Predicting organ functioning with and without blood transfusion in critically ill patients with anemia.

BACKGROUND: To develop a model for the prediction of the (most likely) effect of red blood cell (RBC) transfusion on subsequent organ functioning in nonbleeding critically ill patients with hemoglobin concentrations between 6 and 9 g/dL. STUDY DESIGN AND METHODS: We conducted a retrospective cohort study using electronic health care data of nonbleeding patients admitted between November 2004 and May 2016 at the intensive care unit (ICU) of the Leiden University Medical Center, The Netherlands. We analyzed the associations between transfusion (yes/no) and next-day SOFA scores (Sequential Organ Failure Assessment-as a measure for organ functioning) for all observed combinations of hemoglobin values (between 6 and 9 g/dL) and concurrent clinical variables. RESULTS: Data of 6425 ICU admission of 5756 critically ill patients with 28,702 hemoglobin values between 6 and 9 g/dL (transfusion decision moments) of which 22.1% were followed by a transfusion were analyzed. The adjusted average difference between the next-day SOFA score of transfused versus not-transfused patients was 0.08 (95% confidence interval [CI] -0.03 to 0.18). At singular transfusion decision moments, the score predicted a beneficial effect of transfusion on next-day SOFA score for some subgroups and medical conditions and a harmful effect in other occasions. CONCLUSIONS: Among these critically ill patients with hemoglobin concentrations between 6 and 9 g/dL the population average effect of transfusion on the next SOFA score was negligible. Further, our results support caution in clinical decision-making regarding transfusion of critical ill, nonbleeding ICU patients.

Multiple testing: when is many too much?

In almost all medical research, more than a single hypothesis is being tested or more than a single relation is being estimated. Testing multiple hypotheses increases the risk of drawing a false-positive conclusion. We briefly discuss this phenomenon, which is often called multiple testing. Also, methods to mitigate the risk of false-positive conclusions are discussed.

Mortality after Delay of Adequate Empiric Antimicrobial Treatment of Bloodstream Infection.

BACKGROUND: Timely empiric antimicrobial therapy is one of the cornerstones of the management of suspected bloodstream infection (BSI). However, studies about the effects of empiric therapy on mortality have reported inconsistent results. The objective of this study was to estimate the effect of delay of appropriate empiric therapy on early mortality in patients with BSI. Methods: Data for the propensity score matching (PSM) study were obtained from a cohort of patients with BSI. Inadequate empiric treatment was defined as in vitro resistance to the antimicrobial regimen administered <6 h after blood cultures were taken. The primary outcome measure was 14-day mortality. Thirty-day mortality and median length of stay (LOS) were secondary outcomes. PSM was applied to control for confounding. Results: Of a total of 893 included patients with BSI, 35.7% received inadequate initial empiric treatment. In the PSM cohort (n = 334), 14-day mortality was 9.6% for inadequate antibiotic treatment, compared to. 10.2% in adequate empiric treatment (p = 0.85). No prolonged median LOS was observed in patients who initially received inadequate therapy (10.5 vs. 10.7 days, p = 0.89). Conclusions: In this study, we found no clear effect of inadequate empirical treatment on mortality in a low-risk BSI population. The importance of early empiric therapy compared to other determinants, may be limited. This may not apply for specific subpopulations, e.g., patients with sepsis.

Statin-induced LDL cholesterol response and type 2 diabetes: a bidirectional two-sample Mendelian randomization study.

It remains unclear whether the increased risk of new-onset type 2 diabetes (T2D) seen in statin users is due to low LDL-C concentrations, or due to the statin-induced proportional change in LDL-C. In addition, genetic instruments have not been proposed before to examine whether liability to T2D might cause greater proportional statin-induced LDL-C lowering. Using summary-level statistics from the Genomic Investigation of Statin Therapy (GIST, nmax = 40,914) and DIAGRAM (nmax = 159,208) consortia, we found a positive genetic correlation between LDL-C statin response and T2D using LD score regression (rgenetic = 0.36, s.e. = 0.13). However, mendelian randomization analyses did not provide support for statin response having a causal effect on T2D risk (OR 1.00 (95% CI: 0.97, 1.03) per 10% increase in statin response), nor that liability to T2D has a causal effect on statin-induced LDL-C response (0.20% increase in response (95% CI: -0.40, 0.80) per doubling of odds of liability to T2D). Although we found no evidence to suggest that proportional statin response influences T2D risk, a definitive assessment should be made in populations comprised exclusively of statin users, as the presence of nonstatin users in the DIAGRAM dataset may have substantially diluted our effect estimate.

Comparing Methods for Measurement Error Detection in Serial 24-h Hormonal Data.

Measurement errors commonly occur in 24-h hormonal data and may affect the outcomes of such studies. Measurement errors often appear as outliers in such data sets; however, no well-established method is available for their automatic detection. In this study, we aimed to compare performances of different methods for outlier detection in hormonal serial data. Hormones (glucose, insulin, thyroid-stimulating hormone, cortisol, and growth hormone) were measured in blood sampled every 10 min for 24 h in 38 participants of the Leiden Longevity Study. Four methods for detecting outliers were compared: (1) eyeballing, (2) Tukey's fences, (3) stepwise approach, and (4) the expectation-maximization (EM) algorithm. Eyeballing detects outliers based on experts' knowledge, and the stepwise approach incorporates physiological knowledge with a statistical algorithm. Tukey's fences and the EM algorithm are data-driven methods, using interquartile range and a mathematical algorithm to identify the underlying distribution, respectively. The performance of the methods was evaluated based on the number of outliers detected and the change in statistical outcomes after removing detected outliers. Eyeballing resulted in the lowest number of outliers detected (1.0% of all data points), followed by Tukey's fences (2.3%), the stepwise approach (2.7%), and the EM algorithm (11.0%). In all methods, the mean hormone levels did not change materially after removing outliers. However, their minima were affected by outlier removal. Although removing outliers affected the correlation between glucose and insulin on the individual level, when averaged over all participants, none of the 4 methods influenced the correlation. Based on our results, the EM algorithm is not recommended given the high number of outliers detected, even where data points are physiologically plausible. Since Tukey's fences is not suitable for all types of data and eyeballing is time-consuming, we recommend the stepwise approach for outlier detection, which combines physiological knowledge and an automated process.

