Can linear regression modeling help clinicians in the interpretation of genotypic resistance data? An application to derive a lopinavir-score

Concordance of genotypic resistance interpretation algorithms in HIV-1 infected patients: An exploratory analysis in Greece

PURPOSE

Genotypic resistance-related mutations in HIV-1 disease are often difficult to interpret. Different algorithms have been developed to provide meaningful application into clinical context. We aimed to compare, for the first time in Greece, the results of genotypic resistance derived from three interpretation algorithms.

METHODS

The sequences of 120 HIV 1-infected patients were tested for genotypic resistance to 19 antiretroviral (ARV) drugs (n = 2280 sequences). The interpretation results of Rega, ANRS and ViroSeq algorithms were compared.

RESULTS

Complete concordance was found for 2/19 ARV drugs, namely lamivudine and emptricitabine. Concordance was high for nucleoside reverse transcriptase inhibitors (NRTIs) and low for protease inhibitors (PIs). In inter-algorithm pairs, agreement was high between Rega and ViroSeq (kappa = 0.701), especially by ARV class, namely NRTIs (k = 0.869) and NNRTIs (k = 0.562). The only exception was noted for rilpivirine, where agreement was higher between ANRS and Rega (k = 0.410) compared to other inter-algorithm pairs (k = 0.018-0.055). By contrast, for PIs all comparisons yielded concordance equivalent to chance (k = 0.000).

CONCLUSIONS

Our exploratory analysis provided evidence of significant inter-algorithm discordances, especially for PIs and NNRTIs highlighting the importance of matching the results of different algorithms to achieve optimized risk stratification. Ongoing research could assist clinical physicians in interpreting complex genotypic resistance patterns.

Development and validation of a prediction model for tocilizumab failure in hospitalized patients with SARS-CoV-2 infection

BACKGROUND

The aim of this secondary analysis of the TESEO cohort is to identify, early in the course of treatment with tocilizumab, factors associated with the risk of progressing to mechanical ventilation and death and develop a risk score to estimate the risk of this outcome according to patients' profile.

METHODS

Patients with COVID-19 severe pneumonia receiving standard of care + tocilizumab who were alive and free from mechanical ventilation at day 6 after treatment initiation were included in this retrospective, multicenter cohort study. Multivariable logistic regression models were built to identify predictors of mechanical ventilation or death by day-28 from treatment initiation and β-coefficients were used to develop a risk score. Secondary outcome was mortality. Patients with the same inclusion criteria as the derivation cohort from 3 independent hospitals were used as validation cohort.

RESULTS

266 patients treated with tocilizumab were included. By day 28 of hospital follow-up post treatment initiation, 40 (15%) underwent mechanical ventilation or died [26 (10%)]. At multivariable analysis, sex, day-4 PaO2/FiO2 ratio, platelets and CRP were independently associated with the risk of developing the study outcomes and were used to generate the proposed risk score. The accuracy of the score in AUC was 0.80 and 0.70 in internal validation and test for the composite endpoint and 0.92 and 0.69 for death, respectively.

CONCLUSIONS

Our score could assist clinicians in identifying, early after tocilizumab administration, patients who are likely to progress to mechanical ventilation or death, so that they could be selected for eventual rescue therapies.

Lasso regularization for left-censored Gaussian outcome and high-dimensional predictors

Background

Biological assays for the quantification of markers may suffer from a lack of sensitivity and thus from an analytical detection limit. This is the case of human immunodeficiency virus (HIV) viral load. Below this threshold the exact value is unknown and values are consequently left-censored. Statistical methods have been proposed to deal with left-censoring but few are adapted in the context of high-dimensional data.

Methods

We propose to reverse the Buckley-James least squares algorithm to handle left-censored data enhanced with a Lasso regularization to accommodate high-dimensional predictors. We present a Lasso-regularized Buckley-James least squares method with both non-parametric imputation using Kaplan-Meier and parametric imputation based on the Gaussian distribution, which is typically assumed for HIV viral load data after logarithmic transformation. Cross-validation for parameter-tuning is based on an appropriate loss function that takes into account the different contributions of censored and uncensored observations. We specify how these techniques can be easily implemented using available R packages. The Lasso-regularized Buckley-James least square method was compared to simple imputation strategies to predict the response to antiretroviral therapy measured by HIV viral load according to the HIV genotypic mutations. We used a dataset composed of several clinical trials and cohorts from the Forum for Collaborative HIV Research (HIV Med. 2008;7:27-40). The proposed methods were also assessed on simulated data mimicking the observed data.

