A simplified (modified) Duke Activity Status Index (M-DASI) to characterise functional capacity: a secondary analysis of the Measurement of Exercise Tolerance before Surgery (METS) study.

BACKGROUND: Accurate assessment of functional capacity, a predictor of postoperative morbidity and mortality, is essential to improving surgical planning and outcomes. We assessed if all 12 items of the Duke Activity Status Index (DASI) were equally important in reflecting exercise capacity. METHODS: In this secondary cross-sectional analysis of the international, multicentre Measurement of Exercise Tolerance before Surgery (METS) study, we assessed cardiopulmonary exercise testing and DASI data from 1455 participants. Multivariable regression analyses were used to revise the DASI model in predicting an anaerobic threshold (AT) >11 ml kg-1 min-1 and peak oxygen consumption (VO2 peak) >16 ml kg-1 min-1, cut-points that represent a reduced risk of postoperative complications. RESULTS: Five questions were identified to have dominance in predicting AT>11 ml kg-1 min-1 and VO2 peak>16 ml.kg-1min-1. These items were included in the M-DASI-5Q and retained utility in predicting AT>11 ml.kg-1.min-1 (area under the receiver-operating-characteristic [AUROC]-AT: M-DASI-5Q=0.67 vs original 12-question DASI=0.66) and VO2 peak (AUROC-VO2 peak: M-DASI-5Q 0.73 vs original 12-question DASI 0.71). Conversely, in a sensitivity analysis we removed one potentially sensitive question related to the ability to have sexual relations, and the ability of the remaining four questions (M-DASI-4Q) to predict an adequate functional threshold remained no worse than the original 12-question DASI model. Adding a dynamic component to the M-DASI-4Q by assessing the chronotropic response to exercise improved its ability to discriminate between those with VO2 peak>16 ml.kg-1.min-1 and VO2 peak<16 ml.kg-1.min-1. CONCLUSIONS: The M-DASI provides a simple screening tool for further preoperative evaluation, including with cardiopulmonary exercise testing, to guide perioperative management.

A cross-sectional survey of Australian anesthetists' and surgeons' perceptions of preoperative risk stratification and prehabilitation.

PURPOSE: Preoperative fitness training has been listed as a top ten research priority in anesthesia. We aimed to capture the current practice patterns and perspectives of anesthetists and colorectal surgeons in Australia and New Zealand regarding preoperative risk stratification and prehabilitation to provide a basis for implementation research. METHODS: During 2016, we separately surveyed fellows of the Australian and New Zealand College of Anaesthetists (ANZCA) and members of the Colorectal Society of Surgeons in Australia and New Zealand (CSSANZ). Our outcome measures investigated the responders' demographics, practice patterns, and perspectives. Practice patterns examined preoperative assessment and prehabilitation utilizing exercise, hematinic, and nutrition optimization. RESULTS: We received 155 responses from anesthetists and 71 responses from colorectal surgeons. We found that both specialty groups recognized that functional capacity was linked to postoperative outcome; however, fewer agreed that robust evidence exists for prehabilitation. Prehabilitation in routine practice remains low, with significant potential for expansion. The majority of anesthetists do not believe their patients are adequately risk stratified before surgery, and most of their colorectal colleagues are amenable to delaying surgery for at least an additional two weeks. Two-thirds of anesthetists did not use cardiopulmonary exercise testing as they lacked access. Hematinic and nutritional assessment and optimization is less frequently performed by anesthetists compared with their colorectal colleagues. CONCLUSIONS: An unrecognized potential window for prehabilitation exists in the two to four weeks following cancer diagnosis. Early referral, larger multi-centre studies focusing on long-term outcomes, and further implementation research are required.

