In malaria, individuals are often infected with different parasite strains. The complexity of infection (COI) is defined as the number of genetically distinct parasite strains in an individual. ...Changes in the mean COI in a population have been shown to be informative of changes in transmission intensity with a number of probabilistic likelihood and Bayesian models now developed to estimate the COI. However, rapid, direct measures based on heterozygosity or FwS do not properly represent the COI. In this work, we present two new methods that use easily calculated measures to directly estimate the COI from allele frequency data. Using a simulation framework, we show that our methods are computationally efficient and comparably accurate to current approaches in the literature. Through a sensitivity analysis, we characterize how the distribution of parasite densities, the assumed sequencing depth, and the number of sampled loci impact the bias and accuracy of our two methods. Using our developed methods, we further estimate the COI globally from Plasmodium falciparum sequencing data and compare the results against the literature. We show significant differences in the estimated COI globally between continents and a weak relationship between malaria prevalence and COI.
Background
Complex machine learning (ML) models have revolutionized predictions in clinical care. However, for laparoscopic colectomy (LC), prediction of morbidity by ML has not been adequately ...analyzed nor compared against traditional logistic regression (LR) models.
Methods
All LC patients, between 2017 and 2019, in the National Surgical Quality Improvement Program (NSQIP) were identified. A composite outcome of 17 variables defined any post-operative morbidity. Seven of the most common complications were additionally analyzed. Three ML models (Random Forests, XGBoost, and L1-L2-RFE) were compared with LR.
Results
Random Forests, XGBoost, and L1-L2-RFE predicted 30-day post-operative morbidity with average area under the curve (AUC): .709, .712, and .712, respectively. LR predicted morbidity with AUC = .712. Septic shock was predicted with AUC ≤ .9, by ML and LR.
Conclusion
There was negligible difference in the predictive ability of ML and LR in post-LC morbidity prediction. Possibly, the computational power of ML cannot be realized in limited datasets.
Despite the effectiveness of total knee arthroplasty (TKA), patients often have lingering pain and dysfunction. Recent studies have raised concerns that preoperative mental health may negatively ...affect outcomes after TKA. The primary aim of this study investigates the relationship between patient-reported mental health and postoperative physical function following TKA.
A retrospective study of 1392 primary TKA patients was performed. Mental health and physical function scores were measured using the Patient-Reported Outcomes Measurement Information System (PROMIS) Global Health, and PROMIS Physical Function 10a and Knee injury and Osteoarthritis Outcome Score Physical Function (KOOS-PS) short forms. These assessments were completed preoperatively and up to 1-year postoperatively. Patients were stratified based on preoperative mental health scores into five distinct categories ranging from “Poor” to “Excellent.” Locally estimated scatter plot smoothing curves (LOESS) were fit to the data examining physical function score trends over time.
Patients with higher mental health scores before surgery demonstrated better preoperative and postoperative physical function scores. However, all patients experienced similar gains in physical function following surgery. Despite this early improvement, patients with the worst mental health scores experienced a sharp decline in physical function approximately a year after surgery and did not appear to recover.
Poor mental health should not be a contraindication for performing TKA. For patients with the lowest mental health scores, physicians should account for the possibility that physical function scores may deteriorate a year after surgery. Tighter follow-up guidelines, more frequent physical therapy visits, or treatment for mental health issues may be considered to counter such deterioration.
Controversy remains over outcomes between total hip arthroplasty (THA) approaches. This study aimed to compare the time to achieve the minimal clinically important difference (MCID) for the Hip ...Disability and Osteoarthritis Outcome Score-Physical Function Short Form (HOOS-PS) and the Patient-Reported Outcomes Measurement Information System (PROMIS) Global-Physical for patients who underwent anterior and posterior surgical approaches in primary THA.
Patients from 2018 to 2021 with preoperative and postoperative HOOS-PS or PROMIS Global-Physical questionnaires were grouped by approach. Demographic and MCID achievement rates were compared, and survival curves with and without interval-censoring were used to assess the time to achieve the MCID by approach. Log-rank and weighted log-rank tests were used to compare groups, and Weibull regression analyses were performed to assess potential covariates.
A total of 2,725 patients (1,054 anterior and 1,671 posterior) were analyzed. There were no significant differences in median MCID achievement times for either the HOOS-PS (anterior: 5.9 months, 95% confidence interval (CI): 4.6 to 6.4; posterior: 4.4 months, 95% CI: 4.1 to 5.1, P = 0.65) or the PROMIS Global-Physical (anterior: 4.2 months, 95% CI: 3.5 to 5.3; posterior: 3.5 months, 95% CI: 3.4 to 3.8, P = 0.08) between approaches. Interval-censoring revealed earlier times of achieving the MCID for both the HOOS-PS (anterior: 1.509 to 1.511 months; posterior: 1.7 to 2.3 months, P = 0.87) and the PROMIS Global-Physical (anterior: 3.0 to 3.1 weeks; posterior: 2.7 to 3.3 weeks, P = 0.18) for both surgical approaches.
