Linda M. Peelen

Effects of the introduction of the WHO "Surgical Safety Checklist" on in-hospital mortality: a cohort study.

Objective:To evaluate the effect of implementation of the WHOs Surgical Safety Checklist on mortality and to determine to what extent the potential effect was related to checklist compliance. Background:Marked reductions in postoperative complications after implementation of a surgical checklist have been reported. As compliance to the checklists was reported to be incomplete, it remains unclear whether the benefits obtained were through actual completion of a checklist or from an increase in overall awareness of patient safety issues. Methods:This retrospective cohort study included 25,513 adult patients undergoing non-day case surgery in a tertiary university hospital. Hospital administrative data and electronic patient records were used to obtain data. In-hospital mortality within 30 days after surgery was the main outcome and effect estimates were adjusted for patient characteristics, surgical specialty and comorbidity. Results:After checklist implementation, crude mortality decreased from 3.13% to 2.85% (P = 0.19). After adjustment for baseline differences, mortality was significantly decreased after checklist implementation (odds ratio [OR] 0.85; 95% CI, 0.73–0.98). This effect was strongly related to checklist compliance: the OR for the association between full checklist completion and outcome was 0.44 (95% CI, 0.28–0.70), compared to 1.09 (95% CI, 0.78–1.52) and 1.16 (95% CI, 0.86–1.56) for partial or noncompliance, respectively. Conclusions:Implementation of the WHO Surgical Checklist reduced in-hospital 30-day mortality. Although the impact on outcome was smaller than previously reported, the effect depended crucially upon checklist compliance.

Pain intensity on the first day after surgery: a prospective cohort study comparing 179 surgical procedures.

Background:Severe pain after surgery remains a major problem, occurring in 20–40% of patients. Despite numerous published studies, the degree of pain following many types of surgery in everyday clinical practice is unknown. To improve postoperative pain therapy and develop procedure-specific, optimized pain-treatment protocols, types of surgery that may result in severe postoperative pain in everyday practice must first be identified. Methods:This study considered 115,775 patients from 578 surgical wards in 105 German hospitals. A total of 70,764 patients met the inclusion criteria. On the first postoperative day, patients were asked to rate their worst pain intensity since surgery (numeric rating scale, 0–10). All surgical procedures were assigned to 529 well-defined groups. When a group contained fewer than 20 patients, the data were excluded from analysis. Finally, 50,523 patients from 179 surgical groups were compared. Results:The 40 procedures with the highest pain scores (median numeric rating scale, 6–7) included 22 orthopedic/trauma procedures on the extremities. Patients reported high pain scores after many “minor” surgical procedures, including appendectomy, cholecystectomy, hemorrhoidectomy, and tonsillectomy, which ranked among the 25 procedures with highest pain intensities. A number of “major” abdominal surgeries resulted in comparatively low pain scores, often because of sufficient epidural analgesia. Conclusions:Several common minor- to medium-level surgical procedures, including some with laparoscopic approaches, resulted in unexpectedly high levels of postoperative pain. To reduce the number of patients suffering from severe pain, patients undergoing so-called minor surgery should be monitored more closely, and postsurgical pain treatment needs to comply with existing procedure-specific pain-treatment recommendations.

Behavior and development in children and age at the time of first anesthetic exposure.