RéSUMé: OBJECTIF: Le conditionnement physique préopératoire a été cité dans les dix priorités de recherche les plus importantes en anesthésie. Notre objectif était de déterminer quels étaient les habitudes actuelles de pratique ainsi que les perspectives des anesthésistes et des chirurgiens colorectaux en Australie et en Nouvelle-Zélande concernant la stratification préopératoire du risque et la préhabilitation afin de proposer un point de départ pour la recherche sur sa mise en œuvre. MéTHODE: Au cours de l'année 2016, nous avons soumis un questionnaire séparé aux membres du Collège australien et néozélandais des anesthésistes (ANZCA - Australian and New Zealand College of Anaesthetists) et aux membres de la Société colorectale des chirurgiens australiens et néozélandais (CSSANZ - Colorectal Society of Surgeons in Australia and New Zealand). Nos critères d'évaluation portaient sur les données démographiques, les habitudes de pratique et les perspectives des répondants. Les questions sur les habitudes de pratique touchaient à l'évaluation préopératoire et la préhabilitation fondée sur l'exercice physique et l'optimisation antianémique et nutritionnelle. RéSULTATS: Nous avons reçu 155 réponses d'anesthésistes et 71 réponses de chirurgiens colorectaux. Notre questionnaire a révélé que les deux spécialités reconnaissaient que la capacité fonctionnelle est liée au pronostic postopératoire; toutefois, moins de répondants étaient d'avis qu'il existe des données probantes fiables concernant la préhabilitation. Dans la pratique de routine, la préhabilitation demeure peu courante mais a le potentiel de prendre plus d'ampleur. La plupart des anesthésistes estiment que leurs patients ne sont pas stratifiés adéquatement en fonction de leur risque avant leur chirurgie, et la plupart de leurs collègues colorectaux sont ouverts à l'idée de retarder la chirurgie d'au moins deux semaines supplémentaires. Deux tiers des anesthésiologistes n'ont pas eu recours à un test d'effort cardiopulmonaire par manque d'accès à ce type d'examen. L'évaluation et l'optimisation antianémique et nutritionnelle sont moins fréquemment réalisées par les anesthésistes comparativement à leurs collègues colorectaux. CONCLUSION: Il existe une fenêtre potentielle mais non reconnue pour la mise en œuvre d'une préhabilitation au cours des deux à quatre semaines suivant l'annonce d'un diagnostic de cancer. Une prise en charge précoce par des spécialistes, des études multicentriques plus importantes s'intéressant aux pronostics à long terme et des travaux de recherche supplémentaires sur la mise en œuvre sont nécessaires.

Multimodal Prehabilitation Programs as a Bundle of Care in Gastrointestinal Cancer Surgery: A Systematic Review.

BACKGROUND: Prehabilitation reflects a proactive process of preoperative optimization undertaken between cancer diagnosis and definitive surgical treatment, with the intent of improving physiological capacity to withstand the major insult of surgery. Prehabilitation before GI cancer surgery is currently not widely adopted, and most research has focused on unimodal interventions such as exercise therapy, nutritional supplementation, and hematinic optimization. A review of the existing literature was undertaken to investigate the impact of multimodal prehabilitation programs as a "bundle of care." DATA SOURCE: A systematic literature search was performed utilizing Medline, PubMed, Embase, Cinahl, Cochrane, and Google Scholar databases. STUDY SELECTION: The quality of studies was assessed by using the Cochrane tool for assessing risk of bias (randomized trials) and the Newcastle-Ottawa Quality Assessment scale (cohort studies). INTERVENTION: Studies were chosen that involved pre-operative optimization of patients before GI cancer surgery. MAIN OUTCOMES: The primary outcome measured was the impact of prehabilitation programs on preoperative fitness and postoperative outcomes. RESULTS: Of the 544 studies identified, 20 were included in the qualitative analysis. Two trials investigated the impact of multimodal prehabilitation (exercise, nutritional supplementation, anxiety management). Trials exploring prehabilitation with unimodal interventions included impact of exercise therapy (7 trials), impact of preoperative iron replacement (5 trials), nutritional optimization (5 trials), and impact of preoperative smoking cessation (2 trials). Compliance within the identified studies was variable (range: 16%-100%). LIMITATIONS: There is a lack of adequately powered trials that utilize objective risk stratification and uniform end points. As such, a meta-analysis was not performed because of the heterogeneity in study design. CONCLUSION: Although small studies are supportive of multimodal interventions, there are insufficient data to make a conclusion about the integration of prehabilitation in GI cancer surgery as a bundle of care. Larger, prospective trials, utilizing uniform objective risk stratification and structured interventions, with predefined clinical and health economic end points, are required before definitive value can be assigned to prehabilitation programs.

Vision-based assessment of parkinsonism and levodopa-induced dyskinesia with pose estimation.