The time to achieve the MCID did not differ by surgical approach. Most patients will achieve clinically meaningful improvements in physical function much earlier than previously believed.
Differences in patient-reported outcome measures (PROMs) between primary TKA (pTKA) and revision TKA (rTKA) have not been well-studied. Therefore, we compared pTKA and rTKA patients by the rates of ...achieving the Minimal Clinically Important Difference for Improvement (MCID-I) and Worsening (MCID-W).
A total of 2,448 patients (2,239 pTKAs/209 rTKAs) were retrospectively studied. Patients who completed the Knee Injury and Osteoarthritis Outcome Score-Physical Function Short Form (KOOS-PS), Patient-Reported Outcomes Measurement Information System (PROMIS) Physical Function Short Form 10a (PF10a), PROMIS Global-Mental, or PROMIS Global-Physical questionnaires were identified by Current Procedural Terminology (CPT) codes. Patient-reported outcome measures and MCID-I/MCID-W rates were compared. Multivariate logistic regression models measured relationships between surgery type and postoperative outcomes.
Patients who underwent rTKA (all causes) had lower rates of improvement and higher rates of worsening compared to pTKA patients for KOOS-PS (MCID-I: 54 versus 68%, P < .001; MCID-W: 18 versus 8.6%, P < .001), PF10a (MCID-I: 44 versus 65%, P < .001; MCID-W: 22 versus 11%, P < .001), PROMIS Global-Mental (MCID-I: 34 versus 45%, P = .005), and PROMIS Global-Physical (MCID-I: 51 versus 60%, P = .014; MCID-W: 29 versus 14%, P < .001). Undergoing revision was predictive of worsening postoperatively for KOOS-PS, PF10a, and PROMIS Global-Physical compared to pTKA. Postoperative scores were significantly higher for all 4 PROMs following pTKA.
Patients reported significantly less improvement and higher rates of worsening following rTKA, particularly for PROMs that assessed physical function. Although pTKA patients did better overall, the improvement rates may be considered relatively low and should prompt discussions on improving outcomes following pTKA and rTKA.
Level III, retrospective comparative study.
Patient-reported outcome measures (PROMs) provide the patient’s perspective following total hip arthroplasty (THA), although differences between primary THA (pTHA) and revision THA (rTHA) remain ...unclear. Thus, we compared the Minimal Clinically Important Difference for Improvement (MCID-I) and Worsening (MCID-W) in pTHA and rTHA patients.
Data from 2,159 patients (1,995 pTHAs/164 rTHAs) who had completed Hip Disability and Osteoarthritis Outcome Score-Physical Function Short Form (HOOS-PS), Patient-Reported Outcomes Measurement Information System (PROMIS) Physical Function Short Form 10a (PF10a), PROMIS Global-Mental, or PROMIS Global-Physical questionnaires were analyzed. The PROMs and MCID-I/MCID-W rates were compared using statistical tests and multivariate logistic regressions.
Compared to the pTHA group, the rTHA group had lower rates of improvement and higher rates of worsening for almost all PROMs, including HOOS-PS (MCID-I: 54 versus 84%, P < .001; MCID-W: 24 versus 4.4%, P < .001), PF10a (MCID-I: 44 versus 73%, P < .001; MCID-W: 22 versus 5.9%, P < .001), PROMIS Global-Mental (MCID-W: 42 versus 28%, P < .001), and PROMIS Global-Physical (MCID-I: 41 versus 68%, P < .001; MCID-W: 26 versus 11%, P < .001). Odds ratios supported rates of worsening following revision for the HOOS-PS (Odds Ratio (OR): 8.25, 95% Confidence Interval (CI): 5.62 to 12.4, P < .001), PF10a (OR: 8.34, 95% CI: 5.63 to 12.6, P < .001), PROMIS Global-Mental (OR: 2.16, 95% CI: 1.41 to 3.34, P < .001), and PROMIS Global-Physical (OR: 3.69, 95% CI: 2.46 to 5.62, P < .001).
Patients reported higher rates of worsening and lower rates of improvement following rTHA than pTHA, with significantly less score improvement and lower postoperative scores for all PROMs after revision. Most patients reported improvements following pTHA, with few worsening postoperatively.
Level III, retrospective comparative study.
The effect of mental health on patient-reported outcome measures is not fully understood in total joint arthroplasty (TJA). Thus, we investigated the relationship between mental health diagnoses ...(MHDs) and the Minimal Clinically Important Difference for Improvement (MCID-I) and Worsening (MCID-W) in primary TJA and revision TJA (rTJA).
