A risk score model integrating chronic comorbidities and acute events at ICU admission can identify patients at high risk to develop AKI. This risk assessment tool could help clinicians to stratify patients for primary prevention, surveillance and early therapeutic intervention to improve care and outcomes of ICU patients.

Objective: To develop and validate automated electronic note search strategies (automated digital algorithm) to identify Charlson comorbidities. Patients and Methods: The automated digital algorithm was built by a series of programmatic queries applied to an institutional electronic medical record database. The automated digital algorithm was derived from secondary analysis of an observational cohort study of 1447 patients admitted to the intensive care unit from January 1 through December 31, 2006, and validated in an independent cohort of 240 patients. The sensitivity, specificity, and positive and negative predictive values of the automated digital algorithm and International Classification of Diseases, codes were compared with comprehensive medical record review (reference standard) for the Charlson comorbidities. Results: In the derivation cohort, the automated digital algorithm achieved a median sensitivity of 100% (range, 99%-100%) and a median specificity of 99.7% (range, 99%-100%). In the validation cohort, the sensitivity of the automated digital algorithm ranged from 91% to 100%, and the specificity ranged from 98% to 100%. The sensitivity of the ICD-9 codes ranged from 8% for dementia to 100% for leukemia, whereas specificity ranged from 86% for congestive heart failure to 100% for leukemia, dementia, and AIDS. Conclusion: Our results suggest that search strategies that use automated electronic search strategies to extract Charlson comorbidities from the clinical notes contained within the electronic medical record are feasible and reliable. Automated digital algorithm outperformed ICD-9 codes in all the Charlson variables except leukemia, with greater sensitivity, specificity, and positive and negative predictive values.

BACKGROUND:The diagnostic workup of transfusionrelated acute lung injury (TRALI) requires an exclusion of transfusion-associated circulatory overload (TACO). Brain natriuretic peptide (BNP) and N-terminal pro-brain natriuretic (NT-pro-BNP) accurately diagnosed TACO in preliminary studies that did not include patients with TRALI. STUDY DESIGN AND METHODS:In this prospective cohort study, two critical care experts blinded to serum levels of BNP and NT-pro-BNP determined the diagnosis of TRALI, TACO, and possible TRALI based on the consensus conference definitions. The accuracy of BNP and NT-pro-BNP was assessed based on the area under the receiver operating curve (AUC). RESULTS: Of 115 patients who developed acute pulmonary edema after transfusion, 34 were identified with TRALI, 31 with possible TRALI, and 50 with TACO. Median BNP was 375 pg per mL (interquartile range [IQR], 123 to 781 pg/mL) in TRALI, 446 pg per mL (IQR, 128 to 743 pg/mL) in possible TRALI, and 559 pg per mL (IQR, 288 to 1348 pg/mL) in TACO patients (p = 0.038). The NT-pro-BNP levels among patients with TRALI, possible TRALI, and TACO differed significantly with a median value of 1559 pg per mL (IQR, 629 to 5114 pg/mL), 2349 pg/mL (IQR, 919 to 4610 pg/mL), and 5197 pg/mL (IQR, 1695 to 15,714 pg/mL; p = 0.004), respectively. The accuracy of BNP and NT-pro-BNP to diagnose TACO was moderate with an AUC of 0.63 (95% confidence interval [CI], 0.51-0.74) and 0.70 (95% CI, 0.59 to 0.80). CONCLUSIONS: Natriuretic peptides are of limited diagnostic value in a differential diagnosis of pulmonary edema after transfusion in the critically ill patients.

OBJECTIVE:To develop and validate time-efficient automated electronic search strategies for identifying preoperative risk factors for postoperative acute lung injury. PATIENTS AND METHODS:This secondary analysis of a prospective cohort study included 249 patients undergoing high-risk surgery between November 1, 2005, and August 31, 2006. Two independent data-extraction strategies were compared. The first strategy used a manual review of medical records and the second a Webbased query-building tool. Web-based searches were derived and refined in a derivation cohort of 83 patients and subsequently validated in an independent cohort of 166 patients. Agreement between the 2 search strategies was assessed with percent agreement and Cohen κ statistics. RESULTS:Cohen κ statistics ranged from 0.34 (95% confidence interval, 0.00-0.86) for amiodarone to 0.85 for cirrhosis (95% confidence interval, 0.57-1.00). Agreement between manual and automated electronic data extraction was almost complete for 3 variables (diabetes mellitus, cirrhosis, H 2 -receptor antagonists), substantial for 3 (chronic obstructive pulmonary disease, proton pump inhibitors, statins), moderate for gastroesophageal reflux disease, and fair for 2 variables (restrictive lung disease and amiodarone). Automated electronic queries outperformed manual data collection in terms of sensitivities (median, 100% [range, 77%-100%] vs median, 87% [range, 0%-100%]). The specificities were uniformly high (≥96%) for both search strategies.CONCLUSION: Automated electronic query building is an iterative process that ultimately results in accurate, highly efficient data extraction. These strategies may be useful for both clinicians and researchers when determining the risk of time-sensitive conditions such as postoperative acute lung injury. Mayo Clin © 2011 Mayo Foundation for Medical Education and Research For editorial comment, see page 373A cute lung injury (ALI) is a devastating postoperative respiratory complication and a leading cause of postoperative respiratory failure, 1-3 with a mortality rate of up to 45% in certain surgical populations.4,5 Moreover, treatment options are limited once the condition is fully established. Earlier identification of at-risk populations may allow the implementation of effective ALI prevention strategies. Recognizing that numerous baseline factors can modify a patient's response to illness or injury and the likelihood of developing ALI, we recently developed an ALI risk prediction model for mixed medical and surgical populations. 6,7 This score assigns points both for conditions that predispose patients to ALI (eg, shock, aspiration, sepsis, pancreatitis, pneumonia, high-risk surgery, high-risk trauma) and ALI-modifying factors (eg, sex, excess alcohol use, obesity, chemotherapy, diabetes mellitus [DM], smoking) at the time of hospital admission. We have shown the cumulative score to be a reliable predictor of the risk of developing ALI during hospitalization.A key remaining limitation to the early identification of patients at h...