BACKGROUND: Despite the effectiveness of levodopa for treatment of Parkinson's disease (PD), prolonged usage leads to development of motor complications, most notably levodopa-induced dyskinesia (LID). Persons with PD and their physicians must regularly modify treatment regimens and timing for optimal relief of symptoms. While standardized clinical rating scales exist for assessing the severity of PD symptoms, they must be administered by a trained medical professional and are inherently subjective. Computer vision is an attractive, non-contact, potential solution for automated assessment of PD, made possible by recent advances in computational power and deep learning algorithms. The objective of this paper was to evaluate the feasibility of vision-based assessment of parkinsonism and LID using pose estimation. METHODS: Nine participants with PD and LID completed a levodopa infusion protocol, where symptoms were assessed at regular intervals using the Unified Dyskinesia Rating Scale (UDysRS) and Unified Parkinson's Disease Rating Scale (UPDRS). Movement trajectories of individual joints were extracted from videos of PD assessment using Convolutional Pose Machines, a pose estimation algorithm built with deep learning. Features of the movement trajectories (e.g. kinematic, frequency) were used to train random forests to detect and estimate the severity of parkinsonism and LID. Communication and drinking tasks were used to assess LID, while leg agility and toe tapping tasks were used to assess parkinsonism. Feature sets from tasks were also combined to predict total UDysRS and UPDRS Part III scores. RESULTS: For LID, the communication task yielded the best results (detection: AUC = 0.930, severity estimation: r = 0.661). For parkinsonism, leg agility had better results for severity estimation (r = 0.618), while toe tapping was better for detection (AUC = 0.773). UDysRS and UPDRS Part III scores were predicted with r = 0.741 and 0.530, respectively. CONCLUSION: The proposed system provides insight into the potential of computer vision and deep learning for clinical application in PD and demonstrates promising performance for the future translation of deep learning to PD clinical practices. Convenient and objective assessment of PD symptoms will facilitate more frequent touchpoints between patients and clinicians, leading to better tailoring of treatment and quality of care.

Comparison of Duke Activity Status Index with cardiopulmonary exercise testing in cancer patients.

PURPOSE: The Duke Activity Status Index (DASI), a patient-administered questionnaire, is used to quantify functional capacity in patients undergoing cancer surgery. METHODS: This retrospective cohort study assessed whether the DASI was accurate in predicting peak oxygen consumption (pVO2) that was objectively measured using cardiopulmonary exercise testing (CPET) in 43 consecutive patients scheduled for elective major cancer surgery at a tertiary cancer centre. The primary outcome measured the limits of agreement between DASI-predicted pVO2 and actual measured pVO2. RESULTS: The study population was elderly (median 63 years, interquartile range 18), 58% were male, with the majority having intraabdominal cancer surgery. Although the DASI scores were statistically related to the measured pVO2 (N = 43, adjusted R2 = 0.20, p = 0.002), both the bias (8 ml kg- 1 min- 1) and 95% limits of agreement (19.5 to - 3.4 ml kg- 1 min- 1) between the predicted and measured pVO2 were large. Using some of the individual components, recalibrating the intercept and regression coefficient of the total DASI score did not substantially improve its ability to predict the measured pVO2. CONCLUSION: In summary, both the limits of agreement and bias between the measured and DASI-predicted pVO2 were substantial. The DASI-predicted pVO2 based on patient's assessment of their functional status could not be considered a reliable surrogate of measured pVO2 during CPET for the population of patients pending major cancer surgery and cannot, therefore, be used as a triage tool for referral to CPET centres for objective risk assessment.

Investigation of the Kinematic Upper-Limb Movement Assessment (KUMA): A Pilot Study.

Purpose: Upper limb movement disorders are common after stroke and can severely impact activities of daily living. Available clinical measures of these disorders are subjective and may lack the sensitivity needed to track a patient's progress and to compare different therapies. Kinematic analyses can provide clinicians with more objective measures for evaluating the effects of rehabilitation. We present a novel method to assess the quality of upper limb movement: the Kinematic Upper-limb Movement Assessment (KUMA). This assessment uses motion capture to provide three kinematic measures of upper limb movement: active range of motion, speed, and compensatory trunk movement. The researchers sought to evaluate the ability of the KUMA to distinguish motion in the affected versus unaffected limb. Method: We used the KUMA with three participants with stroke to assess three single-joint movements in: wrist flexion and extension, elbow flexion and extension, and shoulder flexion/extension and abduction/adduction. Participants also completed the Modified Ashworth Scale and the Chedoke-McMaster Stroke Assessment, two clinical measures of functional ability. Results: The KUMA distinguished between affected and unaffected upper limb motion. Conclusions: The KUMA provides clinicians with supplementary objective information for motion characterization that is not available through clinical measures alone. The KUMA can complement existing clinical measures such as the MAS and CMSA and can be helpful for monitoring patient progress.