Background:Several experimental studies have suggested that early exposure to anesthetic agents, i.e., before completion of synaptogenesis, can result in widespread apoptotic neuronal degeneration and late cognitive impairment, but human data are lacking. The authors performed a retrospective pilot study to test the feasibility and calculate sample sizes for a larger epidemiologic study of disturbed neurobehavioral development as a function of age at the time of first anesthetic exposure. Pediatric urological procedures were selected because the timing of surgery depends mainly on the age at which a diagnosis is made. Methods:Neurobehavioral development was assessed using the validated 120-item parental Child Behavior CheckList/4–18 in 314 children who were operated for pediatric urological procedures between the ages of 0 and 6 yr. Results:Of 243 questionnaires returned, the total problem score was clinically deviant in 41 (23%) of children aged less than 24 months at the time of first surgery and 13 (20%) aged greater than 24 months. Crude and adjusted odds ratios for a clinically deviant Child Behavior CheckList/4–18 score increased with younger age at the time of surgery, but the confidence intervals were very wide. Adjusted odds ratio was 1.38 (0.59–3.22) when operated at age less than 6 months, 1.19 (0.45–3.18) when operated between 6 and 12 months of age, and 1.20 (0.45–3.20) when operated between 12 and 24 months (using operated at greater than 24 months of age as reference category). A properly powered cohort study would require at least 2,268 children. Conclusions:Children undergoing urologic surgery at age less than 24 months showed more behavioral disturbances than children in whom surgery was performed after age 2 yr, although the results were not statistically significant. To confirm or refute an effect of anesthesia on cognitive development, at least 2,268 children need to be studied. With retrospective study designs, residual confounding remains an issue that can only be solved in prospective randomized studies.

Association between administered oxygen, arterial partial oxygen pressure and mortality in mechanically ventilated intensive care unit patients

IntroductionThe aim of this study was to investigate whether in-hospital mortality was associated with the administered fraction of oxygen in inspired air (FiO2) and achieved arterial partial pressure of oxygen (PaO2).MethodsThis was a retrospective, observational study on data from the first 24 h after admission from 36,307 consecutive patients admitted to 50 Dutch intensive care units (ICUs) and treated with mechanical ventilation. Oxygenation data from all admission days were analysed in a subset of 3,322 patients in 5 ICUs.ResultsMean PaO2 and FiO2 in the first 24 h after ICU admission were 13.2 kPa (standard deviation (SD) 6.5) and 50% (SD 20%) respectively. Mean PaO2 and FiO2 from all admission days were 12.4 kPa (SD 5.5) and 53% (SD 18). Focusing on oxygenation in the first 24 h of admission, in-hospital mortality was shown to be linearly related to FiO2 value and had a U-shaped relationship with PaO2 (both lower and higher PaO2 values were associated with a higher mortality), independent of each other and of Simplified Acute Physiology Score (SAPS) II, age, admission type, reduced Glasgow Coma Scale (GCS) score, and individual ICU. Focusing on the entire ICU stay, in-hospital mortality was independently associated with mean FiO2 during ICU stay and with the lower two quintiles of mean PaO2 value during ICU stay.ConclusionsActually achieved PaO2 values in ICU patients in The Netherlands are higher than generally recommended in the literature. High FiO2, and both low PaO2 and high PaO2 in the first 24 h after admission are independently associated with in-hospital mortality in ICU patients. Future research should study whether this association is causal or merely a reflection of differences in severity of illness insufficiently corrected for in the multivariate analysis.

Prediction models for risk of developing type 2 diabetes: systematic literature search and independent external validation study

Objective To identify existing prediction models for the risk of development of type 2 diabetes and to externally validate them in a large independent cohort. Data sources Systematic search of English, German, and Dutch literature in PubMed until February 2011 to identify prediction models for diabetes. Design Performance of the models was assessed in terms of discrimination (C statistic) and calibration (calibration plots and Hosmer-Lemeshow test).The validation study was a prospective cohort study, with a case cohort study in a random subcohort. Setting Models were applied to the Dutch cohort of the European Prospective Investigation into Cancer and Nutrition cohort study (EPIC-NL). Participants 38 379 people aged 20-70 with no diabetes at baseline, 2506 of whom made up the random subcohort. Outcome measure Incident type 2 diabetes. Results The review identified 16 studies containing 25 prediction models. We considered 12 models as basic because they were based on variables that can be assessed non-invasively and 13 models as extended because they additionally included conventional biomarkers such as glucose concentration. During a median follow-up of 10.2 years there were 924 cases in the full EPIC-NL cohort and 79 in the random subcohort. The C statistic for the basic models ranged from 0.74 (95% confidence interval 0.73 to 0.75) to 0.84 (0.82 to 0.85) for risk at 7.5 years. For prediction models including biomarkers the C statistic ranged from 0.81 (0.80 to 0.83) to 0.93 (0.92 to 0.94). Most prediction models overestimated the observed risk of diabetes, particularly at higher observed risks. After adjustment for differences in incidence of diabetes, calibration improved considerably. Conclusions Most basic prediction models can identify people at high risk of developing diabetes in a time frame of five to 10 years. Models including biomarkers classified cases slightly better than basic ones. Most models overestimated the actual risk of diabetes. Existing prediction models therefore perform well to identify those at high risk, but cannot sufficiently quantify actual risk of future diabetes.

