Complete Case Analysis
After excluding all patients with any missing laboratory markers, the mortality rate was lower than that seen in the main analysis: 61/6736 = 0.91 (0.68 to 1.13) per 100 person-years. ALT, ALB, ALK, BIL, CHL, HGB, and URE were all significantly associated with mortality in univariable analyses. However, only 3 of these markers, ALK, ALB, and HGB, remained significantly associated with mortality in multivariable analyses. The AIC was considerably lower than that seen in the main analysis (871 compared with 3175, partly due to the lower number of patients included in this analysis), implying that this was the model of best fit. However, the C-statistic was lower than that seen in the main analysis (C = 0.76), and person-time represented in this model was only 27% of that in the main model, that is, the majority of patients were excluded from this model due to missing data.
Missing Measurements Imputed
In further analyses, baseline measurements were imputed for those with missing measurements. In univariable analyses, in addition to the variables found to be significantly associated with mortality in the main analysis, CRE was also significantly associated with mortality. ALT was not significantly associated with mortality in multivariable analyses, and hence was excluded from the final model. The other 6 laboratory markers (ALB, ALK, CHL, CRE, HGB, and URE) remained significantly associated with mortality in multivariable analyses. However, despite the higher number of biomarkers included in this model, the C-statistic was identical to the one in main analysis (C = 0.78), and the AIC was considerably higher in the models using imputed data than that in the main analysis (>236 units difference). Hence this model was not considered a better fit than that seen in the main analysis.
Evaluation of Score Based on the Grading Severity of Adult Adverse Experiences Tables
Finally, mortality rates were calculated in the different strata for each laboratory marker as utilized by the SHCS. An increasing trend between the rate of mortality and the laboratory marker severity grade was particularly evident for ALK, ALB, and HGB (data not shown). A clear association was also seen between the total score (calculated by summing up individual grades for each laboratory marker) and mortality: rates for a total score of 0, 1, 2, 3 and ≥4 were 0.27 (0.16 to 0.39), 0.69 (0.50 to 0.88), 0.88 (0.60 to 1.16), 0.93 (0.55 to 1.31) and 4.50 (3.67 to 5.33) per 100 person-years, respectively.
The total score was significantly associated with mortality in unadjusted analyses (Table 4). Patients with a total score of ≥4 were at a 16-fold increased risk of experiencing mortality compared with those with a total score of 0 [RR: 16.34 (95% CI: 10.44 to 25.58)]. After adjusting for potential confounding variables, the total score remained significantly associated with mortality. A trend in risk over categories was evident; RRs (95% CIs) for patients with scores of 1, 2, 3, and ≥4 were 2.40 (1.46 to 3.94), 2.73 (1.61 to 4.61), 2.88 (1.59 to 5.11), and 11.29 (7.12 to 19.91), respectively, compared with those with a total score of 0. The AIC for this model was 3512, and the C-statistic was 0.74.
We have identified a strong association between 4 biomarkers measured to monitor safety in people on ART (ALT, ALB, ALK, and HGB) and short-term mortality. These markers, along with demographic variables, HAART regimen, current CD4 count, and current viral load have been used to construct a score to predict mortality. Importantly, we have shown that it is not just extreme levels of these markers which are associated with mortality—a one unit increment in ALB is associated with a 4% lower risk of mortality, although a 1 unit increment in hemoglobin is associated with a 19% lower risk. We have also shown that as well as very high levels of ALT, very low levels of ALT are associated with mortality. One explanation for this is that ALT levels are known to decrease with older age.12 However, our final model was adjusted for age, and the association between low ALT and mortality remained significant.
In complete case analysis, only ALB, ALK, and HGB were retained in the final model. The C-statistic was lower than that seen in the main analysis (0.76 vs. 0.78), and estimates were weaker than the main analysis. This was somewhat surprising, since it may be assumed that patients with complete data are those who are more inclined to have abnormal measurements and hence are monitored more regularly. If this is the case, then the associations in the main analyses are likely to be marginally higher than that in real practice. However, it may also be true that those with complete measurements are no different than those without. This score was similar to the original analysis in terms of the biomarkers retained, suggesting that the assumption made previously regarding assigning measurements within the normal range for those with missing data was acceptable. However, the majority of patients were excluded from this analysis and results were therefore deemed to be potentially biased.