Objectif : les troubles des mouvements de membres supérieurs sont courants après un accident vasculaire cérébral et peuvent nuire fortement aux activités de la vie quotidienne. Les mesures cliniques disponibles pour ces troubles sont subjectives et ne possèdent peut-être pas la sensibilité nécessaire pour suivre le progrès d'un patient et comparer les diverses thérapies. Les analyses de cinématique peuvent fournir aux cliniciens des mesures plus objectives pour évaluer les effets de la réadaptation. Les auteurs présentent une nouvelle méthode pour évaluer la qualité des mouvements des membres supérieurs : l'évaluation cinématique des mouvements des membres supérieurs (KUMA, pour Kinematic Upper-limb Movement Assessment ). Cette évaluation fait appel à la capture des mouvements pour fournir trois mesures cinématiques des mouvements des membres supérieurs : l'amplitude de mouvements actifs, la vitesse et le mouvement compensatoire du tronc. Les chercheurs ont cherché à évaluer la capacité de la KUMA à distinguer le mouvement du membre touché par rapport au membre non touché. Méthodologie : les chercheurs ont utilisé la KUMA auprès de trois participants ayant subi un accident vasculaire cérébral pour évaluer trois mouvements monoarticulaires : flexion et extension du poignet, flexion et extension du coude, et flexion et extension, abduction et adduction de l'épaule. Les participants ont également utilisé l'échelle modifiée d'Ashworth (MAS) et l'évaluation Chedoke-McMaster de l'accident vasculaire cérébral (AVC), deux mesures cliniques de la capacité fonctionnelle. Résultats : la KUMA distinguait le mouvement du membre supérieur atteint de celui qui ne l'était pas. Conclusions : La KUMA fournit aux cliniciens de l'information objective supplémentaires pour caractériser les mouvements d'une manière qui n'est pas disponible par les seules mesures cliniques. La KUMA peut compléter les mesures cliniques en place comme l'échelle modifiée d'Ashworth et l'évaluation Chedoke-McMaster de l'AVC et peut être utile pour surveiller le progrès des patients.

Predicting Glycaemia in Type 1 Diabetes Patients: Experiments in Feature Engineering and Data Imputation.

Patients with type 1 diabetes manually regulate blood glucose concentration by adjusting insulin dosage in response to factors such as carbohydrate intake and exercise intensity. Automated near-term prediction of blood glucose concentration is essential to prevent hyper- and hypoglycaemic events in type 1 diabetes patients and to improve control of blood glucose levels by physicians and patients. The imperfect nature of patient monitoring introduces missing values into all variables that play important roles to predict blood glucose level, necessitating data imputation. In this paper, we investigated the importance of variables and explored various feature engineering methods to predict blood glucose level. Next, we extended our work by developing a new empirical imputation method and investigating the predictive accuracy achieved under different methods to impute missing data. Also, we examined the influence of past signal values on the prediction of blood glucose levels. We reported the relative performance of predictive models in different testing scenarios and different imputation methods. Finally, we found an optimal combination of data imputation methods and built an ensemble model for the reliable prediction of blood glucose levels on a 30-minute horizon.

Automated genome mining for natural products.

BACKGROUND: Discovery of new medicinal agents from natural sources has largely been an adventitious process based on screening of plant and microbial extracts combined with bioassay-guided identification and natural product structure elucidation. Increasingly rapid and more cost-effective genome sequencing technologies coupled with advanced computational power have converged to transform this trend toward a more rational and predictive pursuit. RESULTS: We have developed a rapid method of scanning genome sequences for multiple polyketide, nonribosomal peptide, and mixed combination natural products with output in a text format that can be readily converted to two and three dimensional structures using conventional software. Our open-source and web-based program can assemble various small molecules composed of twenty standard amino acids and twenty two other chain-elongation intermediates used in nonribosomal peptide systems, and four acyl-CoA extender units incorporated into polyketides by reading a hidden Markov model of DNA. This process evaluates and selects the substrate specificities along the assembly line of nonribosomal synthetases and modular polyketide synthases. CONCLUSION: Using this approach we have predicted the structures of natural products from a diverse range of bacteria based on a limited number of signature sequences. In accelerating direct DNA to metabolomic analysis, this method bridges the interface between chemists and biologists and enables rapid scanning for compounds with potential therapeutic value.