BACKGROUND:Th e surviving sepsis guidelines recommend early aggressive fl uid resuscitation within 6 h of sepsis onset. Although rapid fl uid administration may off er benefi t, studies on the timing of resuscitation are lacking. We hypothesized that there is an association between quicker, adequate fl uid resuscitation and patient outcome from sepsis onset time.

IntroductionDead-space fraction (Vd/Vt) has been shown to be a powerful predictor of mortality in acute lung injury (ALI) patients. The measurement of Vd/Vt is based on the analysis of expired CO2 which is not a part of standard practice thus limiting widespread clinical application of this method. The objective of this study was to determine prognostic value of Vd/Vt estimated from routinely collected pulmonary variables.MethodsSecondary analysis of the original data from two prospective studies of ALI patients. Estimated Vd/Vt was calculated using the rearranged alveolar gas equation: Vd/Vt=1−[(0.86×V˙CO2est)/(VE×PaCO2)] where V˙CO2est is the estimated CO2 production calculated from the Harris Benedict equation, minute ventilation (VE) is obtained from the ventilator rate and expired tidal volume and PaCO2 from arterial gas analysis. Logistic regression models were created to determine the prognostic value of estimated Vd/Vt.ResultsOne hundred and nine patients in Mayo Clinic validation cohort and 1896 patients in ARDS-net cohort demonstrated an increase in percent mortality for every 10% increase in Vd/Vt in a dose response fashion. After adjustment for non-pulmonary and pulmonary prognostic variables, both day 1 (adjusted odds ratio-OR = 1.07, 95%CI 1.03 to 1.13) and day 3 (OR = 1.12, 95% CI 1.06 to 1.18) estimated dead-space fraction predicted hospital mortality.ConclusionsElevated estimated Vd/Vt predicts mortality in ALI patients in a dose response manner. A modified alveolar gas equation may be of clinical value for a rapid bedside estimation of Vd/Vt, utilizing routinely collected clinical data.

IntroductionDyschloremia is common in critically ill patients, although its impact has not been well studied. We investigated the epidemiology of dyschloremia and its associations with the incidence of acute kidney injury and other intensive care unit outcomes.Material and MethodsThis is a single-center, retrospective cohort study at Mayo Clinic Hospital—Rochester. All adult patients admitted to intensive care units from January 1st, 2006, through December 30th, 2012 were included. Patients with known acute kidney injury and chronic kidney disease stage 5 before intensive care unit admission were excluded. We evaluated the association of dyschloremia with ICU outcomes, after adjustments for the effect of age, gender, Charlson comorbidity index and severity of illness score.ResultsA total of 6,025 patients were enrolled in the final analysis following the implementation of eligibility criteria. From the cohort, 1,970 patients (33%) developed acute kidney injury. Of the total patients enrolled, 4,174 had a baseline serum chloride. In this group, 1,530 (37%) had hypochloremia, and 257 (6%) were hyperchloremic. The incidence of acute kidney injury was higher in hypochloremic and hyperchloremic patients compared to those with a normal serum chloride level (43% vs.30% and 34% vs. 30%, respectively; P < .001). Baseline serum chloride was lower in the acute kidney injury group vs. the non-acute kidney injury group [100 mmol/L (96–104) vs. 102 mmol/L (98–105), P < .0001]. In a multivariable logistic regression model, baseline serum chloride of ≤94 mmol/L found to be independently associated with the risk of acute kidney injury (OR 1.7, 95% CI 1.1–2.6; P = .01).DiscussionDyschloremia is common in critically ill patients, and severe hypochloremia is independently associated with an increased risk of development of acute kidney injury.

It is imperative to provide a more uniform method to improve the validity of prevalence studies on multimorbidity. However, the status of prevalence studies on multimorbidity of chronic disease is still yet to be confirmed in China. The objective of the present systematic review was to evaluate the variance across prevalence studies and to explore possible explanations for variations in China. Published literature was obtained from four databases. The studies that described the prevalence of multimorbidity on chronic disease based on the general population were considered. We assessed the risk of bias by a preplanned checklist referring to STROBE (Strengthening the Reporting of Observational Studies in Epidemiology). The heterogeneity among eligible studies was estimated by I(2) statistic and P-value using MetaAnalyst software. Nine studies were eligible for this systematic review. The prevalence of multimorbidity among the population aged 60 years or more ranged from 6.4% (95% CI 5.1-8.0) to 76.5% (95%CI 73.6-79.2). However, just two of nine studies could be judged as having a low risk of bias. It was shown that key items introducing the risk of bias included inconsistent sampling method, lacking of uniform measure indices and data source based on self-report. Heterogeneity test showed I(2)  = 50% (P < 0.001), which showed there was substantial variation among individual studies. Therefore, only a narrative summary rather than meta-analysis was carried out. Marked methodology heterogeneity exists among prevalence studies on multimorbidity. Suggested methodological aspects that should be considered in future studies include sampling method, measure indices of multimorbidity and data source.