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Opioid-based analgesic therapy represents a cornerstone of pain management after surgery. The recent rise in opioid sales and opioid overdoses suggests it is important to maximize the safety of opioid prescribing after surgery. Given that patients may live with other family members in the home, safe storage and appropriate disposal of excess opioids after hospital discharge are necessary to prevent unintended secondary exposures. Identifying characteristics of patients who are likely to be prescribed excess opioids after surgery may enable more targeted prescription practices and safety interventions. Our study aimed to elucidate patient-reported opioid use patterns and modes of home storage of opioids among patients discharged home after Cesarean section (C-section) and thoracic surgery. Specifically, we sought to identify characteristics of patients who reported using about half or more versus less of the opioids prescribed to them for use after hospital discharge. For this cohort study, we developed a survey on quality of analgesia following hospital discharge, amounts of opioids taken relative to the amount prescribed, reasons for not taking all prescribed medications, and storage and disposal methods for leftover opioids. Adult patients, who had C-section or thoracic surgery at a tertiary academic medical center, were given a web-based self-administered survey after discharge. Descriptive statistics (means and standard deviations, proportions) were used to describe the study sample and survey results. Comparisons between patients who reported taking about half or more versus less of the opioids prescribed to them for use after hospital discharge were made using unpaired t-tests, Mann-Whitney tests, and Chi-square tests as appropriate. The majority (53%) of respondents after C-section (N = 30) reported taking either no or very few (less than 5) prescribed opioid pills; 83% reported taking half or less; and 17% of women, reported taking all or nearly all (5 or fewer pills left over) of their opioid prescription. In a cohort of patients after thoracic surgery (n = 31) 45% reported taking either no or very few (5 or less) prescribed opioid pills; 71% reported taking half or less; and 29% of patients reported taking all or nearly all (5 or fewer pills left over) of their opioid prescription. In both cohorts, use of opioids while hospitalized was higher in the group reporting using about half or more of prescribed opioids after discharge. Leftover opioids were stored in an unlocked location in 77% and 73% of cases following C-section and thoracic surgery, respectively. Our findings from surveys in two distinct patient populations at a single academic medical center suggest that current opioid prescribing practices for pain management at hospital discharge following Cesarean section and thoracic surgery may not account for individual patients' analgesic requirements. Excess opioid pills are commonly stored in unsecured locations and represent a potential source for non-medical opioid use and associated morbidity and mortality in patients and their families. Research to develop goal-directed and patient-centered post-discharge opioid prescription practices and encourage opioid safety practices after surgery is needed.

Background Enrollment problems are common among randomized controlled trials conducted in the ICU. However, little is known about actual trial enrollment rates and influential factors. We set out to determine the overall enrollment rate in recent randomized controlled trials (RCTs) of patients with acute respiratory distress syndrome (ARDS), acute lung injury (ALI), or sepsis, and which factors influenced enrollment rate. Methods We conducted a systematic review by searching Pubmed using predefined terms for ARDS/ALI and sepsis to identify individually RCTs published among the seven highest impact general medicine and seven highest impact critical care journals between 2009 and 2019. Cluster randomized trials were excluded. Data were extracted by two independent reviewers using an electronic database management system. We conducted a random-effects meta-analysis of the eligible trials for the primary outcome of enrollment rate by time and site. Results Out of 457 articles identified, 94 trials met inclusion criteria. Trials most commonly evaluated pharmaceutical interventions (53%), were non-industry funded (78%), and required prospective informed consent (81%). The overall mean enrollment rate was 0.83 (95% confidence interval: 0.57–1.21) participants per month per site. Enrollment in ARDS/ALI and sepsis trials were 0.48 (95% CI 0.32–0.70) and 0.98 (95% CI 0.62–1.56) respectively. The enrollment rate was significantly higher for single-center trials (4.86; 95% CI 2.49–9.51) than multicenter trials (0.52; 95% CI 0.41–0.66). Of the 36 trials that enrolled < 95% of the target sample size, 8 (22%) reported slow enrollment as the reason. Conclusions In this systematic review and meta-analysis, recent ARDS/ALI and sepsis clinical trials had an overall enrollment rate of less than 1 participant per site per month. Novel approaches to improve critical care trial enrollment efficiency are needed to facilitate the translation of best evidence into practice.

