Comparative Analysis of Initial Full Blood Count Parameters in Adults Infected With Plasmodium falciparum for Classification of Disease Severity and Previous Exposure Across Endemic (Gabon) and Nonendemic (Germany) Settings

Background: The clinical presentation of individuals infected with Plasmodium falciparum is exceptionally diverse, ranging from asymptomatic parasitemia to life-threatening disease. Frequent previous exposure to Plasmodium spp results in partial protection from severe disease; however, this protection wanes in individuals emigrating from holoendemic regions, and there are currently no reliable biomarkers that accurately indicate this semi-immunity. Methods: Data were analyzed from 1392 adults infected with P falciparum in Gabon and Germany. Full blood count parameters and ratios were evaluated individually and as a combined ensemble-based machine learning classifier to predict disease severity, ranging from asymptomatic infection to severe malaria. As a secondary objective, the influence of previous exposure to Plasmodium spp was assessed. Results: Comparing asymptomatic parasitemia with uncomplicated malaria in Gabonese and comparing uncomplicated with severe malaria in German patients revealed significantly lower platelet counts (218 vs 150 ×103/µL, P < .0001; 85 vs 40 ×103/µL, P < .0001, respectively) and higher neutrophil counts (2.32 vs 2.57 ×103/µL, P = .0037; 3.08 vs 4.49 ×103/µL, P < .0001) in those with greater infection severity. The machine learning classifier outperformed single parameters in differentiating infection severity in both comparisons (area under the receiver operating characteristic curve, 0.94 and 0.84). Lymphocyte and monocyte counts showed a pattern that follows the level of previous malaria exposure, with lower cell counts in naive vs previously exposed patients, regardless of infection severity. Conclusions: The value of simple full blood count parameters for classification of P falciparum infection severity and previous exposure is considerable. The accuracy can be increased by integrating individual parameters into a joint machine learning model

Long-term risk prediction after major lower limb amputation: 1-year results of the PERCEIVE study

Background: Decision-making when considering major lower limb amputation is complex and requires individualized outcome estimation. It is unknown how accurate healthcare professionals or relevant outcome prediction tools are at predicting outcomes at 1-year after major lower limb amputation. Methods: An international, multicentre prospective observational study evaluating healthcare professional accuracy in predicting outcomes 1 year after major lower limb amputation and evaluation of relevant outcome prediction tools identified in a systematic search of the literature was undertaken. Observed outcomes at 1 year were compared with: healthcare professionals' preoperative predictions of death (surgeons and anaesthetists), major lower limb amputation revision (surgeons) and ambulation (surgeons, specialist physiotherapists and vascular nurse practitioners); and probabilities calculated from relevant outcome prediction tools. Results: A total of 537 patients and 2244 healthcare professional predictions of outcomes were included. Surgeons and anaesthetists had acceptable discrimination (C-statistic = 0.715), calibration and overall performance (Brier score = 0.200) when predicting 1-year death, but performed worse when predicting major lower limb amputation revision and ambulation (C-statistics = 0.627 and 0.662 respectively). Healthcare professionals overestimated the death and major lower limb amputation revision risks. Consultants outperformed trainees, especially when predicting ambulation. Allied healthcare professionals marginally outperformed surgeons in predicting ambulation. Two outcome prediction tools (C-statistics = 0.755 and 0.717, Brier scores = 0.158 and 0.178) outperformed healthcare professionals' discrimination, calibration and overall performance in predicting death. Two outcome prediction tools for ambulation (C-statistics = 0.688 and 0.667) marginally outperformed healthcare professionals. Conclusion: There is uncertainty in predicting 1-year outcomes following major lower limb amputation. Different professional groups performed comparably in this study. Two outcome prediction tools for death and two for ambulation outperformed healthcare professionals and may support shared decision-making