract surgery) discharged from the hospital at the same time and adjusted for age, sex and comorbidities present before hospitalisation. We reported 2-year mortality and compared healthcare utilisation and frailty scores in the 2-year periods before and after ICU hospitalisation. Results One thousand two hundred and twenty elderly survivors of acute respiratory infection in the ICU were discharged, and 988 were successfully matched with controls. After discharge, patients had a 10.1-fold [95% CI, 6.1–17.3] higher risk of death at 6 months and 3.6-fold [95% CI, 2.9–4.6] higher risk of death at 2 years compared with controls. They also had a 2-fold increase in both healthcare utilisation and frailty score in the 2 years after hospital discharge, whereas healthcare utilisation and frailty scores among controls were stable before and after hospitalisation. Conclusions We observed a substantially increased rate of death in the years following ICU hospitalisation for elderly patients along with elevated healthcare resource use and accelerated age-associated decline as assessed by frailty score. These findings provide data for better informed goals-of-care discussions and may help target post-ICU discharge services.

By removing systematic differences across treatment groups, simple randomization is assumed to protect against bias. However, random differences may remain if the sample size is insufficiently large. We sought to determine the minimal sample size required to eliminate random differences, thereby allowing an unbiased estimation of the treatment effect. We reanalyzed two published multicenter, large, and simple trials: the International Stroke Trial (IST) and the Coronary Artery Bypass Grafting (CABG) Off- or On-Pump Revascularization Study (CORONARY). We reiterated 1,000 times the analysis originally reported by the investigators in random samples of varying size. We measured the covariates balance across the treatment arms. We estimated the effect of aspirin and heparin on death or dependency at 30 days after stroke (IST), and the effect of off-pump CABG on a composite primary outcome of death, nonfatal stroke, nonfatal myocardial infarction, or new renal failure requiring dialysis at 30 days (CORONARY). In addition, we conducted a series of Monte Carlo simulations of randomized trials to supplement these analyses. Randomization removes random differences between treatment groups when including at least 1,000 participants, thereby resulting in minimal bias in effects estimation. Later, substantial bias is observed. In a short review, we show such an enrollment is achieved in 41.5% of phase 3 trials published in the highest impact medical journals. Conclusions drawn from completely randomized trials enrolling a few participants may not be reliable. In these circumstances, alternatives such as minimization or blocking should be considered for allocating the treatment. •In small trials, simple randomization may not correctly remove covariate imbalance.•In small trials, random covariate imbalance leads to accidental bias.•Including at least 1,000 participants should ensure covariate balance.

Introduction In a randomized controlled trial comparing tight glucose control with a computerized decision support system and conventional protocols ( post hoc analysis), we tested the hypothesis that hypoglycemia is associated with a poor outcome, even when controlling for initial severity. Methods We looked for moderate (2.2 to 3.3 mmol/L) and severe (<2.2 mmol/L) hypoglycemia, multiple hypoglycemic events (n ≥3) and the other main components of glycemic control (mean blood glucose level and blood glucose coefficient of variation (CV)). The primary endpoint was 90-day mortality. We used both a multivariable analysis taking into account only variables observed at admission and a multivariable matching process (greedy matching algorithm; caliper width of 10 −5 digit with no replacement). Results A total of 2,601 patients were analyzed and divided into three groups: no hypoglycemia (n =1,474), moderate hypoglycemia (n =874, 34%) and severe hypoglycemia (n =253, 10%). Patients with moderate or severe hypoglycemia had a poorer prognosis, as shown by a higher mortality rate (36% and 54%, respectively, vs. 28%) and decreased number of treatment-free days. In the multivariable analysis, severe (odds ratio (OR), 1.50; 95% CI, 1.36 to 1.56; P =0.043) and multiple hypoglycemic events (OR, 1.76, 95% CI, 1.31 to 3.37; P <0.001) were significantly associated with mortality, whereas blood glucose CV was not. Using multivariable matching, patients with severe (53% vs. 35%; P <0.001), moderate (33% vs. 27%; P =0.029) and multiple hypoglycemic events (46% vs. 32%, P <0.001) had a higher 90-day mortality. Conclusion In a large cohort of ICU patients, severe hypoglycemia and multiple hypoglycemic events were associated with increased 90-day mortality. Trial registration Clinicaltrials.gov Identifier: NCT01002482 . Registered 26 October 2009.