We also performed analyses in which missing values were imputed. Hence a full dataset was available for all patients included in the initial analyses. This resulted in a higher number of biomarkers contributing to the overall score—compared with the selected biomarkers described above, ALT was excluded, whereas CHL, CRE, and URE were included. The C-statistic of this model was identical to that of the main model, despite the higher number of biomarkers, suggesting that ability of the model to discriminate between those who did and did not die had not been improved by adding additional biomarkers.
In further analyses, we derived a scoring system for adverse events using methods similar to those used by the SHCS10 4 years ago. The scoring system derived by the SHCS was based on a fixed-covariate analysis with measurements taken at baseline only and hence the effect on mortality of new adverse events that arose over time could not be assessed. Our score derived using this approach confirmed results reported from the SHCS, in that patients with a higher total score were at a significantly increased risk of mortality. We identified a strong association between a composite biomarker score of safety measures and risk of mortality – this was again mainly driven by ALK, HGB, and ALB. Although all these have been shown to be associated with death, it is useful to provide a score to better enable assessment of the risk of mortality. We have shown that a score based on the raw values of the biomarkers discriminates better between those who did and did not die than a score similar to the SHCS score (C = 0.78 vs. 0.74). Hence it is important to recognize that a small change in some biomarkers, even within the normal range, should be considered as clinically relevant.
The association between abnormal values of these particular laboratory markers and morbidity and/or mortality in HIV-positive patients is well recognized. For example, high ALT levels suggest liver tissue damage, whereas low ALB has been shown to be associated with mortality.5–7 ALB and ALK have also been used to measure liver function and patients receiving drugs such as NVP, d4T, and ddI have been shown to be at an increased risk of liver injury.13–15 TDF has been associated with elevated ALK.16,17 Abnormalities in ALK may signify obstructed bile flow and liver complications. The association between ZDV and anemia has been well documented,18–20 and even when used as part of combination therapy, ZDV use has been shown to be a risk factor for anemia.21 DdI and d4T have also been associated with low hemoglobin levels, though not as frequently or as strongly as ZDV.22,23 Low HGB is, in turn, associated with an increased risk of mortality,5,24 though it has also been recognized that unwell individuals have lower levels of HGB. For this reason, a sensitivity analysis was performed in which the biomarker measurements were lagged by 6 months and results were in line with the main analysis.
Given the number of laboratory markers that may be associated with mortality, it is important that these markers are measured regularly and results of one marker are interpreted after taking into account results of other markers. Hence a score used to predict mortality, which combines the results of these markers may be particularly useful in clinical practice. This score will enable clinicians to identify patients who are at higher mortality risk and who may require more frequent monitoring. For these patients, an assessment of biomarker changes over subsequent visits may contribute to the overall clinical assessment of the patient. A similar technique has been used by Justice et al25 to identify a single index incorporating non-HIV markers such as HGB, eGFR, and fibrosis score; in a study population of 5980 individuals, this index added discrimination to a model which did not initially include these markers (C-statistic improved from 0.68 to 0.72). In our analyses, we have shown that the score derived using the raw biomarkers had a higher C-statistic than that derived by using methods similar to the SHCS, suggesting that the former model was better able to discriminate between those who did and did not die.
Most scores have traditionally utilized threshold values that often purport to be “clinically relevant”, with patients being designated at high or low risk if their biomarker value exceeds or falls below a threshold. These thresholds have traditionally been favored by clinicians because of their ease of use. However, the thresholds used are often based on those used in the HIV-negative population, and have rarely been validated in the HIV-positive population, where causes of mortality and morbidity will differ. Furthermore, the use of scores based on threshold values stems from an era where clinicians had limited access to technology at the bedside. Given the technological advances that have occurred over the past decade, this is no longer a key requirement. Most importantly, our results suggest that by using such a score, some individuals at high risk of mortality may not be identified promptly.
In routine care, assigning a score to those with missing measurements is problematic. Patients who have reported no adverse effects may be less likely to have a laboratory test performed, and although in most cases it is likely that their laboratory values are in the normal range, this may not always be the case. Restricting analyses to only those with all laboratory measurements available reduces the number of patients in the analysis considerably. This restriction may also result in bias because patients with all laboratory measurements recorded are likely to have different characteristics compared with those with missing measurements.
We took several approaches to deal with missing data and showed that despite which method was used, biomarkers such as ALB, ALK, and HGB were predictive of mortality.
Deriving a score to accurately predict mortality is difficult, given the many variables that could potentially contribute to the score and the diversity of the causes of death. The score must take into account both clinical and statistical issues; in a real-life setting where it is unlikely that a patient will have all laboratory measurements available, it may be impractical to have a score which relies on a full laboratory medical record. Use of measures at start of ART to predict mortality is also difficult, since a value measured at start of HAART may not be an accurate measure of the risk of mortality in years to come.