Prediction models for the risk of cardiovascular disease in patients with type 2 diabetes: a systematic review

Context A recent overview of all CVD models applicable to diabetes patients is not available. Objective To review the primary prevention studies that focused on the development, validation and impact assessment of a cardiovascular risk model, scores or rules that can be applied to patients with type 2 diabetes. Design Systematic review. Data sources Medline was searched from 1966 to 1 April 2011. Study selection A study was eligible when it described the development, validation or impact assessment of a model that was constructed to predict the occurrence of cardiovascular disease in people with type 2 diabetes, or when the model was designed for use in the general population but included diabetes as a predictor. Data extraction A standardized form was sued to extract all data of the CVD models. Results 45 prediction models were identified, of which 12 were specifically developed for patients with type 2 diabetes. Only 31% of the risk scores has been externally validated in a diabetes population, with an area under the curve ranging from 0.61 to 0.86 and 0.59 to 0.80 for models developed in a diabetes population and in the general population, respectively. Only one risk score has been studied for its effect on patient management and outcomes. 10% of the risk scores are advocated in national diabetes guidelines. Conclusion Many cardiovascular risk scores are available that can be applied to patients with type 2 diabetes. A minority of these risk scores has been validated and tested for its predictive accuracy, with only a few showing a discriminative value of ≥0.80. The impact of applying these risk scores in clinical practice is almost completely unknown, but their use is recommended in various national guidelines.

A systematic review of risk factors for delirium in the ICU.

Objective:Although numerous risk factors for delirium in the ICU have been proposed, the strength of evidence supporting each risk factor remains unclear. This study systematically identifies risk factors for delirium in critically ill adults where current evidence is strongest. Data Sources:CINAHL, EMBASE, MEDLINE, the Cochrane Central Register for Controlled Trials, and the Cochrane Database of Systematic Reviews. Study Selection:Studies published from 2000 to February 2013 that evaluated critically ill adults, not undergoing cardiac surgery, for delirium, and used either multivariable analysis or randomization to evaluate variables as potential risk factors for delirium. Data Extraction:Data were abstracted in duplicate, and quality was scored using Scottish Intercollegiate Guidelines Network checklists (i.e., high, acceptable, and low). Using a best-evidence synthesis each variable was evaluated using 3 criteria: the number of studies investigating it, the quality of these studies, and whether the direction of association was consistent across the studies. Strengths of association were not summarized. Strength of evidence was defined as strong (consistent findings in ≥2 high quality studies), moderate (consistent findings in 1 high quality study and ≥1 acceptable quality studies), inconclusive (inconsistent findings or 1 high quality study or consistent findings in only acceptable quality/low quality studies) or no evidence available. Data Synthesis:Among 33 studies included, 70% were high quality. There was strong evidence that age, dementia, hypertension, pre-ICU emergency surgery or trauma, Acute Physiology and Chronic Health Evaluation II score, mechanical ventilation, metabolic acidosis, delirium on the prior day, and coma are risk factors for delirium, that gender is not associated with delirium, and that use of dexmedetomidine is associated with a lower delirium prevalence. There is moderate evidence that multiple organ failure is a risk factor for delirium. Conclusions:Only 11 putative risk factors for delirium are supported by either strong or moderate level of evidence. These factors should be considered when designing delirium prevention strategies or controlling for confounding in future etiologic studies.

