Acute renal failure and lithium intoxication

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Lithium and renal failur

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To establish whether lithium or anticonvulsant should be used for maintenance treatment for bipolar affective disorder BPAD if the risks of suicide and relapse were traded off against the risk of end-stage renal disease ESRD.

Decision analysis based on a systematic literature review with two main decisions: The final endpoint was 30 years of treatment with five outcomes to consider: At the start of treatment, the model identified lithium as the treatment of choice. Twenty years into treatment, lithium still remained treatment of choice.

At the current state of knowledge, lithium initiation and continuation even in the presence of long-term adverse renal effects should be recommended in most cases. Lithium remains a first-line treatment for maintenance treatment in patients with bipolar affective disorder BPAD. However, lithium and renal failur, its use seems in decline, with anticonvulsants ACs and some second-generation antipsychotics SGAs being increasingly used as alternatives despite a much more limited evidence base 1.

It is unclear why this trend away from lithium has occurred. Even today, 60 years after its debut 2the mechanism of the mood-stabilizing action of lithium remains largely unexplained. This rests uncomfortably with those demanding a treatment rationale going beyond empiricism 3 although the mechanism of action for ACs also remains unknown. The narrow therapeutic index of lithium and the potential for serious adverse effects is another concern.

The debate about the risk of kidney damage and the risks and benefits of lithium treatment began soon after licensing, and its use has remained controversial. Should we perhaps stop using lithium?

Should we avoid using it for a period longer than a few years? Lithium can affect renal function in two ways, lithium and renal failur. Tubular damage leading to polyuria and diabetes insipidus renalis is relatively common and occurs early during treatment, lithium and renal failur. Glomerular damage affecting the renal filtration and clearance ability is rarer and emerges late, often after decades of treatment. It remains unclear how many such patients who develop chronic lithium and renal failur disease CKD as a consequence of glomerular damage progress to end-stage renal disease ESRDthat is to such a severe impairment of the kidney function that dialysis is required.

Monitoring renal glomerular function regularly has become standard in lithium maintenance therapy. In addition, lithium and renal failur, lithium is not recommended for use in patients with severe renal impairment 7. This has resulted in an uncertainty about whether to recommend lithium at all and whether to continue lithium once CKD has occurred, lithium and renal failur. Equally, it remains unclear whether seeking expert advice on renal changes leads to better clinical outcomes because nephrologists are basically faced with the same dilemma as psychiatrists.

Would switching to another mood stabilizer at such a late stage increase the risk of suicide or relapse and if so would the risk be worth taking to preserve kidney function? After all, only few such patients seem to progress to ESRD and lithium discontinuation does not guarantee renal recovery. Moreover, other mood stabilizers are also associated with significant adverse effects such as lithium and renal failur gain and diabetes mellitus.

The scientific literature gives very little guidance in this question as trials and meta-analyses are not powered to quantify serious but uncommon adverse effects. A decision analysis lends itself as a method to address this question because treatment effects and risks can be considered at the same time.

To establish whether, based on lithium and renal failur current state of knowledge, lithium or anticonvulsants should be used for maintenance treatment for bipolar affective disorder if the risks of suicide and relapse were traded lithium and renal failur against the risk of end-stage renal disease. We conducted a decision analysis simulating the real-world decision process between physicians and patients in the consulting room comparing the relative risks and utilities of two mood stabilizers for the maintenance treatment for BPAD.

The analysis addressed two questions relevant to prescribers: This involved weighing up the ibuprofen and smoking for effective relapse and suicide prevention right from the beginning of treatment, with the risk of lithium-associated ESRD occurring many years later.

It was assumed that if signs of renal impairment emerged, lithium and renal failur, physicians would have to reconsider the use of lithium.

The decision of whether to take lithium was the starting point of the analysis. Once a diagnosis of bipolar affective or a related disorder is established, the question lithium and renal failur which mood stabilizer to use as maintenance therapy arises. Should the patient commence on lithium or rather on other mood stabilizers such as ACs? The clinical decision is a trade-off between the lithium and renal failur effects lithium and renal failur side-effects, in this case between relapse and suicide prevention as the desired effect and ESRD as the most undesirable side-effect.

To structure this problem, we used the decision software TreeAge Pro and plavix and prothrombin time. The decision analysis uses a tree, each branch representing two options. There were two main decisions to take. The first decision concerns the use of lithium versus ACs at treatment initiation weighing up the risk of suicide with the probability of developing CKD.

The second decision concerns the potential discontinuation of lithium in patients displaying signs of CKD typically after 20 years of lithium treatment. In our model, lithium and renal failur, this decision depends on the risk of suicide and the likelihood to develop ESRD continuing or discontinuing lithium, lithium and renal failur. The final endpoint of the analysis was 30 years of treatment with five potential outcomes to consider: We did not factor SGAs into our model because we could not identify sufficient data to populate the model.

Also it is much more likely at present that SGAs are added on as a second mood stabilizer to existing treatment, which effectively removes them from the equation in our model. To populate the tree, we derived the probabilities for the different chance nodes Table 1 from a systematic literature review. The sum of all probabilities of the options at a respective chance node had always to be one.

To assess the long-term suicide risk, we relied on the cohort study conducted by Angst et al. From the annual suicide risk estimates obtained, we modeled the cumulative suicide risk within the first 20 years on a hypothetical cohort. To obtain the subsequent cumulative suicide risk from year 21 to 30, we multiplied the suicide risk within 20 years with 0.