Automated assessment of levodopa-induced dyskinesia: Evaluating the responsiveness of video-based features.

INTRODUCTION: Technological solutions for quantifying Parkinson's disease (PD) symptoms may provide an objective means to track response to treatment, including side effects such as levodopa-induced dyskinesia. Vision-based systems are advantageous as they do not require physical contact with the body and have minimal instrumentation compared to wearables. We have developed a vision-based system to quantify a change in dyskinesia as reported by patients using 2D videos of clinical assessments during acute levodopa infusions. METHODS: Nine participants with PD completed a total of 16 levodopa infusions, where they were asked to report important changes in dyskinesia (i.e. onset and remission). Participants were simultaneously rated using the UDysRS Part III (from video recordings analyzed post-hoc). Body joint positions and movements were tracked using a state-of-the-art deep learning pose estimation algorithm applied to the videos. 416 features (e.g. kinematics, frequency distribution) were extracted to characterize movements. The sensitivity and specificity of each feature to patient-reported changes in dyskinesia severity was computed and compared with physician-rated results. RESULTS: Features achieved similar or superior performance to the UDysRS for detecting the onset and remission of dyskinesia. The best AUC for detecting onset of dyskinesia was 0.822 and for remission of dyskinesia was 0.958, compared to 0.826 and 0.802 for the UDysRS. CONCLUSIONS: Video-based features may provide an objective means of quantifying the severity of levodopa-induced dyskinesia, and have responsiveness as good or better than the clinically-rated UDysRS. The results demonstrate encouraging evidence for future integration of video-based technology into clinical research and eventually clinical practice.

Case report: Iatrogenic brachial artery dissection with complete anterograde occlusion during elective arterial line placement.

INTRODUCTION: Brachial arterial catheters provide a more accurate reflection of central aortic arterial pressure compared to their radial counterparts. Although brachial arterial line complications are uncommon, we report a case of a rare iatrogenic brachial artery dissection with complete anterograde occlusion from elective arterial line placement. PRESENTATION OF CASE: A 41-year-old female presented for a right upper and middle lobe resection of a large neuroendocrine lung cancer. A brachial arterial line was inserted for continuous blood pressure monitoring using clinical landmarks. Six hours postoperatively the left hand was noted to be pale, cool and pulseless with complete paraesthesia. Thrombus was initially suspected on computed tomography angiography. Upon return to theatre, extensive dissection of the posterior brachial arterial wall was identified. CONCLUSION: We review our diagnostic pathway and treatment of this rare complication. Recommendations to minimise the risks of complications from brachial arterial line insertion are also overviewed. We recommend the routine utilization of ultrasound-guided technique and regular post-insertion neurovascular monitoring for the prevention and early recognition of complications from brachial artery catheter insertion.

Automatic Detection of Compensation During Robotic Stroke Rehabilitation Therapy.

Robotic stroke rehabilitation therapy can greatly increase the efficiency of therapy delivery. However, when left unsupervised, users often compensate for limitations in affected muscles and joints by recruiting unaffected muscles and joints, leading to undesirable rehabilitation outcomes. This paper aims to develop a computer vision system that augments robotic stroke rehabilitation therapy by automatically detecting such compensatory motions. Nine stroke survivors and ten healthy adults participated in this study. All participants completed scripted motions using a table-top rehabilitation robot. The healthy participants also simulated three types of compensatory motions. The 3-D trajectories of upper body joint positions tracked over time were used for multiclass classification of postures. A support vector machine (SVM) classifier detected lean-forward compensation from healthy participants with excellent accuracy (AUC = 0.98, F1 = 0.82), followed by trunk-rotation compensation (AUC = 0.77, F1 = 0.57). Shoulder-elevation compensation was not well detected (AUC = 0.66, F1 = 0.07). A recurrent neural network (RNN) classifier, which encodes the temporal dependency of video frames, obtained similar results. In contrast, F1-scores in stroke survivors were low for all three compensations while using RNN: lean-forward compensation (AUC = 0.77, F1 = 0.17), trunk-rotation compensation (AUC = 0.81, F1 = 0.27), and shoulder-elevation compensation (AUC = 0.27, F1 = 0.07). The result was similar while using SVM. To improve detection accuracy for stroke survivors, future work should focus on predefining the range of motion, direct camera placement, delivering exercise intensity tantamount to that of real stroke therapies, adjusting seat height, and recording full therapy sessions.