Prediction models for cardiovascular disease risk in the general population: systematic review

Objective To provide an overview of prediction models for risk of cardiovascular disease (CVD) in the general population. Design Systematic review. Data sources Medline and Embase until June 2013. Eligibility criteria for study selection Studies describing the development or external validation of a multivariable model for predicting CVD risk in the general population. Results 9965 references were screened, of which 212 articles were included in the review, describing the development of 363 prediction models and 473 external validations. Most models were developed in Europe (n=167, 46%), predicted risk of fatal or non-fatal coronary heart disease (n=118, 33%) over a 10 year period (n=209, 58%). The most common predictors were smoking (n=325, 90%) and age (n=321, 88%), and most models were sex specific (n=250, 69%). Substantial heterogeneity in predictor and outcome definitions was observed between models, and important clinical and methodological information were often missing. The prediction horizon was not specified for 49 models (13%), and for 92 (25%) crucial information was missing to enable the model to be used for individual risk prediction. Only 132 developed models (36%) were externally validated and only 70 (19%) by independent investigators. Model performance was heterogeneous and measures such as discrimination and calibration were reported for only 65% and 58% of the external validations, respectively. Conclusions There is an excess of models predicting incident CVD in the general population. The usefulness of most of the models remains unclear owing to methodological shortcomings, incomplete presentation, and lack of external validation and model impact studies. Rather than developing yet another similar CVD risk prediction model, in this era of large datasets, future research should focus on externally validating and comparing head-to-head promising CVD risk models that already exist, on tailoring or even combining these models to local settings, and investigating whether these models can be extended by addition of new predictors.

Procedure-specific risk factor analysis for the development of severe postoperative pain.

Background: Many studies have analyzed risk factors for the development of severe postoperative pain with contradictory results. To date, the association of risk factors with postoperative pain intensity among different surgical procedures has not been studied and compared. Methods: The authors selected precisely defined surgical groups (at least 150 patients each) from prospectively collected perioperative data from 105 German hospitals (2004–2010). The association of age, sex, and preoperative chronic pain intensity with worst postoperative pain intensity was studied with multiple linear and logistic regression analyses. Pooled data of the selected surgeries were studied with random-effect analysis. Results: Thirty surgical procedures with a total number of 22,963 patients were compared. In each surgical procedure, preoperative chronic pain intensity and younger age were associated with higher postoperative pain intensity. A linear decline of postoperative pain with age was found. Females reported more severe pain in 21 of 23 surgeries. Analysis of pooled surgical groups indicated that postoperative pain decreased by 0.28 points (95% CI, 0.26 to 0.31) on the numeric rating scale (0 to 10) per decade age increase and postoperative pain increased by 0.14 points (95% CI, 0.13 to 0.15) for each higher score on the preoperative chronic pain scale. Females reported 0.29 points (95% CI, 0.22 to 0.37) higher pain intensity. Conclusions: Independent of the type and extent of surgery, preoperative chronic pain and younger age were associated with higher postoperative pain. Females consistently reported slightly higher pain scores regardless of the type of surgery. The clinical significance of this small sex difference has to be analyzed in future studies.

Prediction Models for Prolonged Intensive Care Unit Stay After Cardiac Surgery Systematic Review and Validation Study

Background— Several models have been developed to predict prolonged stay in the intensive care unit (ICU) after cardiac surgery. However, no extensive quantitative validation of these models has yet been conducted. This study sought to identify and validate existing prediction models for prolonged ICU length of stay after cardiac surgery. Methods and Results— After a systematic review of the literature, the identified models were applied on a large registry database comprising 11 395 cardiac surgical interventions. The probabilities of prolonged ICU length of stay based on the models were compared with the actual outcome to assess the discrimination and calibration performance of the models. Literature review identified 20 models, of which 14 could be included. Of the 6 models for the general cardiac surgery population, the Parsonnet model showed the best discrimination (area under the receiver operating characteristic curve=0.75 [95% confidence interval, 0.73 to 0.76]), followed by the European system for cardiac operative risk evaluation (EuroSCORE) (0.71 [0.70 to 0.72]) and a model by Huijskes and colleagues (0.71 [0.70 to 0.73]). Most of the models showed good calibration. Conclusions— In this validation of prediction models for prolonged ICU length of stay, 2 widely implemented models (Parsonnet, EuroSCORE), although originally designed for prediction of mortality, were superior in identifying patients with prolonged ICU length of stay.