Genetic crosses of phenotypically distinct strains of the human malaria parasite Plasmodium falciparum are a powerful tool for identifying genes controlling drug resistance and other key phenotypes. Previous studies relied on the isolation of recombinant parasites from splenectomized chimpanzees, a research avenue that is no longer available. Here, we demonstrate that human-liver chimeric mice support recovery of recombinant progeny for the identification of genetic determinants of parasite traits and adaptations.

There is little known about how academic medical centers (AMCs) in the US develop, implement, and maintain predictive modeling and machine learning (PM and ML) models. We conducted semi-structured interviews with leaders from AMCs to assess their use of PM and ML in clinical care, understand associated challenges, and determine recommended best practices. Each transcribed interview was iteratively coded and reconciled by a minimum of 2 investigators to identify key barriers to and facilitators of PM and ML adoption and implementation in clinical care. Interviews were conducted with 33 individuals from 19 AMCs nationally. AMCs varied greatly in the use of PM and ML within clinical care, from some just beginning to explore their utility to others with multiple models integrated into clinical care. Informants identified 5 key barriers to the adoption and implementation of PM and ML in clinical care: (1) culture and personnel, (2) clinical utility of the PM and ML tool, (3) financing, (4) technology, and (5) data. Recommendation to the informatics community to overcome these barriers included: (1) development of robust evaluation methodologies, (2) partnership with vendors, and (3) development and dissemination of best practices. For institutions developing clinical PM and ML applications, they are advised to: (1) develop appropriate governance, (2) strengthen data access, integrity, and provenance, and (3) adhere to the 5 rights of clinical decision support. This article highlights key challenges of implementing PM and ML in clinical care at AMCs and suggests best practices for development, implementation, and maintenance at these institutions.

Background: Legacy hip outcome measures may be burdensome to patients and sometimes yield floor or ceiling effects. Patient-Reported Outcomes Measurement Information System (PROMIS) computer adaptive tests (CATs) allow for low-burden data capture and limited ceiling and floor effects. Purpose/Hypothesis: The purpose of this study was to determine whether the PROMIS CAT domains demonstrate correlation against commonly used legacy patient-reported outcome measures in a population of patients presenting to a tertiary care hip preservation center. The authors hypothesized the following: (1) PROMIS CAT scores based on physical function (PF), pain interference (PIF), pain behavior, and pain intensity would show strong correlation with the following legacy scores: modified Harris Hip Score (mHHS), International Hip Outcome Tool–12 (iHOT-12), Hip Outcome Score (HOS) Sports and Activities of Daily Living subscales, and Veterans RAND–6D (VR-6D) utility measure. (2) The mental and physical health portions of the VR-6D legacy measure would show weak correlation with mental- and psychosocial-specific PROMIS elements—depression, anxiety, fatigue, sleep, and ability to participate in social roles and activities. (3) All PROMIS measures would exhibit fewer floor and ceiling effects than legacy scores. Study Design: Cohort study (diagnosis); Level of evidence, 3. Methods: Prospective data were collected on 125 patients in the hip preservation clinics. Enrollees completed legacy scores (visual analog scale for pain, mHHS, iHOT-12, HOS, and VR-6D) and PROMIS CAT questionnaires (PF, PIF, pain behavior, anxiety, depression, sleep, social roles and activities, pain intensity, fatigue). Spearman rank correlations were calculated, with rs values of 0 to 0.3 indicating negligible correlation; 0.3 to 0.5, weak correlation; 0.5 to 0.7, moderately strong correlation; and >0.7, strong correlation. Floor and ceiling effects were evaluated. Results: As anticipated, the PF-CAT yielded strong correlations with the iHOT-12, mHHS, HOS–Sports, HOS–Activities of Daily Living, and VR-6D, with rs values of 0.76, 0.71, 0.81, 0.87, and 0.71, respectively. The PIF-CAT was the only pain score to show moderately strong to strong correlation with all 14 patient-reported outcome measures. A strong correlation was observed between the VR-6D and the social roles and activities CAT (rs = 0.73). The depression CAT had a significant floor effect at 19%. No additional floor or ceiling effect was present for any other legacy or PROMIS measure. Conclusion: The PF-CAT shows strong correlation with legacy patient-reported outcome scores among patients presenting to a tertiary care hip preservation center. The PIF-CAT also correlates strongly with legacy and PROMIS measures evaluating physical and mental well-being. PROMIS measures are less burdensome and demonstrate no floor or ceiling effects, making them a potential alternative to legacy patient-reported outcome measures for the hip.

Level II, prospective observational study.

Background: Total knee arthroplasty (TKA) is a common treatment for end-stage knee osteoarthritis but is associated with increased complication rates compared with unicompartmental knee arthroplasty (UKA). UKA offers better functional outcomes but is associated with a higher risk of revision. The purpose of this study was to apply good-practice, stated-preference methods to quantify patient preferences for benefit-risk tradeoffs associated with arthroplasty treatments for end-stage knee osteoarthritis. Methods: A discrete-choice experiment was developed with the following attributes: chance of complications, functional ability, awareness of the knee implant, and chance of needing another operation within 10 years. Patients included those aged 40 to 80 years with knee osteoarthritis. A pivot design filtered respondents into 1 of 2 surveys on the basis of self-reported functional ability (good compared with fair or poor) as measured by the Oxford Knee Score. Treatment-preference data were collected, and relative attribute-importance weights were estimated. Results: Two hundred and fifty-eight completed survey instruments from 92 males and 164 females were analyzed, with 72 respondents in the good-function cohort and 186 in the fair/poor-function cohort. Patients placed the greatest value or relative importance on serious complications and rates of revision in both cohorts. Preference weights did not vary between cohorts for any attribute. In the good-function cohort, 42% of respondents chose TKA and 58% chose UKA. In the fair/poor-function cohort, 54% chose TKA and 46% chose UKA. Conclusions: Patient preferences for various treatment attributes varied among patients in a knee osteoarthritis population. Complication and revision rates were the most important factors to patients, suggesting that physicians should focus on these areas when discussing treatments. The proportion of patients who chose UKA suggests that the current trend of increased UKA utilization is aligned with patient preferences. Clinical Relevance: Systematic elicitation of patient preferences for knee arthroplasty procedures, which lays out evidence-based risks and benefits of different treatments, indicates a larger subset of the knee osteoarthritis population may prefer UKA than would be suggested by the current rates of utilization of the procedure. Arthroplasty treatment should align with patient preferences and eligibility criteria to better deliver patient-centered care.