Patients diagnosed with COVID-19 frequently require mechanical ventilation. Knowledge of laboratory tests associated with the prolonged need for mechanical ventilation may guide resource allocation. We hypothesized that an elevated plasma procalcitonin level (>0.1 ng/ml) would be associated with the duration of invasive mechanical ventilation. Patients diagnosed with COVID-19, who were admitted to any of our health system's hospitals between March 9th-April 20th, 2020 and required invasive mechanical ventilation, were eligible for this observational cohort study. Demographics, comorbidities, components of the Sequential Organ Failure Assessment score, and procalcitonin levels on admission were obtained from the electronic health record. The primary outcome was the duration of mechanical ventilation; secondary outcomes included 28-day mortality and time to intubation. Outcomes were assessed within the first 28 days of admission. Baseline demographics and comorbidities were summarized by descriptive statistics. Univariate comparisons were made using Pearson's chi-square test for binary outcomes and Mann-Whitney U test for continuous outcomes. A multiple linear regression was fitted to assess the association between procalcitonin levels and the duration of mechanical ventilation. Patients with an initial procalcitonin level >0.1 ng/ml required a significantly longer duration of mechanical ventilation than patients with a level of ≤0.1 ng/ml (p = 0.021) in the univariate analysis. There was no significant difference in 28-day mortality or time to intubation between the two groups. After adjusted analysis using multivariable linear regression, the duration of mechanical ventilation was, on average, 5.6 (p = 0.016) days longer in patients with an initial procalcitonin level >0.1 ng/ml. In this cohort of 93 mechanically ventilated COVID-19 patients, we found an association between an initial plasma procalcitonin level >0.1 ng/ml and the duration of mechanical ventilation. These findings may help to identify patients at risk for prolonged mechanical ventilation upon admission.

BackgroundOverprescribing of opioid medications for patients to be used at home after surgery is common. We sought to ascertain important patient and procedural characteristics that are associated with low versus high rates of self-reported utilization of opioids at home, 1–4 weeks after discharge following gastrointestinal surgery.MethodsWe developed a survey consisting of questions from NIH PROMIS tools for pain intensity/interference and queries on postoperative analgesic use. Adult patients completed the survey weekly during the first month after discharge. Using regression procedures we determined the patient and procedure characteristics that predicted high post-discharge opioid use operationalized as 75 mg oral morphine equivalents/50 mg oxycodone reported taken.ResultsThe survey response rate was 86% (201/233). High opioid use was reported by 52.7% of patients (106/201). Median reported intake of opioid pain pills was 7 for week #1 and 0 for weeks #2–4. Combinations of acetaminophen and non-steroidal and anti-inflammatory drugs were used by 8.9%–12.5% of patients after discharge. Following adjustment for significant variables of the univariate analysis, last 24-h in-hospital opioid intake remained as a significant co-variate for post-discharge opioid intake.ConclusionsAfter gastrointestinal surgery, the equivalent of each oxycodone 5 mg tablet taken in the last 24 h before discharge increases the likelihood of taking the equivalent of > 10 oxycodone 5 mg tablets by 5%. Non-opioid analgesia was utilized in less than half of the cases. Maximizing non-opioid analgesic therapy and basing opioid prescriptions on 24-h pre-discharge opioid intake may improve the quality of post-discharge pain management.

In this cohort of 93 mechanically ventilated COVID-19 patients, we found an association between an initial plasma procalcitonin level >0.1 ng/ml and the duration of mechanical ventilation. These findings may help to identify patients at risk for prolonged mechanical ventilation upon admission.

In the United States, alcohol use disorder adversely affects 5.6% of all adults. Excessive alcohol consumption adversely affects organ functions critical for adaptation to stress induced by surgery. Colorectal resection is one of the most common major surgeries in patients at risk for alcohol use disorder. The objective of this study was to assess the impact of alcohol use disorder on hospital outcomes after colectomy using a population-based discharge database. Population-based discharge database. The Premier Healthcare Database was queried for the 603,730 adult patients who underwent colectomy from 2016 to 2019. None. Multiple logistic regressions estimated the associations between in-hospital mortality, length of stay, and hospitalization cost with alcohol use disorder as the primary predictor, adjusting for other substance use disorders, psychotic disorders, depression, other Elixhauser comorbidities, age, payor, race, gender, non-elective surgery, and other unbalanced variables. A discharge code for alcohol use disorder was identified in 2.9% of colectomy patients and the overall in-hospital mortality rate in all sampled colectomy patients was 1.4%. Alcohol use disorder was associated with a significantly increased risk of in-hospital mortality after adjusting for other factors (AOR 1.36, 95% CI 1.24–1.48, p < 0.0001). Alcohol use disorder was also significantly associated with long length of stay (AOR 1.45, 95% CI 1.39–1.52, p < 0.0001) and high hospitalization costs (AOR 1.63, 95% CI 1.56–1.70, p < 0.0001). Alcohol use disorder is associated with an increased risk of in-hospital mortality in patients undergoing colectomy, one of the most common major surgeries. Future research should examine if enhanced efforts to identify patients with alcohol use disorder could enable anesthesiologists to provide worthwhile perioperative interventions for this high-risk population. •Alcohol use disorder occurred in 2.9% of patients undergoing a common surgical procedure – colectomy.•3.7% of colectomy patients with an alcohol use disorder discharge code vs. 1.3% without died in the hospital.•Alcohol use disorder was associated with higher in-hospital mortality, length of stay, and costs in colectomy patients.