Results

Approaches accounting for left-censoring outperformed simple imputation methods in a high-dimensional setting. The Gaussian Buckley-James method with cross-validation based on the appropriate loss function showed the lowest prediction error on simulated data and, using real data, the most valid results according to the current literature on HIV mutations.

Conclusions

The proposed approach deals with high-dimensional predictors and left-censored outcomes and has shown its interest for predicting HIV viral load according to HIV mutations.

Cost of HAART in Italy: multicentric evaluation and determinants from a large HIV outpatient cohort

Background

As HIV infection turned into a chronic treatable disease, now ranking as one of the most costly in medicine, long-term sustainability of highly active antiretroviral treatment (HAART) expenses became a major issue, especially in countries with universal access to care. Identification of determinants of higher HAART costs may therefore help in controlling costs of care, while keeping high levels of retention in care and viral suppression.

Methods

With this aim, we enrolled a large multicentric sample of consecutive unselected human immunodeficiency virus (HIV) patients followed at five sites of care in Italy, and evaluated annual individual HAART costs in relation to a number of sociodemographic, clinical, and laboratory variables.

Results

We enrolled 2,044 patients, including 1,902 on HAART. Mean HAART costs were €9,377±€3,501 (range 782–29,852) per year, with remarkable site-based differences, possibly related to the different composition of local assisted populations. Percentages of patients on viral suppression were homogeneously high across all study sites. The factors identified by cross-validation were line of HAART, diagnosis of acquired immune deficiency syndrome, current CD4 T-cell count, and detectable HIV viremia >50 copies/mL. In the final multivariable model, HAART costs were independently directly associated with more advanced HAART line (P<0.001) and inversely correlated with current CD4 T-cell count (P=0.024). Site of care held independent prediction of higher costs, with marked control of expenses at sites 2 (P=0.001) and 5 (P<0.001).

Conclusion

Higher costs of HAART were strongly associated with previous treatment failures, detectable HIV viremia, and lower CD4 T-cell count at the time of evaluation, with no correlation at all with sex, age, hepatitis C virus coinfection, and nadir CD4 T-cell counts. Newer drugs, which are typically those associated with high prices, at the time of the analysis were still prevalently prescribed to rescue and maintain viral suppression in patients with more complex treatment history. Further analyses of the contribution of the single drug/regimen to the estimated cost are warranted.

Scoring methods for building genotypic scores: an application to didanosine resistance in a large derivation set

Background

Several attempts have been made to determine HIV-1 resistance from genotype resistance testing. We compare scoring methods for building weighted genotyping scores and commonly used systems to determine whether the virus of a HIV-infected patient is resistant.

Methods and Principal Findings

Three statistical methods (linear discriminant analysis, support vector machine and logistic regression) are used to determine the weight of mutations involved in HIV resistance. We compared these weighted scores with known interpretation systems (ANRS, REGA and Stanford HIV-db) to classify patients as resistant or not. Our methodology is illustrated on the Forum for Collaborative HIV Research didanosine database (N = 1453). The database was divided into four samples according to the country of enrolment (France, USA/Canada, Italy and Spain/UK/Switzerland). The total sample and the four country-based samples allow external validation (one sample is used to estimate a score and the other samples are used to validate it). We used the observed precision to compare the performance of newly derived scores with other interpretation systems. Our results show that newly derived scores performed better than or similar to existing interpretation systems, even with external validation sets. No difference was found between the three methods investigated. Our analysis identified four new mutations associated with didanosine resistance: D123S, Q207K, H208Y and K223Q.

Conclusions

We explored the potential of three statistical methods to construct weighted scores for didanosine resistance. Our proposed scores performed at least as well as already existing interpretation systems and previously unrecognized didanosine-resistance associated mutations were identified. This approach could be used for building scores of genotypic resistance to other antiretroviral drugs.