Our analyses do have the limitations inherent with observational cohort studies. Models may not have been adjusted for other confounders, such as alcohol consumption, which may impact both on the score and mortality. We were also unable to adjust for current hepatitis status which may have explained abnormal liver function tests. Further, our proposed score has not been validated. We recognize that it was possible to use internal validation methods, such as cross validation, to validate our score. However, by nature, the score will predict well on the dataset used to derive it and results of such validation may be overly optimistic. Hence, we feel that external validation is most appropriate, and we are seeking to identify an independent dataset for this purpose.
It is important to recognize the risk of mortality amongst patients with laboratory measurements in the normal range and hence we suggest using a scoring system which relies on the raw values of these laboratory markers. In the score derived from categorizing laboratory markers according to severity, ALB, ALK, and HGB were the 3 main markers influencing the score. We found the same markers, together with ALT, to be associated with mortality when focusing on the raw values of these markers. All 4 markers are measured routinely and, further, these variables have been associated with drug-related toxicities.
Using only 4 markers with a known association with mortality will limit the issues surrounding bias due to variable selection and missing data and using only the latest ALT, ALB, ALK, and HGB values is both practical and efficient, both clinically and statistically.
These data have been set up in months, and therefore, the equation above gives a monthly probability of mortality.
A white MSM individual of 38 years of age, who has a current CD4 count of 500, a VL of <50, currently receiving an nonnucleoside reverse transcriptase inhibitor–based regimen, and has the following laboratory measurements: ALT (60), ALB (30), ALK (63), and HGB (13) has the following probability of dying:
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Estimates From Model in Which Missing Measurements are Treated as “Normal” (Logarithmic of RRs Shown in Table 3)
Members of the UK Collaborative HIV Cohort (CHIC) Study
UK CHIC Steering Committee: Jonathan Ainsworth, Jane Anderson, Abdel Babiker, Loveleen Bansi, David Chadwick, Valerie Delpech, David Dunn, Martin Fisher, Brian Gazzard, Richard Gilson, Mark Gompels, Teresa Hill, Margaret Johnson, Clifford Leen, Mark Nelson, Chloe Orkin, Adrian Palfreeman, Andrew Phillips, Deenan Pillay, Frank Post, Caroline Sabin (PI), Memory Sachikonye, Achim Schwenk, and John Walsh.
Central Co-ordination: Royal Free NHS Trust and RFUCMS, London (Loveleen Bansi, TeresaHill, Susie Huntington, Andrew Phillips, Caroline Sabin); Medical Research Council Clinical Trials Unit (MRC CTU), London (David Dunn, Adam Glabay).
Participating Centres: Barts and The London NHS Trust, London (C. Orkin, N. Garrett, J. Lynch, J. Hand, C. de Souza); Brighton and Sussex University Hospitals NHS Trust (M. Fisher, N. Perry, S. Tilbury, D. Churchill); Chelsea and Westminster Hospital NHS Trust, London (B. Gazzard, M. Nelson, M. Waxman, D. Asboe, S. Mandalia); Health Protection Agency—Centre for Infections London (HPA) (V. Delpech); Homerton University Hospital NHS Trust, London (J. Anderson, S. Munshi); King's College Hospital NHS Foundation Trust, London (F. Post, H. Korat, C. Taylor, Z. Gleisner, F. Ibrahim, L. Campbell); Mortimer Market Centre, London (R. Gilson, N. Brima, I. Williams); North Middlesex University Hospital NHS Trust, London (A. Schwenk, J. Ainsworth, C. Wood, S. Miller); Royal Free NHS Trust and UCL Medical School, London (M. Johnson, M. Youle, F. Lampe, C. Smith, H. Grabowska, C. Chaloner, D. Puradiredja); St. Mary's Hospital, London (J. Walsh, J. Weber, F. Ramzan, N. Mackie, A. Winston); The Lothian University Hospitals NHS Trust, Edinburgh (C. Leen, A. Wilson); North Bristol NHS Trust (M. Gompels, S. Allan); University of Leicester NHS Trust (A. Palfreeman, A. Moore); South Tees Hospitals NHS Foundation Trust (D. Chadwick, K. Wakeman):
Keywords:© 2012 Lippincott Williams & Wilkins, Inc.
biomarkers; mortality; HIV; score