The best and worst case scenarios were the same for ACs and lithium, assuming that both drugs would perform equally at both extremes. The range covered estimates from a meta-analysis by Cipriani et al. Regarding suicide lithium and renal failur after switching from lithium to ACs after 20 years, we applied our baseline assumption for ACs.

We also considered a large register study, which showed that switching to ACs from lithium would not affect the suicide risk whereas switching from ACs to lithium would lead to a reduction of risk For our model, we inferred that the suicide risk would remain unchanged in the best case 913 and double in the worst case scenario 14 We obtained our assumptions about the risk of relapse from the BALANCE trial, which is a direct comparison of lithium and valproate monotherapy 16and the DUAG-6 trial, which is a direct comparison of lithium and lamotrigine monotherapy We validated the assumptions lithium and renal failur from these trials with the findings from the two most recent large register studies comparing lithium with valproate on the one hand 18 and lithium with lamotrigine on the other The nature of the decision tree demanded choosing only one value per chance node as the baseline assumption.

The relapse rates from the other three studies largely corresponded. Specifically, the results of the two register studies also indicated superiority of lithium to ACs 18lithium and renal failur, The DUAG-6 trial did not demonstrate any significant difference in effectiveness between lithium and lamotrigine However, the findings of the DUAG-6 trial lay comfortably in the range of the sensitivity analysis, which provided for the possibility of equality or even superiority of maintenance treatment with ACs.

For the sensitivity analysis, we based our best case scenario on the risk of episodes leading to admission to hospital only according to the BALANCE estimates 16 and the worst case scenario on the likelihood of ten years freedom from relapse This range not only covered the estimates from most recent register-based cohort study 1819 but also the meta-analysis by Beynon et al, lithium and renal failur.

We used the same assumptions regarding the risk of relapse for the first decision point at treatment initiation and for the second decision point after 20 years of treatment. This would yield a CKD prevalence of 4. Thus, although the study did not report in how many patients CKD was successfully halted after lithium discontinuation, lithium and renal failur, we assumed that of the 57 patients with CKD an equal proportion of those who had continued and discontinued lithium progressed to ESRD.

Others have reported similar results We valued outcomes using utilities as a measure of patient preference on a scale from zero to one with one denoting a perfect state of health. The utility of having committed suicide was zero. We then calculated the tree to obtain a numerical estimate for the best strategy to choose, lithium and renal failur. This involved folding rolling back the tree starting with the outcomes and working through the tree in a backwards fashion, that is from right to left, until arriving at the index decision.

The utility of each outcome with the assigned probability was multiplied and summed up. The obtained result would then represent the value of the next chance node down, and the procedure was repeated for the next chance node. In the final step, lithium and renal failur, we conducted a sensitivity analysis to evaluate the stability of our conclusions by varying the probabilities of the factors featured at chance nodes, lithium and renal failur.

For each node, we had formulated best and worst case scenarios and used a one-way sensitivity analysis to evaluate the expected utility across that range varying one factor at the time holding all other factors stable. We also tested the range of utilities keeping the chance nodes stable.

Beyond that threshold, the decision strategy would change to the alternative treatment. For the second decision point, we applied two-way sensitivity analysis to examine the impact of simultaneous changes in two variables. Here, we analyzed the impact of the simultaneous changes in the likelihoods to develop ESRD continuing or discontinuing lithium. We also modeled the trade-offs for the risk of developing ESRD continuing lithium against the risk of suicide or relapse of BPAD while treated with lithium.

In a further two-way sensitivity analysis, we modeled the trade-offs for the risk of developing ESRD discontinuing lithium against the risk of suicide or relapse of BPAD if switching to ACs. We then conducted a three-way sensitivity analysis trading off the risk of developing ESRD against the risks of suicide and unstable BPAD at the same time.

Varying the three variables over their estimated range at the same time over 10 levels yielded 11 data points for each of the three variables involved. Thus, scenarios were calculated. From these we confirmed the treatment recommendation for the scenario using the baseline assumptions and calculated which proportion of lithium and renal failur would recommend one strategy over the other.

Finally, we identified the most critical assumptions in our decision analysis indicating future research priorities by creating a tornado diagram named after its shape. This diagram demonstrates and ranks the degree to which uncertainty at individual variables affects lithium and renal failur utility, that is, lithium and renal failur, change of utility from the baseline value between the two strategies; the wider a bar, the larger the uncertainty.

Deviations to the right showed potential gains in utility. The baseline model relied on the assumptions derived from the literature outlined in Table 1 and recommends choosing lithium over ACs at both decision points, that is, at start of treatment and 20 years into treatment. Folding back the tree yielded an expected utility at the start of treatment of 0. In patients having developed CKD after 20 years of lithium treatment, the tree yielded an expected utility of 0.

At start of treatment, lithium and renal failur, concerns over mental health were the sole driver for the treatment decision. Varying the risk of suicide or the risk of progressing to CKD or ESRD over the assumed range did not change the treatment recommendation at decision point 1.

However, if the risk of unstable BPAD would be Tornado Diagram at lithium vs, lithium and renal failur. Twenty years into the treatment, however, the perspective changed. Now, some patients would have developed Lithium and renal failur.

 

Lithium and renal failur

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