Whole blood lactate kinetics in patients undergoing quantitative resuscitation for septic shock

Introduction We sought to compare the association of whole blood lactate kinetics with survival in patients with septic shock undergoing early quantitative resuscitation. Methods Preplanned analysis of a multicenter emergency department (ED)-based randomized control trial of early sepsis resuscitation targeting three physiological variables: central venous pressure, mean arterial pressure, and either central venous oxygen saturation or lactate clearance. Inclusion criteria: suspected infection, two or more systemic inflammatory response syndrome criteria, and either SBP 4 mmol/l. All patients had a lactate measured initially and subsequently at two hours. Normalization of lactate was defined as a lactate decline to 2.0 mmol/l was seen in 187/272 (69%), and 68/187 (36%) patients normalized their lactate. Overall mortality was 19.7%. AUCs for initial lactate, relative lactate clearance, and absolute lactate clearance were 0.70, 0.69, and 0.58, respectively. Lactate normalization best predicted survival (OR = 6.1, 95% CI = 2.2 to 21), followed by lactate clearance of 50% (OR = 4.3, 95% CI = 1.8 to 10.3), initial lactate of <2 mmol/l (OR = 3.4, 95% CI = 1.5 to 7.8), and initial lactate <4 mmol/l (OR = 2.3, 95% CI = 1.3 to 4.3), with lactate clearance of 10% not reaching significance (OR = 2.3, 95% CI = 0.96 to 5.6). Conclusions In ED sepsis patients undergoing early quantitative resuscitation, normalization of serum lactate during resuscitation was more strongly associated with survival than any absolute value or absolute/ relative change in lactate. Further studies should address whether strategies targeting lactate normalization leads to improved outcomes

Markers for early detection of cancer: Statistical guidelines for nested case-control studies

BACKGROUND: Recently many long-term prospective studies have involved serial collection and storage of blood or tissue specimens. This has spurred nested case-control studies that involve testing some specimens for various markers that might predict cancer. Until now there has been little guidance in statistical design and analysis of these studies. METHODS: To develop statistical guidelines, we considered the purpose, the types of biases, and the opportunities for extracting additional information. RESULTS: The following guidelines: (1) For the clearest interpretation, statistics should be based on false and true positive rates – not odds ratios or relative risks (2) To avoid overdiagnosis bias, cases should be diagnosed as a result of symptoms rather than on screening. (3) To minimize selection bias, the spectrum of control conditions should be the same in study and target screening populations. (4) To extract additional information, criteria for a positive test should be based on combinations of individual markers and changes in marker levels over time. (5) To avoid overfitting, the criteria for a positive marker combination developed in a training sample should be evaluated in a random test sample from the same study and, if possible, a validation sample from another study. (6) To identify biomarkers with true and false positive rates similar to mammography, the training, test, and validation samples should each include at least 110 randomly selected subjects without cancer and 70 subjects with cancer. CONCLUSION: These guidelines ensure good practice in the design and analysis of nested case-control studies of early detection biomarkers