Retrospective data were collected using relevant Current Procedural Terminology and MHDs International Classification of Diseases, 10th Revision, codes with completed Hip Disability and Osteoarthritis Outcome Score-Physical Function Short Form, Knee Injury and Osteoarthritis Outcome Score-Physical Function Short Form, Patient-reported Outcomes Measurement Information System (PROMIS)-Physical Function Short Form 10a, PROMIS Global-Mental, or PROMIS Global-Physical questionnaires. Logistic regressions and statistical analyses were used to determine the effect of a MHD on MCID-I/MCID-W rates.
Data included 4,562 patients (4,190 primary TJAs/372 rTJAs). In primary total hip arthroplasty (pTHA), MHD-affected outcomes for Hip Disability and Osteoarthritis Outcome Score-Physical Function Short Form (MCID-I: 81% versus 86%, P = 0.007; MCID-W: 6.0% versus 3.2%, P = 0.008), Physical Function Short Form 10a (MCID-I: 68% versus 77%, P < 0.001), PROMIS Global-Mental (MCID-I: 38% versus 44%, P = 0.009), and PROMIS Global-Physical (MCID-I: 61% versus 73%, P < 0.001; MCID-W: 14% versus 7.9%, P < 0.001) versus pTHA patients without MHD. A MHD led to lower rates of MCID-I for PROMIS Global-Physical (MCID-I: 56% versus 63%, P = 0.003) in primary total knee arthroplasty patients. No effects from a MHD were observed in rTJA patients.
The presence of a MHD had a prominent negative influence on pTHA patients. Patients who underwent rTJA had lower MCID-I rates, higher MCID-W rates, and lower patient-reported outcome measure scores despite less influence from a MHD.
Level III, retrospective comparative study.
The novel coronavirus SARS-CoV-2 and its associated disease, COVID-19, have caused worldwide disruption, leading countries to take drastic measures to address the progression of the disease. As ...SARS-CoV-2 continues to spread, hospitals are struggling to allocate resources to patients who are most at risk. In this context, it has become important to develop models that can accurately predict the severity of infection of hospitalized patients to help guide triage, planning, and resource allocation.
The aim of this study was to develop accurate models to predict the mortality of hospitalized patients with COVID-19 using basic demographics and easily obtainable laboratory data.
We performed a retrospective study of 375 hospitalized patients with COVID-19 in Wuhan, China. The patients were randomly split into derivation and validation cohorts. Regularized logistic regression and support vector machine classifiers were trained on the derivation cohort, and accuracy metrics (F1 scores) were computed on the validation cohort. Two types of models were developed: the first type used laboratory findings from the entire length of the patient's hospital stay, and the second type used laboratory findings that were obtained no later than 12 hours after admission. The models were further validated on a multicenter external cohort of 542 patients.
Of the 375 patients with COVID-19, 174 (46.4%) died of the infection. The study cohort was composed of 224/375 men (59.7%) and 151/375 women (40.3%), with a mean age of 58.83 years (SD 16.46). The models developed using data from throughout the patients' length of stay demonstrated accuracies as high as 97%, whereas the models with admission laboratory variables possessed accuracies of up to 93%. The latter models predicted patient outcomes an average of 11.5 days in advance. Key variables such as lactate dehydrogenase, high-sensitivity C-reactive protein, and percentage of lymphocytes in the blood were indicated by the models. In line with previous studies, age was also found to be an important variable in predicting mortality. In particular, the mean age of patients who survived COVID-19 infection (50.23 years, SD 15.02) was significantly lower than the mean age of patients who died of the infection (68.75 years, SD 11.83; P<.001).
Machine learning models can be successfully employed to accurately predict outcomes of patients with COVID-19. Our models achieved high accuracies and could predict outcomes more than one week in advance; this promising result suggests that these models can be highly useful for resource allocation in hospitals.
Current state-of-the-art decision tree algorithms, such as Classification and Regression Trees (CART), build the decision tree using a recursive approach based on a greedy heuristic. We study the ...benefits of an optimal decision tree approach, which creates the entire decision tree at once using Mixed Integer Optimization (MIO). While such problems are known to be hard to solve for large instances, we leverage modern solver techniques that are able to obtain near-optimal solutions in a reasonable amount of time. The methodology is able to handle both single-feature splits, as in CART, and also hyperplane splits that use multiple features. We test optimal regression trees on a host of synthetic datasets and optimal classification tress on a novel application concerning the usage of CT imagining to diagnose head injuries in children. Our results demonstrate that optimal trees lead to a significantly greater accuracy than CART.
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