Payor presented as all others versus the referent Medicaid, charity, and self-pay categories.Fig. 2 Variable Parameter Estimate (SE) Chi-Sq (DF) P value AOR 95% CI In-Hospital Mortality Intercept −7.68 (0.075) 10,387.69 (1) <0.0001 N/A N/A Alcohol Use Disorder 0.31 (0.045) 45.37 (1) <0.0001 1.36 1.24, 1.48 Substance Use Disorder −0.06 (0.066) 0.88 (1) 0.3475 0.94 0.83, 1.07 Psychotic Disorder 0.22 (0.092) 6.05 (1) 0.0139 1.26 1.05, 1.50 Depression −0.19 (0.033) 32.73 (1) <0.0001 0.83 0.77, 0.88 Age (decades) 0.02 (0.011) 248.07 (1) <0.0001 1.18 1.16, 1.21 Elixhauser, reduced 0.39 (0.004) 7520.64 (1) <0.0001 1.48 1.47, 1.50 Non-elective Surgery 1.41 (0.036) 1535.75 (1) <0.0001 4.09 3.82, 4.40 Woman −0.10 (0.023) 19.65 (1) <0.0001 0.90 0.86, 0.94 Race, black −0.29 (0.037) 61.65 (1) <0.0001 0.75 0.69, 0.80 Race, other 0.06 (0.037) 2.30 (1) 0.1294 1.06 0.98, 1.14 Hispanic −0.18 (0.051) 12.61 (1) 0.0004 0.83 0.75, 0.92 Rural 0.04 (0.036) 1.09 (1) 0.2959 1.04 0.97, 1.11 Teaching Hospital 0.27 (0.023) 131.33 (1) <0.0001 1.31 1.25, 1.37 Payor −0.30 (0.040) 54.50 (1) <0.0001 0.74 0.69, 0.81 LOS (High vs. Low) Intercept −1.12 (0.017) 4569.52 (1) <0.0001 N/A N/A Alcohol Use Disorder 0.49 (0.023) 458.20 (1) <0.0001 1.63 1.56, 1.70 Substance Use Disorder 0.72 (0.028) 676.46 (1) <0.0001 2.06 1.96, 2.18 Psychotic Disorder 0.29 (0.045) 43.12 (1) <0.0001 1.34 1.23, 1.47 Depression 0.09 (0.011) 67.28 (1) <0.0001 1.09 1.07, 1.12 Age (decades) −0.10 (0.003) 1586.75 (1) <0.0001 0.90 0.90, 0.91 Elixhauser, reduced 0.51 (0.002) 60,778.37 (1) <0.0001 1.66 1.66, 1.67 Non-elective Surgery 1.23 (0.007) 32,379.43 (1) <0.0001 3.41 3.37, 3.46 Woman 0.13 (0.007) 441.63 (1) <0.0001 1.14 1.13, 1.15 Race, black −0.17 (0.011) 249.64 (1) <0.0001 0.84 0.83, 0.86 Race, other 0.003 (0.01) 0.07 (1) 0.7885 1.00 0.98, 1.02 Hispanic 0.16 (0.013) 151.90 (1) <0.0001 1.17 1.14, 1.20 Rural −0.21 (0.009) 529.13 (1) <0.0001 0.81 0.80, 0.86 Teaching Hospital 0.43 (0.006) 4637.99 (1) <0.0001 1.54 1.52, 1.56 Payor −0.05 (0.011) 27.03 (1) <0.0001 0.95 0.93, 0.97 Fig. 2 Adjusted odds ratio plot for the associations between independent variables and in-hospital mortality. Abbreviations: AOR, adjusted odds ratio; CI, confidence interval; DF, degrees of freedom; LOS, length of stay; SE, standard error.