Prediction of prolonged treatment course for depressive and anxiety disorders in an outpatient setting: The Leiden routine outcome monitoring study.

OBJECTIVE: The aim of this study was to improve clinical identification of patients with a prolonged treatment course for depressive and anxiety disorders early in treatment. METHOD: We conducted a cohort study in 1.225 adult patients with a depressive or anxiety disorders in psychiatric specialty care setting between 2007 and 2011, with at least two Brief Symptom Inventory (BSI) assessments within 6 months. With logistic regression, we modelled baseline age, gender, ethnicity, education, marital status, housing situation, employment status, psychiatric comorbidity and both baseline and 1st follow-up BSI scores to predict prolonged treatment course (>2 years). Based on the regression coefficients, we present an easy to use risk prediction score. RESULTS: BSI at 1st follow-up proved to be a strong predictor for both depressive and anxiety disorders (OR = 2.17 (CI95% 1.73-2.74); OR = 2.52 (CI95% 1.86-3.23)). The final risk prediction score included BSI 1st follow-up and comorbid axis II disorder for depressive disorder, for anxiety disorders BSI 1st follow-up and age were included. For depressive disorders, for 28% of the patients with the highest scores, the positive predictive value for a prolonged treatment course was60% (sensitivity 0.38, specificity 0.81). For anxiety disorders, for 35% of the patients with the highest scores, the positive predictive value for a prolonged treatment course was 52% (sensitivity 0.55, specificity 0.75). CONCLUSIONS: A high level of symptoms at 2-6 months of follow-up is a strong predictor for prolonged treatment course. This facilitates early identification of patients at risk of a prolonged course of treatment; in a relatively easy way by a self-assessed symptom severity.

Effects of age and genetic variations in VKORC1, CYP2C9 and CYP3A4 on the phenprocoumon dose in pediatric patients.

AIM: To study the effects of clinical and genetic factors on the phenprocoumon dose requirement in pediatric patients and to develop a dosing algorithm. METHODS: Pediatric patients who used phenprocoumon were invited to participate in a retrospective follow-up study. Clinical information and genotypes of genetic variations in CYP2C9, VKORC1, CYP4F2, CYP2C18 and CYP3A4 were collected and tested with linear regression for association with phenprocoumon dose requirement. RESULTS: Of the 41 patients included in the analysis, age, VKORC1, CYP2C9*2/*3 and CYP3A4*1B were statistically significantly associated with dose requirement, and together explained 80.4% of the variability in phenprocoumon dose requirement. CONCLUSION: Our study reveals that age and genetic variations explain a significant part of the variability in phenprocoumon dose requirement in pediatric patients.

Association of the 6q23 region with the rate of joint destruction in rheumatoid arthritis.

BACKGROUND: Two novel genetic polymorphisms on chromosome 6q23 are associated with susceptibility to rheumatoid arthritis (RA). Both polymorphisms (rs6920220 and rs10499194) reside in a region close to the gene encoding tumour necrosis factor alpha-induced protein 3 (TNFAIP3). TNFAIP3 is a negative regulator of NF-kappaB and is involved in inhibiting TNF-receptor-mediated signalling effects. Interestingly, the initial associations were detected in patients with longstanding RA. However, no association was found for rs10499194 in a Swedish cohort with early arthritis. This might be caused by over-representation of patients with severe disease in cohorts with longstanding RA. OBJECTIVE: To analyse the effect of the 6q23 region on the rate of joint destruction. METHODS: Five single nucleotide polymorphisms in 6q23 were genotyped in 324 Dutch patients with early RA. Genotypes were correlated with progression of radiographic joint damage for a follow-up time of 5 years. RESULTS: Two polymorphisms (rs675520 and rs9376293) were associated with severity of radiographic joint damage in patients positive for anti-citrullinated protein/peptide antibodies (ACPA). Importantly, the effects were present after correction for confounding factors such as secular trends in treatment. CONCLUSIONS: These data associate the 6q23 region with the rate of joint destruction in ACPA+ RA.

Staff opinions regarding the Newborn Individualized Developmental Care and Assessment Program (NIDCAP).

This study explored the opinions of (para)medical and nursing staff in two Dutch Neonatal Intensive Care Units (NICU's). A questionnaire was used that measured: a) the perceived impact of NIDCAP on several NICU conditions, b) attitudes, subjective norm, perceived behavioral control, knowledge and abilities of using the NIDCAP method (based on the Theory of Planned Behavior) and c) training interest, requirements, information sources and the relevance of the NIDCAP method for different groups of NICU patients. Respondents were positive about NIDCAP and felt that using NIDCAP is fulfilling and leads to improvement of the infant's development, health and well-being. However, NIDCAP was also thought to be time-consuming and might worsen job conditions. The nursing staff, compared to the medical staff, had a more positive attitude (p=.004), higher perceived behavioral control (p=.004) and perceived a more positive impact of NIDCAP on NICU conditions (p=.008).