Noncontact Vision-Based Cardiopulmonary Monitoring in Different Sleeping Positions.

Individuals with obstructive sleep apnea (OSA) can experience partial or complete collapse of the upper airway during sleep. This condition affects between 10-17% of adult men and 3-9% of adult women, requiring arousal to resume regular breathing. Frequent arousals disrupt proper sleeping patterns and cause daytime sleepiness. Untreated OSA has been linked to serious medical issues including cardiovascular disease and diabetes. Unfortunately, diagnosis rates are low (∼20%) and current sleep monitoring options are expensive, time consuming, and uncomfortable. Toward the development of a convenient, noncontact OSA monitoring system, this paper presents a simple, computer vision-based method to monitor cardiopulmonary signals (respiratory and heart rates) during sleep. System testing was performed with 17 healthy participants in five different simulated sleep positions. To monitor cardiopulmonary rates, distinctive points are automatically detected and tracked in infrared image sequences. Blind source separation is applied to extract candidate signals of interest. The optimal respiratory and heart rates are determined using periodicity measures based on spectral analysis. Estimates were validated by comparison to polysomnography recordings. The system achieved a mean percentage error of 3.4% and 5.0% for respiratory rate and heart rate, respectively. This study represents an important step in building an accessible, unobtrusive solution for sleep apnea diagnosis.

Automated vision-based analysis of levodopa-induced dyskinesia with deep learning.

Levodopa is the gold standard therapy for Parkinson's disease (PD), but its prolonged usage leads to additional motor complications, namely levodopa-induced dyskinesia (LID). To assess LID and adjust drug regimens for optimal relief, patients attend regular clinic visits. However, the intermittent nature of these visits can fail to capture important changes in a person's condition. With the recent emergence of deep learning achieving impressive results in a wide array of fields including computer vision, there is an opportunity for video analysis to be used for automated assessment of LID. Deep learning for pose estimation was studied as a viable means of extracting body movements from PD assessment videos. Movement features were computed from joint trajectories. Results show that features derived from vision-based analysis have moderate to good correlation with clinician ratings of dyskinesia severity. This study presents the first application of deep learning to video analysis in PD, and demonstrates promise for future development of a non-contact system for objective PD assessment.

A non-contact vision-based system for respiratory rate estimation.

A non-contact vision-based system is presented for continuous respiratory rate monitoring. The system identifies feature points in a video feed and tracks them over time. Two methods are presented for comparison - a method which uses principal component analysis (PCA) and a simple averaging approach. These methods condense the feature point trajectories into a compact set of representative signals. The signal which most closely resembles an expected respiratory trace is selected based on spectral analysis. System performance is assessed by comparing the estimated respiratory rate to the rate determined via inductance plethysmogram. The system was evaluated on 5 participants in 4 simulated sleep scenarios. Accuracies of within 1 breath/minute were achieved for more than 97% of the recorded time in all scenarios. The proposed system is accurate, cost-effective, and simple, making it a suitable candidate for at-home installation.

Role of tipranavir in treatment of patients with multidrug-resistant HIV.

The worldwide emergence of multidrug-resistant human immunodeficiency virus (HIV)-1 strains has the driven the development of new antiretroviral (ARV) agents. Over the past 5 years, HIV-entry and integrase inhibitor ARVs, as well as improved non-nucleoside reverse transcriptase inhibitors (NRTIs) and protease inhibitors (PIs), have become available for treatment. It is important to assess how these new ARVs might be most judiciously used, paying close attention to viral susceptibility patterns, pharmacodynamic parameters, and the likelihood that patients will adhere to their therapy. Herein we review published material in Medline, EMBASE, and ISI for each antiretroviral agent/classes currently approved and summarize the available data on their efficacy, safety, and pharmacologic parameters. We focus on the role of tipranavir, a recently approved nonpeptidic PI, for treating HIV-infected children, adolescents, and adults with a history of multidrug-resistant HIV.